Tootfinder

This https://arxiv.org/abs/2405.02937 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Unraveling the Dominance of Large Language Models Over Transformer Models for Bangla Natural Language Inference: A Comprehensive Study
Natural Language Inference (NLI) is a cornerstone of Natural Language Processing (NLP), providing insights into the entailment relationships between text pairings. It is a critical component of Natural Language Understanding (NLU), demonstrating the ability to extract information from spoken or written interactions. NLI is mainly concerned with determining the entailment relationship between two statements, known as the premise and hypothesis. When the premise logically implies the hypothesis, …

Understanding Language Modeling Paradigm Adaptations in Recommender Systems: Lessons Learned and Open Challenges
Lemei Zhang, Peng Liu, Yashar Deldjoo, Yong Zheng, Jon Atle Gulla
https://arxiv.org/abs/2404.03788

Understanding Language Modeling Paradigm Adaptations in Recommender Systems: Lessons Learned and Open Challenges
The emergence of Large Language Models (LLMs) has achieved tremendous success in the field of Natural Language Processing owing to diverse training paradigms that empower LLMs to effectively capture intricate linguistic patterns and semantic representations. In particular, the recent "pre-train, prompt and predict" training paradigm has attracted significant attention as an approach for learning generalizable models with limited labeled data. In line with this advancement, these training paradi…

Improving Long Text Understanding with Knowledge Distilled from Summarization Model
Yan Liu, Yazheng Yang, Xiaokang Chen
https://arxiv.org/abs/2405.04955 h…

Improving Long Text Understanding with Knowledge Distilled from Summarization Model
Long text understanding is important yet challenging for natural language processing. A long article or document usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as noise. With recent advances of abstractive summarization, we propose our \emph{Gist Detector} to leverage the gist detection ability of a summarization model and integrate the extracted gist into downstream models to enhance their long text understanding ability. Specifically, Gi…

This https://arxiv.org/abs/2310.12357 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Large Language Models for Code Analysis: Do LLMs Really Do Their Job?
Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks. Their capacity to comprehend and generate human-like code has spurred research into harnessing LLMs for code analysis purposes. However, the existing body of literature falls short in delivering a systematic evaluation and assessment of LLMs' effectiveness in code analysis, particularly in the context of obfuscated code. This paper seeks to…

Improving Long Text Understanding with Knowledge Distilled from Summarization Model
Yan Liu, Yazheng Yang, Xiaokang Chen
https://arxiv.org/abs/2405.04955 h…

Improving Long Text Understanding with Knowledge Distilled from Summarization Model
Long text understanding is important yet challenging for natural language processing. A long article or document usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as noise. With recent advances of abstractive summarization, we propose our \emph{Gist Detector} to leverage the gist detection ability of a summarization model and integrate the extracted gist into downstream models to enhance their long text understanding ability. Specifically, Gi…

This https://arxiv.org/abs/2404.13236 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models
Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors undermine trust in LLMs and pose significant hurdles to their adoption in real-world applic…

TTPXHunter: Actionable Threat Intelligence Extraction as TTPs form Finished Cyber Threat Reports
Nanda Rani, Bikash Saha, Vikas Maurya, Sandeep Kumar Shukla
https://arxiv.org/abs/2403.03267

TTPXHunter: Actionable Threat Intelligence Extraction as TTPs form Finished Cyber Threat Reports
Understanding the modus operandi of adversaries aids organizations in employing efficient defensive strategies and sharing intelligence in the community. This knowledge is often present in unstructured natural language text within threat analysis reports. A translation tool is needed to interpret the modus operandi explained in the sentences of the threat report and translate it into a structured format. This research introduces a methodology named TTPXHunter for the automated extraction of thr…

ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, Yonghong Tian
https://arxiv.org/abs/2402.16445 https://arxiv.org/pdf/2402.16445
arXiv:2402.16445v1 Announce Type: new
Abstract: Large Language Models (LLMs), including GPT-x and LLaMA2, have achieved remarkable performance in multiple Natural Language Processing (NLP) tasks. Under the premise that protein sequences constitute the protein language, Protein Large Language Models (ProLLMs) trained on protein corpora excel at de novo protein sequence generation. However, as of now, unlike LLMs in NLP, no ProLLM is capable of multiple tasks in the Protein Language Processing (PLP) field. This prompts us to delineate the inherent limitations in current ProLLMs: (i) the lack of natural language capabilities, (ii) insufficient instruction understanding, and (iii) high training resource demands. To address these challenges, we introduce a training framework to transform any general LLM into a ProLLM capable of handling multiple PLP tasks. Specifically, our framework utilizes low-rank adaptation and employs a two-stage training approach, and it is distinguished by its universality, low overhead, and scalability. Through training under this framework, we propose the ProLLaMA model, the first known ProLLM to handle multiple PLP tasks simultaneously. Experiments show that ProLLaMA achieves state-of-the-art results in the unconditional protein sequence generation task. In the controllable protein sequence generation task, ProLLaMA can design novel proteins with desired functionalities. In the protein property prediction task, ProLLaMA achieves nearly 100\% accuracy across many categories. The latter two tasks are beyond the reach of other ProLLMs. Code is available at \url{https://github.com/Lyu6PosHao/ProLLaMA}.

