Tootfinder

Improving Retrieval for RAG based Question Answering Models on Financial Documents
Spurthi Setty, Katherine Jijo, Eden Chung, Natan Vidra
https://arxiv.org/abs/2404.07221

Improving Retrieval for RAG based Question Answering Models on Financial Documents
The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in recent years, users may still encounter inaccuracies or irrelevant answers; these issues often stem from suboptimal text chunk retrieval by …

I Did Not Notice: A Comparison of Immersive Analytics with Augmented and Virtual Reality
Xiaoyan Zhou, Anil Ufuk Batmaz, Adam S. Williams, Dylan Schreiber, Francisco Ortega
https://arxiv.org/abs/2404.03814

I Did Not Notice: A Comparison of Immersive Analytics with Augmented and Virtual Reality
Immersive environments enable users to engage in embodied interaction, enhancing the sensemaking processes involved in completing tasks such as immersive analytics. Previous comparative studies on immersive analytics using augmented and virtual realities have revealed that users employ different strategies for data interpretation and text-based analytics depending on the environment. Our study seeks to investigate how augmented and virtual reality influences sensemaking processes in quantitativ…

This https://arxiv.org/abs/2405.00465 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine
Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains; however, these models encounter issues such as generating inaccurate information or hallucinations. Retrieval-augmented generation provided a solution for these models to update knowledge and enhance their performance. In contrast to previous retrieval-augmented LMs, which utilize specialized cross-attention mechanisms to help LLM encode retrieved text, Biome…

This https://arxiv.org/abs/2205.04890 has been replaced.
link: https://scholar.google.com/scholar?q=a

Formal category theory in augmented virtual double categories
In this article we develop formal category theory within augmented virtual double categories. Notably we formalise the classical notions of Kan extension, Yoneda embedding $\text y_A\colon A \to \hat A$, exact square, total category and 'small' cocompletion; the latter in an appropriate sense. Throughout we compare our formalisations to their corresponding $2$-categorical counterparts. Our approach has several advantages. For instance, the structure of augmented virtual double categories natura…

ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
Guande Wu, Jing Qian, Sonia Castelo, Shaoyu Chen, Joao Rulff, Claudio Silva
https://arxiv.org/abs/2402.18797

ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
Text presented in augmented reality provides in-situ, real-time information for users. However, this content can be challenging to apprehend quickly when engaging in cognitively demanding AR tasks, especially when it is presented on a head-mounted display. We propose ARTiST, an automatic text simplification system that uses a few-shot prompt and GPT-3 models to specifically optimize the text length and semantic content for augmented reality. Developed out of a formative study that included seve…

This https://arxiv.org/abs/2205.04890 has been replaced.
link: https://scholar.google.com/scholar?q=a

Formal category theory in augmented virtual double categories
In this article we develop formal category theory within augmented virtual double categories. Notably we formalise the classical notions of Kan extension, Yoneda embedding $\text y_A\colon A \to \hat A$, exact square, total category and 'small' cocompletion; the latter in an appropriate sense. Throughout we compare our formalisations to their corresponding $2$-categorical counterparts. Our approach has several advantages. For instance, the structure of augmented virtual double categories natura…

This https://arxiv.org/abs/2310.01329 has been replaced.
link: https://scholar.google.com/scholar?q=a

BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models
Retrieval augmentation addresses many critical problems in large language models such as hallucination, staleness, and privacy leaks. However, running retrieval-augmented language models (LMs) is slow and difficult to scale due to processing large amounts of retrieved text. We introduce binary token representations (BTR), which use 1-bit vectors to precompute every token in passages, significantly reducing computation during inference. Despite the potential loss of accuracy, our new calibration…

Label-Free Topic-Focused Summarization Using Query Augmentation
Wenchuan Mu, Kwan Hui Lim
https://arxiv.org/abs/2404.16411 https://ar…

Label-Free Topic-Focused Summarization Using Query Augmentation
In today's data and information-rich world, summarization techniques are essential in harnessing vast text to extract key information and enhance decision-making and efficiency. In particular, topic-focused summarization is important due to its ability to tailor content to specific aspects of an extended text. However, this usually requires extensive labelled datasets and considerable computational power. This study introduces a novel method, Augmented-Query Summarization (AQS), for topic-focus…