This https://arxiv.org/abs/2403.01528 has been replaced.
link: https://scholar.google.com/scholar?q=a

Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. This approach leverages the rich, multifaceted descriptions of biomolecules contained within textual data sources to enhance our fundamental understanding and enable downstream computational tasks such as biomolecule property prediction. The fusion of the nuanced narratives expressed through natural language …

Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
Yuan Wang, Xuyang Wu, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang
https://arxiv.org/abs/2404.03192

Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
The integration of Large Language Models (LLMs) in information retrieval has raised a critical reevaluation of fairness in the text-ranking models. LLMs, such as GPT models and Llama2, have shown effectiveness in natural language understanding tasks, and prior works (e.g., RankGPT) have also demonstrated that the LLMs exhibit better performance than the traditional ranking models in the ranking task. However, their fairness remains largely unexplored. This paper presents an empirical study eval…

This https://arxiv.org/abs/2309.12284 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open-source LLMs (e.g., LLaMA-2) are still far away from satisfactory for solving mathematical problem due to the complex reasoning procedures. To bridge this gap, we propose MetaMath, a fine-tuned language model that specializes in mathematical reasoning. Specifically, we start by bootstrapping mathematical questions by …

This https://arxiv.org/abs/2306.11943 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Towards Understanding What Code Language Models Learned
Pre-trained language models are effective in a variety of natural language tasks, but it has been argued their capabilities fall short of fully learning meaning or understanding language. To understand the extent to which language models can learn some form of meaning, we investigate their ability to capture semantics of code beyond superficial frequency and co-occurrence. In contrast to previous research on probing models for linguistic features, we study pre-trained models in a setting that a…

This https://arxiv.org/abs/2308.06013 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Large Language Models for Telecom: Forthcoming Impact on the Industry
Large Language Models (LLMs), AI-driven models that can achieve general-purpose language understanding and generation, have emerged as a transformative force, revolutionizing fields well beyond Natural Language Processing (NLP) and garnering unprecedented attention. As LLM technology continues to progress, the telecom industry is facing the prospect of its impact on its landscape. To elucidate these implications, we delve into the inner workings of LLMs, providing insights into their current ca…

Vision-Language Navigation with Embodied Intelligence: A Survey
Peng Gao, Peng Wang, Feng Gao, Fei Wang, Ruyue Yuan
https://arxiv.org/abs/2402.14304 https:…

Vision-Language Navigation with Embodied Intelligence: A Survey
As a long-term vision in the field of artificial intelligence, the core goal of embodied intelligence is to improve the perception, understanding, and interaction capabilities of agents and the environment. Vision-language navigation (VLN), as a critical research path to achieve embodied intelligence, focuses on exploring how agents use natural language to communicate effectively with humans, receive and understand instructions, and ultimately rely on visual information to achieve accurate navi…

Application of GPT Language Models for Innovation in Activities in University Teaching
Manuel de Buenaga, Francisco Javier Bueno
https://arxiv.org/abs/2403.14694

Application of GPT Language Models for Innovation in Activities in University Teaching
The GPT (Generative Pre-trained Transformer) language models are an artificial intelligence and natural language processing technology that enables automatic text generation. There is a growing interest in applying GPT language models to university teaching in various dimensions. From the perspective of innovation in student and teacher activities, they can provide support in understanding and generating content, problem-solving, as well as personalization and test correction, among others. Fro…

Insights into Natural Language Database Query Errors: From Attention Misalignment to User Handling Strategies
Zheng Ning, Yuan Tian, Zheng Zhang, Tianyi Zhang, Toby Li
https://arxiv.org/abs/2402.07304

Insights into Natural Language Database Query Errors: From Attention Misalignment to User Handling Strategies
Querying structured databases with natural language (NL2SQL) has remained a difficult problem for years. Recently, the advancement of machine learning (ML), natural language processing (NLP), and large language models (LLM) have led to significant improvements in performance, with the best model achieving ~85% percent accuracy on the benchmark Spider dataset. However, there is a lack of a systematic understanding of the types, causes, and effectiveness of error-handling mechanisms of errors for…

This https://arxiv.org/abs/2310.06555 has been replaced.
link: https://scholar.google.com/scholar?q=a

It's About Time: Temporal References in Emergent Communication
Emergent communication studies the development of language between autonomous agents, aiming to improve understanding of natural language evolution and increase communication efficiency. While temporal aspects of language have been considered in computational linguistics, there has been no research on temporal references in emergent communication. This paper addresses this gap, by exploring how agents communicate about temporal relationships. We analyse three potential influences for the emerge…

LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models
Mouhamed Amine Bouchiha, Quentin Telnoff, Souhail Bakkali, Ronan Champagnat, Mourad Rabah, Micka\"el Coustaty, Yacine Ghamri-Doudane
https://arxiv.org/abs/2404.13236

LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models
Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors undermine trust in LLMs and pose significant hurdles to their adoption in real-world applic…

Vision-Language Navigation with Embodied Intelligence: A Survey
Peng Gao, Peng Wang, Feng Gao, Fei Wang, Ruyue Yuan
https://arxiv.org/abs/2402.14304 https:…

Vision-Language Navigation with Embodied Intelligence: A Survey
As a long-term vision in the field of artificial intelligence, the core goal of embodied intelligence is to improve the perception, understanding, and interaction capabilities of agents and the environment. Vision-language navigation (VLN), as a critical research path to achieve embodied intelligence, focuses on exploring how agents use natural language to communicate effectively with humans, receive and understand instructions, and ultimately rely on visual information to achieve accurate navi…

Pegasus-v1 Technical Report
Raehyuk Jung, Hyojun Go, Jaehyuk Yi, Jiho Jang, Daniel Kim, Jay Suh, Aiden Lee, Cooper Han, Jae Lee, Jeff Kim, Jin-Young Kim, Junwan Kim, Kyle Park, Lucas Lee, Mars Ha, Minjoon Seo, Abraham Jo, Ed Park, Hassan Kianinejad, SJ Kim, Tony Moon, Wade Jeong, Andrei Popescu, Esther Kim, EK Yoon, Genie Heo, Henry Choi, Jenna Kang, Kevin Han, Noah Seo, Sunny Nguyen, Ryan Won, Yeonhoo Park, Anthony Giuliani, Dave Chung, Hans Yoon, James Le, Jenny Ahn, June Lee, Manind…

Pegasus-v1 Technical Report
This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed to address the unique challenges posed by video data, such as interpreting spatiotemporal information, to offer nuanced video content comprehension across various lengths. This technical report overviews Pegasus-1's architecture, training strategies, and its performance in benchmarks on video conversation, zero-shot vi…

This https://arxiv.org/abs/2403.04786 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text. However, with their rising prominence, the security and vulnerability aspects of these models have garnered significant attention. This paper presents a comprehensive survey of the various forms of attacks targeting LLMs, discussing the nature and mechanisms of these attacks, their potential impacts, and c…

From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora
Virginia Morini, Valentina Pansanella, Katherine Abramski, Erica Cau, Andrea Failla, Salvatore Citraro, Giulio Rossetti
https://arxiv.org/abs/2403.14298

From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora
Social media platforms are online fora where users engage in discussions, share content, and build connections. This review explores the dynamics of social interactions, user-generated contents, and biases within the context of social media analysis (analyzing works that use the tools offered by complex network analysis and natural language processing) through the lens of three key points of view: online debates, online support, and human-AI interactions. On the one hand, we delineate the pheno…

MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control
Enshen Zhou, Yiran Qin, Zhenfei Yin, Yuzhou Huang, Ruimao Zhang, Lu Sheng, Yu Qiao, Jing Shao
https://arxiv.org/abs/2403.12037

MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control
It is a long-lasting goal to design a generalist-embodied agent that can follow diverse instructions in human-like ways. However, existing approaches often fail to steadily follow instructions due to difficulties in understanding abstract and sequential natural language instructions. To this end, we introduce MineDreamer, an open-ended embodied agent built upon the challenging Minecraft simulator with an innovative paradigm that enhances instruction-following ability in low-level control signal…

Training Table Question Answering via SQL Query Decomposition
Rapha\"el Mouravieff, Benjamin Piwowarski, Sylvain Lamprier
https://arxiv.org/abs/2402.13288

Training Table Question Answering via SQL Query Decomposition
Table Question-Answering involves both understanding the natural language query and grounding it in the context of the input table to extract the relevant information. In this context, many methods have highlighted the benefits of intermediate pre-training from SQL queries. However, while most approaches aim at generating final answers from inputs directly, we claim that there is better to do with SQL queries during training. By learning to imitate a restricted portion of SQL-like algebraic ope…

Saving the legacy of Hero Ibash: Evaluating Four Language Models for Aminoacian
Yunze Xiao, Yiyang Pan
https://arxiv.org/abs/2402.18121 https://

Saving the legacy of Hero Ibash: Evaluating Four Language Models for Aminoacian
This study assesses four cutting-edge language models in the underexplored Aminoacian language. Through evaluation, it scrutinizes their adaptability, effectiveness, and limitations in text generation, semantic coherence, and contextual understanding. Uncovering insights into these models' performance in a low-resourced language, this research pioneers pathways to bridge linguistic gaps. By offering benchmarks and understanding challenges, it lays groundwork for future advancements in natural l…

RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
Yucheng Hu, Yuxing Lu
https://arxiv.org/abs/2404.19543 https://arxiv.org/pdf/2404.19543
arXiv:2404.19543v1 Announce Type: new
Abstract: Large Language Models (LLMs) have catalyzed significant advancements in Natural Language Processing (NLP), yet they encounter challenges such as hallucination and the need for domain-specific knowledge. To mitigate these, recent methodologies have integrated information retrieved from external resources with LLMs, substantially enhancing their performance across NLP tasks. This survey paper addresses the absence of a comprehensive overview on Retrieval-Augmented Language Models (RALMs), both Retrieval-Augmented Generation (RAG) and Retrieval-Augmented Understanding (RAU), providing an in-depth examination of their paradigm, evolution, taxonomy, and applications. The paper discusses the essential components of RALMs, including Retrievers, Language Models, and Augmentations, and how their interactions lead to diverse model structures and applications. RALMs demonstrate utility in a spectrum of tasks, from translation and dialogue systems to knowledge-intensive applications. The survey includes several evaluation methods of RALMs, emphasizing the importance of robustness, accuracy, and relevance in their assessment. It also acknowledges the limitations of RALMs, particularly in retrieval quality and computational efficiency, offering directions for future research. In conclusion, this survey aims to offer a structured insight into RALMs, their potential, and the avenues for their future development in NLP. The paper is supplemented with a Github Repository containing the surveyed works and resources for further study: https://github.com/2471023025/RALM_Survey.