Join Zain Hasan at this year's #bbuzz as he discusses advanced retrieval augmented generation (RAG) techniques such as hybrid search, re-ranking, query generation, and semantic text chunking.
https://2024.berli…

This https://arxiv.org/abs/2404.17723 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering
In customer service technical support, swiftly and accurately retrieving relevant past issues is critical for efficiently resolving customer inquiries. The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue structure and inter-issue relations, which limits performance. We introduce a novel customer service question-answering method that amalgam…

This https://arxiv.org/abs/2405.00465 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine
Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains; however, these models encounter issues such as generating inaccurate information or hallucinations. Retrieval-augmented generation provided a solution for these models to update knowledge and enhance their performance. In contrast to previous retrieval-augmented LMs, which utilize specialized cross-attention mechanisms to help LLM encode retrieved text, Biome…

Tool-Augmented LLMs as a Universal Interface for IDEs
Yaroslav Zharov, Yury Khudyakov, Evgeniia Fedotova, Evgeny Grigorenko, Egor Bogomolov
https://arxiv.org/abs/2402.11635

Tool-Augmented LLMs as a Universal Interface for IDEs
Modern-day Integrated Development Environments (IDEs) have come a long way from the early text editing utilities to the complex programs encompassing thousands of functions to help developers. However, with the increasing number of efficiency-enhancing tools incorporated, IDEs gradually became sophisticated software with a steep learning curve. The rise of the Large Language Models (LLMs) capable of both natural language dialogue and code generation leads to a discourse on the obsolescence of t…

This https://arxiv.org/abs/2404.13892 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Retrieval-Augmented Audio Deepfake Detection
With recent advances in speech synthesis including text-to-speech (TTS) and voice conversion (VC) systems enabling the generation of ultra-realistic audio deepfakes, there is growing concern about their potential misuse. However, most deepfake (DF) detection methods rely solely on the fuzzy knowledge learned by a single model, resulting in performance bottlenecks and transparency issues. Inspired by retrieval-augmented generation (RAG), we propose a retrieval-augmented detection (RAD) framework…

Model Structures on Infinity-Categories of Filtrations
Colin Aitken
https://arxiv.org/abs/2402.17921 https://arxiv.org/pdf/2402.17921…

Model Structures on Infinity-Categories of Filtrations
In 1974, Gugenheim and May showed that the cohomology $\text{Ext}_A(R,R)$ of a connected augmented algebra over a field $R$ is generated by elements with $s = 1$ under matric Massey products. In particular, this applies to the $E_2$ page of the $H\mathbb{F}_p$-based Adams spectral sequence. By studying a novel sequence of deformations of a presentably symmetric monoidal stable $\infty$-category $C$, we show that for a variety of spectral sequences coming from filtered spectra, the set of elemen…

This https://arxiv.org/abs/2404.10702 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMM_…

Retrieval Augmented Verification: Unveiling Disinformation with Structured Representations for Zero-Shot Real-Time Evidence-guided Fact-Checking of Multi-modal Social media posts
Social Media posts, where real images are unscrupulously reused along with provocative text to promote a particular idea, have been one of the major sources of disinformation. By design, these claims are without editorial oversight and accessible to a vast population who otherwise may not have access to multiple information sources. This implies the need to fact-check these posts and clearly explain which parts of the posts are fake. In the supervised learning setup, this is often reduced to a …

Context-Aware Clustering using Large Language Models
Sindhu Tipirneni, Ravinarayana Adkathimar, Nurendra Choudhary, Gaurush Hiranandani, Rana Ali Amjad, Vassilis N. Ioannidis, Changhe Yuan, Chandan K. Reddy
https://arxiv.org/abs/2405.00988

Context-Aware Clustering using Large Language Models
Despite the remarkable success of Large Language Models (LLMs) in text understanding and generation, their potential for text clustering tasks remains underexplored. We observed that powerful closed-source LLMs provide good quality clusterings of entity sets but are not scalable due to the massive compute power required and the associated costs. Thus, we propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS), a systematic approach that leverages open-source LLMs for efficient and…

Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering
Zhentao Xu, Mark Jerome Cruz, Matthew Guevara, Tie Wang, Manasi Deshpande, Xiaofeng Wang, Zheng Li
https://arxiv.org/abs/2404.17723

Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering
In customer service technical support, swiftly and accurately retrieving relevant past issues is critical for efficiently resolving customer inquiries. The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue structure and inter-issue relations, which limits performance. We introduce a novel customer service question-answering method that amalgam…

This https://arxiv.org/abs/2312.08976 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Dynamic Retrieval-Augmented Generation
Current state-of-the-art large language models are effective in generating high-quality text and encapsulating a broad spectrum of world knowledge. These models, however, often hallucinate and lack locally relevant factual data. Retrieval-augmented approaches were introduced to overcome these problems and provide more accurate responses. Typically, the retrieved information is simply appended to the main request, restricting the context window size of the model. We propose a novel approach for …

This https://arxiv.org/abs/2404.10702 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMM_…

Retrieval Augmented Verification: Unveiling Disinformation with Structured Representations for Zero-Shot Real-Time Evidence-guided Fact-Checking of Multi-modal Social media posts
Social Media posts, where real images are unscrupulously reused along with provocative text to promote a particular idea, have been one of the major sources of disinformation. By design, these claims are without editorial oversight and accessible to a vast population who otherwise may not have access to multiple information sources. This implies the need to fact-check these posts and clearly explain which parts of the posts are fake. In the supervised learning setup, this is often reduced to a …

A Survey on Retrieval-Augmented Text Generation for Large Language Models
Yizheng Huang, Jimmy Huang
https://arxiv.org/abs/2404.10981 https://

A Survey on Retrieval-Augmented Text Generation for Large Language Models
Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements to address the static limitations of large language models (LLMs) by enabling the dynamic integration of up-to-date external information. This methodology, focusing primarily on the text domain, provides a cost-effective solution to the generation of plausible but incorrect responses by LLMs, thereby enhancing the accuracy and reliability of their outputs through the use of real-world data. As RAG grow…

Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASR
Savitha Murthy, Dinkar Sitaram
https://arxiv.org/abs/2403.10937

Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASR
This paper addresses the problem of improving speech recognition accuracy with lattice rescoring in low-resource languages where the baseline language model is insufficient for generating inclusive lattices. We minimally augment the baseline language model with word unigram counts that are present in a larger text corpus of the target language but absent in the baseline. The lattices generated after decoding with such an augmented baseline language model are more comprehensive. We obtain 21.8% …

This https://arxiv.org/abs/2210.02498 has been replaced.
link: https://scholar.google.com/scholar?q=a

Honest Students from Untrusted Teachers: Learning an Interpretable Question-Answering Pipeline from a Pretrained Language Model
Explainable question answering systems should produce not only accurate answers but also rationales that justify their reasoning and allow humans to check their work. But what sorts of rationales are useful and how can we train systems to produce them? We propose a new style of rationale for open-book question answering, called \emph{markup-and-mask}, which combines aspects of extractive and free-text explanations. In the markup phase, the passage is augmented with free-text markup that enables…

Retrieval Augmented Verification : Unveiling Disinformation with Structured Representations for Zero-Shot Real-Time Evidence-guided Fact-Checking of Multi-modal Social media posts
Arka Ujjal Dey, Artemis Llabr\'es, Ernest Valveny, Dimosthenis Karatzas
https://arxiv.org/abs/2404.10702…

Retrieval Augmented Verification : Unveiling Disinformation with Structured Representations for Zero-Shot Real-Time Evidence-guided Fact-Checking of Multi-modal Social media posts
Social Media posts, where real images are unscrupulously reused along with provocative text to promote a particular idea, have been one of the major sources of disinformation. By design, these claims are without editorial oversight and accessible to a vast population who otherwise may not have access to multiple information sources. This implies the need to fact-check these posts and clearly explain which parts of the posts are fake. In the supervised learning setup, this is often reduced to a …

This https://arxiv.org/abs/2403.05881 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities. However, their application in real clinical settings is challenging due to potential deviations from medical facts and inherent biases. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. Specifically, upon receiving a q…

This https://arxiv.org/abs/2404.10496 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Spiral of Silences: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent. However, the repercussions of LLM-derived content infiltrating the web and influencing the retrieval-generation feedback loop are largely uncharted territories. In this study, we construct and iteratively run a simulation pipeline to deeply investigate the short-term and long-term effects of LLM text on RAG systems. Taking the trending Op…