Multimodal Human-Autonomous Agents Interaction Using Pre-Trained Language and Visual Foundation Models
Linus Nwankwo, Elmar Rueckert
https://arxiv.org/abs/2403.12273

Multimodal Human-Autonomous Agents Interaction Using Pre-Trained Language and Visual Foundation Models
In this paper, we extended the method proposed in [17] to enable humans to interact naturally with autonomous agents through vocal and textual conversations. Our extended method exploits the inherent capabilities of pre-trained large language models (LLMs), multimodal visual language models (VLMs), and speech recognition (SR) models to decode the high-level natural language conversations and semantic understanding of the robot's task environment, and abstract them to the robot's actionable comm…

Efficient and Scalable Fine-Tune of Language Models for Genome Understanding
Huixin Zhan, Ying Nian Wu, Zijun Zhang
https://arxiv.org/abs/2402.08075 https:…

Efficient and Scalable Fine-Tune of Language Models for Genome Understanding
Although DNA foundation models have advanced the understanding of genomes, they still face significant challenges in the limited scale and diversity of genomic data. This limitation starkly contrasts with the success of natural language foundation models, which thrive on substantially larger scales. Furthermore, genome understanding involves numerous downstream genome annotation tasks with inherent data heterogeneity, thereby necessitating more efficient and robust fine-tuning methods tailored …

Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Sara Abdali, Richard Anarfi, CJ Barberan, Jia He
https://arxiv.org/abs/2403.12503

Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutionizing how we approach language understanding and generations. Nevertheless, alongside their remarkable utility, LLMs introduce critical security and risk considerations. These challenges warrant careful examination to ensure responsible deployment and safeguard against potential vulnerabilities. This research paper tho…

Perplexed: Understanding When Large Language Models are Confused
Nathan Cooper, Torsten Scholak
https://arxiv.org/abs/2404.06634 https://

Perplexed: Understanding When Large Language Models are Confused
Large Language Models (LLMs) have become dominant in the Natural Language Processing (NLP) field causing a huge surge in progress in a short amount of time. However, their limitations are still a mystery and have primarily been explored through tailored datasets to analyze a specific human-level skill such as negation, name resolution, etc. In this paper, we introduce perplexed, a library for exploring where a particular language model is perplexed. To show the flexibility and types of insights…

This https://arxiv.org/abs/2402.18838 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

When does word order matter and when doesn't it?
Language models (LMs) may appear insensitive to word order changes in natural language understanding (NLU) tasks. In this paper, we propose that linguistic redundancy can explain this phenomenon, whereby word order and other linguistic cues such as case markers provide overlapping and thus redundant information. Our hypothesis is that models exhibit insensitivity to word order when the order provides redundant information, and the degree of insensitivity varies across tasks. We quantify how inf…

Measuring Geographic Diversity of Foundation Models with a Natural Language--based Geo-guessing Experiment on GPT-4
Zilong Liu, Krzysztof Janowicz, Kitty Currier, Meilin Shi
https://arxiv.org/abs/2404.07612

Measuring Geographic Diversity of Foundation Models with a Natural Language--based Geo-guessing Experiment on GPT-4
Generative AI based on foundation models provides a first glimpse into the world represented by machines trained on vast amounts of multimodal data ingested by these models during training. If we consider the resulting models as knowledge bases in their own right, this may open up new avenues for understanding places through the lens of machines. In this work, we adopt this thinking and select GPT-4, a state-of-the-art representative in the family of multimodal large language models, to study i…

This https://arxiv.org/abs/2402.18838 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

When does word order matter and when doesn't it?
Language models (LMs) may appear insensitive to word order changes in natural language understanding (NLU) tasks. In this paper, we propose that linguistic redundancy can explain this phenomenon, whereby word order and other linguistic cues such as case markers provide overlapping and thus redundant information. Our hypothesis is that models exhibit insensitivity to word order when the order provides redundant information, and the degree of insensitivity varies across tasks. We quantify how inf…

This https://arxiv.org/abs/2312.02003 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly
Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potentia…

Enabling Waypoint Generation for Collaborative Robots using LLMs and Mixed Reality
Cathy Mengying Fang, Krzysztof Zieli\'nski, Pattie Maes, Joe Paradiso, Bruce Blumberg, Mikkel Baun Kj{\ae}rgaard
https://arxiv.org/abs/2403.09308

Enabling Waypoint Generation for Collaborative Robots using LLMs and Mixed Reality
Programming a robotic is a complex task, as it demands the user to have a good command of specific programming languages and awareness of the robot's physical constraints. We propose a framework that simplifies robot deployment by allowing direct communication using natural language. It uses large language models (LLM) for prompt processing, workspace understanding, and waypoint generation. It also employs Augmented Reality (AR) to provide visual feedback of the planned outcome. We showcase the…

Reconfigurable Robot Identification from Motion Data
Yuhang Hu, Yunzhe Wang, Ruibo Liu, Zhou Shen, Hod Lipson
https://arxiv.org/abs/2403.10496 https://

Reconfigurable Robot Identification from Motion Data
Integrating Large Language Models (VLMs) and Vision-Language Models (VLMs) with robotic systems enables robots to process and understand complex natural language instructions and visual information. However, a fundamental challenge remains: for robots to fully capitalize on these advancements, they must have a deep understanding of their physical embodiment. The gap between AI models cognitive capabilities and the understanding of physical embodiment leads to the following question: Can a robot…

The First Place Solution of WSDM Cup 2024: Leveraging Large Language Models for Conversational Multi-Doc QA
Yiming Li, Zhao Zhang
https://arxiv.org/abs/2402.18385

The First Place Solution of WSDM Cup 2024: Leveraging Large Language Models for Conversational Multi-Doc QA
Conversational multi-doc question answering aims to answer specific questions based on the retrieved documents as well as the contextual conversations. In this paper, we introduce our winning approach for the "Conversational Multi-Doc QA" challenge in WSDM Cup 2024, which exploits the superior natural language understanding and generation capability of Large Language Models (LLMs). We first adapt LLMs to the task, then devise a hybrid training strategy to make the most of in-domain unlabeled da…

This https://arxiv.org/abs/2307.08309 has been replaced.
link: https://scholar.google.com/scholar?q=a

LogPrécis: Unleashing Language Models for Automated Malicious Log Analysis
The collection of security-related logs holds the key to understanding attack behaviors and diagnosing vulnerabilities. Still, their analysis remains a daunting challenge. Recently, Language Models (LMs) have demonstrated unmatched potential in understanding natural and programming languages. The question arises whether and how LMs could be also useful for security experts since their logs contain intrinsically confused and obfuscated information. In this paper, we systematically study how to b…

RITFIS: Robust input testing framework for LLMs-based intelligent software
Mingxuan Xiao, Yan Xiao, Hai Dong, Shunhui Ji, Pengcheng Zhang
https://arxiv.org/abs/2402.13518

RITFIS: Robust input testing framework for LLMs-based intelligent software
The dependence of Natural Language Processing (NLP) intelligent software on Large Language Models (LLMs) is increasingly prominent, underscoring the necessity for robustness testing. Current testing methods focus solely on the robustness of LLM-based software to prompts. Given the complexity and diversity of real-world inputs, studying the robustness of LLMbased software in handling comprehensive inputs (including prompts and examples) is crucial for a thorough understanding of its performance.…

This https://arxiv.org/abs/2209.01678 has been replaced.
link: https://scholar.google.com/scholar?q=a

FairSNA: Algorithmic Fairness in Social Network Analysis
In recent years, designing fairness-aware methods has received much attention in various domains, including machine learning, natural language processing, and information retrieval. However, understanding structural bias and inequalities in social networks and designing fairness-aware methods for various research problems in social network analysis (SNA) have not received much attention. In this work, we highlight how the structural bias of social networks impacts the fairness of different SNA …

This https://arxiv.org/abs/2402.14304 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Vision-Language Navigation with Embodied Intelligence: A Survey
As a long-term vision in the field of artificial intelligence, the core goal of embodied intelligence is to improve the perception, understanding, and interaction capabilities of agents and the environment. Vision-language navigation (VLN), as a critical research path to achieve embodied intelligence, focuses on exploring how agents use natural language to communicate effectively with humans, receive and understand instructions, and ultimately rely on visual information to achieve accurate navi…

Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement Learning
Xiuqin Zhong, Shengyuan Yan, Gongqi Lin, Hongguang Fu, Liang Xu, Siwen Jiang, Lei Huang, Wei Fang
https://arxiv.org/abs/2403.14690

Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement Learning
In the context of online education, designing an automatic solver for geometric problems has been considered a crucial step towards general math Artificial Intelligence (AI), empowered by natural language understanding and traditional logical inference. In most instances, problems are addressed by adding auxiliary components such as lines or points. However, adding auxiliary components automatically is challenging due to the complexity in selecting suitable auxiliary components especially when …

This https://arxiv.org/abs/2402.08015 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets
Large language models (LLMs) have received a lot of attention in natural language processing (NLP) research because of their exceptional performance in understanding and generating human languages. However, low-resource languages are left behind due to the unavailability of resources. In this work, we focus on enhancing the LLaMA-2-Amharic model by integrating task-specific and generative datasets to improve language model performance for Amharic. We compile an Amharic instruction fine-tuning d…

This https://arxiv.org/abs/2402.11194 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Assessing LLMs' Mathematical Reasoning in Financial Document Question Answering
Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with an amalgamation of structured tables and unstructured text is uncertain. This study explores LLMs' mathematical reasoning on four financial tabular question-answering datasets: TATQA, FinQA, ConvFinQA, and Multihiertt. Through extensive experiments with various models and prompting techniques, we assess how LLMs adapt to complex tables and mathematical tasks. We fo…

CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou, Shi Han, Zejian Yuan, Dongmei Zhang
https://arxiv.org/abs/2403.13583

CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Large Language Models (LLMs) have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CONLINE framework, which enhances code generation by incorporating planned online searches for information retrieval …

Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for Gradient
Mingxin Li, Richong Zhang, Zhijie Nie
https://arxiv.org/abs/2402.18281

Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for Gradient
Sentence Representation Learning (SRL) is a crucial task in Natural Language Processing (NLP), where contrastive Self-Supervised Learning (SSL) is currently a mainstream approach. However, the reasons behind its remarkable effectiveness remain unclear. Specifically, in other research fields, contrastive SSL shares similarities in both theory and practical performance with non-contrastive SSL (e.g., alignment & uniformity, Barlow Twins, and VICReg). However, in SRL, contrastive SSL outperforms n…

CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou, Shi Han, Zejian Yuan, Dongmei Zhang
https://arxiv.org/abs/2403.13583

CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Large Language Models (LLMs) have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we introduce the CONLINE framework, which enhances code generation by incorporating planned online searches for information retrieval …

This https://arxiv.org/abs/2401.01085 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control
Natural language processing (NLP) has received unprecedented attention. While advancements in NLP models have led to extensive research into their backdoor vulnerabilities, the potential for these advancements to introduce new backdoor threats remains unexplored. This paper proposes Imperio, which harnesses the language understanding capabilities of NLP models to enrich backdoor attacks. Imperio provides a new model control experience. Demonstrated through controlling image classifiers, it empo…

Beyond the Headlines: Understanding Sentiments and Morals Impacting Female Employment in Spain
Oscar Araque, Luca Barbaglia, Francesco Berlingieri, Marco Colagrossi, Sergio Consoli, Lorenzo Gatti, Caterina Mauri, Kyriaki Kalimeri
https://arxiv.org/abs/2402.07339

Beyond the Headlines: Understanding Sentiments and Morals Impacting Female Employment in Spain
After decades of improvements in the employment conditions of females in Spain, this process came to a sudden stop with the Great Spanish Recession of 2008. In this contribution, we analyse a large longitudinal corpus of national and regional news outlets employing advanced Natural Language Processing techniques to capture the valence of mentions of gender inequality expressed in the Spanish press. The automatic analysis of the news articles does indeed capture the known hardships faced by fema…

Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models
Martin Riddell, Ansong Ni, Arman Cohan
https://arxiv.org/abs/2403.04811

Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models
While large language models have achieved remarkable performance on various code generation benchmarks, there have been growing concerns regarding potential contamination of these benchmarks as they may be leaked into pretraining and finetuning data. While recent work has investigated contamination in natural language generation and understanding tasks, there has been less extensive research into how data contamination impacts the evaluation of code generation, which is critical for understandi…

Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
Arijit Ghosh Chowdhury, Md Mofijul Islam, Vaibhav Kumar, Faysal Hossain Shezan, Vaibhav Kumar, Vinija Jain, Aman Chadha
https://arxiv.org/abs/2403.04786

Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text. However, with their rising prominence, the security and vulnerability aspects of these models have garnered significant attention. This paper presents a comprehensive survey of the various forms of attacks targeting LLMs, discussing the nature and mechanisms of these attacks, their potential impacts, and c…

From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
Kung-Hsiang Huang, Hou Pong Chan, Yi R. Fung, Haoyi Qiu, Mingyang Zhou, Shafiq Joty, Shih-Fu Chang, Heng Ji
https://arxiv.org/abs/2403.12027

From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Automatic chart understanding has witnessed significant advancements with the rise of large foundation models in recent years. Foundation models, such as large language models (LLMs), have revolutionized various natural language processing (NLP) tasks and are increasingly being applied to chart understanding tasks. This survey paper provides a compre…

Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
Arijit Ghosh Chowdhury, Md Mofijul Islam, Vaibhav Kumar, Faysal Hossain Shezan, Vaibhav Kumar, Vinija Jain, Aman Chadha
https://arxiv.org/abs/2403.04786

Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text. However, with their rising prominence, the security and vulnerability aspects of these models have garnered significant attention. This paper presents a comprehensive survey of the various forms of attacks targeting LLMs, discussing the nature and mechanisms of these attacks, their potential impacts, and c…

This https://arxiv.org/abs/2402.05130 has been replaced.
link: https://scholar.google.com/scholar?q=a

LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System
Generative Artificial Intelligence (AI), because of its emergent abilities, has empowered various fields, one typical of which is large language models (LLMs). One of the typical application fields of Generative AI is large language models (LLMs), and the natural language understanding capability of LLM is dramatically improved when compared with conventional AI-based methods. The natural language understanding capability has always been a barrier to the intent recognition performance of the Kn…

This https://arxiv.org/abs/2402.05130 has been replaced.
link: https://scholar.google.com/scholar?q=a

LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System
Generative Artificial Intelligence (AI), because of its emergent abilities, has empowered various fields, one typical of which is large language models (LLMs). One of the typical application fields of Generative AI is large language models (LLMs), and the natural language understanding capability of LLM is dramatically improved when compared with conventional AI-based methods. The natural language understanding capability has always been a barrier to the intent recognition performance of the Kn…

This https://arxiv.org/abs/2403.03267 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

TTPXHunter: Actionable Threat Intelligence Extraction as TTPs from Finished Cyber Threat Reports
Understanding the modus operandi of adversaries aids organizations in employing efficient defensive strategies and sharing intelligence in the community. This knowledge is often present in unstructured natural language text within threat analysis reports. A translation tool is needed to interpret the modus operandi explained in the sentences of the threat report and translate it into a structured format. This research introduces a methodology named TTPXHunter for the automated extraction of thr…

This https://arxiv.org/abs/2311.13668 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MAIRA-1: A specialised large multimodal model for radiology report generation
We present a radiology-specific multimodal model for the task for generating radiological reports from chest X-rays (CXRs). Our work builds on the idea that large language model(s) can be equipped with multimodal capabilities through alignment with pre-trained vision encoders. On natural images, this has been shown to allow multimodal models to gain image understanding and description capabilities. Our proposed model (MAIRA-1) leverages a CXR-specific image encoder in conjunction with a fine-tu…

Large Language Models: A Survey
Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, Jianfeng Gao
https://arxiv.org/abs/2402.06196

Large Language Models: A Survey
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data, as predicted by scaling laws \cite{kaplan2020scaling,hoffmann2022training}. The research area of LLMs, while very recent, is evolving rapidly in many different…

This https://arxiv.org/abs/2401.09615 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Learning Shortcuts: On the Misleading Promise of NLU in Language Models
The advent of large language models (LLMs) has enabled significant performance gains in the field of natural language processing. However, recent studies have found that LLMs often resort to shortcuts when performing tasks, creating an illusion of enhanced performance while lacking generalizability in their decision rules. This phenomenon introduces challenges in accurately assessing natural language understanding in LLMs. Our paper provides a concise survey of relevant research in this area an…

NLP4RE Tools: Classification, Overview, and Management
Julian Frattini, Michael Unterkalmsteiner, Davide Fucci, Daniel Mendez
https://arxiv.org/abs/2403.06685

NLP4RE Tools: Classification, Overview, and Management
Tools constitute an essential contribution to natural language processing for requirements engineering (NLP4RE) research. They are executable instruments that make research usable and applicable in practice. In this chapter, we first introduce a systematic classification of NLP4RE tools to improve the understanding of their types and properties. Then, we extend an existing overview with a systematic summary of 126 NLP4RE tools published between April 2019 and June 2023 to ease reuse and evoluti…

This https://arxiv.org/abs/2403.03267 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

TTPXHunter: Actionable Threat Intelligence Extraction as TTPs form Finished Cyber Threat Reports
Understanding the modus operandi of adversaries aids organizations in employing efficient defensive strategies and sharing intelligence in the community. This knowledge is often present in unstructured natural language text within threat analysis reports. A translation tool is needed to interpret the modus operandi explained in the sentences of the threat report and translate it into a structured format. This research introduces a methodology named TTPXHunter for the automated extraction of thr…

This https://arxiv.org/abs/2402.12243 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark
Text-to-SQL, which involves translating natural language into Structured Query Language (SQL), is crucial for enabling broad access to structured databases without expert knowledge. However, designing models for such tasks is challenging due to numerous factors, including the presence of 'noise,' such as ambiguous questions and syntactical errors. This study provides an in-depth analysis of the distribution and types of noise in the widely used BIRD-Bench benchmark and the impact of noise on mo…

This https://arxiv.org/abs/2312.15918 has been replaced.
link: https://scholar.google.com/scholar?q=a

Supervised Knowledge Makes Large Language Models Better In-context Learners
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded their use in real-world language applications. However, the critical challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. While previous in-context learning research has focused on enhancing models to adhere to users' specific…

This https://arxiv.org/abs/2310.14174 has been replaced.
link: https://scholar.google.com/scholar?q=a

An In-Context Schema Understanding Method for Knowledge Base Question Answering
The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. Recently, Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. In doing so, a major challenge for LLMs is to overcome the immensity and heterogeneity of knowledge base schemas.Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific deta…

This https://arxiv.org/abs/2309.08968 has been replaced.
link: https://scholar.google.com/scholar?q=a

Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference
Large language models (LLMs) have revolutionized natural language processing (NLP) by excelling at understanding and generating human-like text. However, their widespread deployment can be prohibitively expensive. SortedNet is a recent training technique for enabling dynamic inference by leveraging the modularity in networks and sorting sub-models based on computation/accuracy in a nested manner. We extend SortedNet to generative NLP tasks, making large language models dynamic without any Pre-T…

This https://arxiv.org/abs/2309.08968 has been replaced.
link: https://scholar.google.com/scholar?q=a

Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference
Large language models (LLMs) have revolutionized natural language processing (NLP) by excelling at understanding and generating human-like text. However, their widespread deployment can be prohibitively expensive. SortedNet is a recent training technique for enabling dynamic inference by leveraging the modularity in networks and sorting sub-models based on computation/accuracy in a nested manner. We extend SortedNet to generative NLP tasks, making large language models dynamic without any Pre-T…

Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity
Sho Hoshino, Akihiko Kato, Soichiro Murakami, Peinan Zhang
https://arxiv.org/abs/2403.05257

Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity
Learning better sentence embeddings leads to improved performance for natural language understanding tasks including semantic textual similarity (STS) and natural language inference (NLI). As prior studies leverage large-scale labeled NLI datasets for fine-tuning masked language models to yield sentence embeddings, task performance for languages other than English is often left behind. In this study, we directly compared two data augmentation techniques as potential solutions for monolingual ST…

Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity
Sho Hoshino, Akihiko Kato, Soichiro Murakami, Peinan Zhang
https://arxiv.org/abs/2403.05257

Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity
Learning better sentence embeddings leads to improved performance for natural language understanding tasks including semantic textual similarity (STS) and natural language inference (NLI). As prior studies leverage large-scale labeled NLI datasets for fine-tuning masked language models to yield sentence embeddings, task performance for languages other than English is often left behind. In this study, we directly compared two data augmentation techniques as potential solutions for monolingual ST…

This https://arxiv.org/abs/2309.08345 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases
Language models (LMs) have already demonstrated remarkable abilities in understanding and generating both natural and formal language. Despite these advances, their integration with real-world environments such as large-scale knowledge bases (KBs) remains an underdeveloped area, affecting applications such as semantic parsing and indulging in "hallucinated" information. This paper is an experimental investigation aimed at uncovering the robustness challenges that LMs encounter when tasked with …

This https://arxiv.org/abs/2309.08345 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases
Language models (LMs) have already demonstrated remarkable abilities in understanding and generating both natural and formal language. Despite these advances, their integration with real-world environments such as large-scale knowledge bases (KBs) remains an underdeveloped area, affecting applications such as semantic parsing and indulging in "hallucinated" information. This paper is an experimental investigation aimed at uncovering the robustness challenges that LMs encounter when tasked with …

LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements
Victoria Basmov, Yoav Goldberg, Reut Tsarfaty
https://arxiv.org/abs/2404.06283

LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements
The task of reading comprehension (RC), often implemented as context-based question answering (QA), provides a primary means to assess language models' natural language understanding (NLU) capabilities. Yet, when applied to large language models (LLMs) with extensive built-in world knowledge, this method can be deceptive. If the context aligns with the LLMs' internal knowledge, it is hard to discern whether the models' answers stem from context comprehension or from LLMs' internal information. …

This https://arxiv.org/abs/2402.12730 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

UMBCLU at SemEval-2024 Task 1A and 1C: Semantic Textual Relatedness with and without machine translation
The aim of SemEval-2024 Task 1, "Semantic Textual Relatedness for African and Asian Languages" is to develop models for identifying semantic textual relatedness (STR) between two sentences using multiple languages (14 African and Asian languages) and settings (supervised, unsupervised, and cross-lingual). Large language models (LLMs) have shown impressive performance on several natural language understanding tasks such as multilingual machine translation (MMT), semantic similarity (STS), and en…

FaBERT: Pre-training BERT on Persian Blogs
Mostafa Masumi, Seyed Soroush Majd, Mehrnoush Shamsfard, Hamid Beigy
https://arxiv.org/abs/2402.06617 https://…

FaBERT: Pre-training BERT on Persian Blogs
We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs corpus, encompassing both informal and formal Persian texts. FaBERT is designed to excel in traditional Natural Language Understanding (NLU) tasks, addressing the intricacies of diverse sentence structures and linguistic styles prevalent in the Persian language. In our comprehensive evaluation of FaBERT on 12 datasets in various downstream tasks, encompassing Sentiment Analysis (SA), Named Entity Recognition (NER), Natura…

ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
Yanming Liu, Xinyue Peng, Tianyu Du, Jianwei Yin, Weihao Liu, Xuhong Zhang
https://arxiv.org/abs/2403.06932

ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
Large language models (LLMs) have achieved commendable accomplishments in various natural language processing tasks. However, LLMs still encounter significant challenges when dealing with complex scenarios involving multiple entities. These challenges arise from the presence of implicit relationships that demand multi-step reasoning. In this paper, we propose a novel approach ERA-CoT, which aids LLMs in understanding context by capturing relationships between entities and supports the reasoning…

Finding fake reviews in e-commerce platforms by using hybrid algorithms
Mathivanan Periasamy, Rohith Mahadevan, Bagiya Lakshmi S, Raja CSP Raman, Hasan Kumar S, Jasper Jessiman
https://arxiv.org/abs/2404.06339

Finding fake reviews in e-commerce platforms by using hybrid algorithms
Sentiment analysis, a vital component in natural language processing, plays a crucial role in understanding the underlying emotions and opinions expressed in textual data. In this paper, we propose an innovative ensemble approach for sentiment analysis for finding fake reviews that amalgamate the predictive capabilities of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree classifiers. Our ensemble architecture strategically combines these diverse models to capitalize on…

This https://arxiv.org/abs/2311.13668 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MAIRA-1: A specialised large multimodal model for radiology report generation
We present a radiology-specific multimodal model for the task for generating radiological reports from chest X-rays (CXRs). Our work builds on the idea that large language model(s) can be equipped with multimodal capabilities through alignment with pre-trained vision encoders. On natural images, this has been shown to allow multimodal models to gain image understanding and description capabilities. Our proposed model (MAIRA-1) leverages a CXR-specific image encoder in conjunction with a fine-tu…

This https://arxiv.org/abs/2311.13668 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MAIRA-1: A specialised large multimodal model for radiology report generation
We present a radiology-specific multimodal model for the task for generating radiological reports from chest X-rays (CXRs). Our work builds on the idea that large language model(s) can be equipped with multimodal capabilities through alignment with pre-trained vision encoders. On natural images, this has been shown to allow multimodal models to gain image understanding and description capabilities. Our proposed model (MAIRA-1) leverages a CXR-specific image encoder in conjunction with a fine-tu…