Tootfinder

Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages
Sankalp Bahad, Pruthwik Mishra, Karunesh Arora, Rakesh Chandra Balabantaray, Dipti Misra Sharma, Parameswari Krishnamurthy
https://arxiv.org/abs/2405.04829

Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages
Named Entity Recognition (NER) is a useful component in Natural Language Processing (NLP) applications. It is used in various tasks such as Machine Translation, Summarization, Information Retrieval, and Question-Answering systems. The research on NER is centered around English and some other major languages, whereas limited attention has been given to Indian languages. We analyze the challenges and propose techniques that can be tailored for Multilingual Named Entity Recognition for Indian Lang…

Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Lihui Liu, Zihao Wang, Ruizhong Qiu, Yikun Ban, Hanghang Tong
https://arxiv.org/abs/2404.04264

Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Despite the superb performance in many tasks, large language models (LLMs) bear the risk of generating hallucination or even wrong answers when confronted with tasks that demand the accuracy of knowledge. The issue becomes even more noticeable when addressing logic queries that require multiple logic reasoning steps. On the other hand, knowledge graph (KG) based question answering methods are capable of accurately identifying the correct answers with the help of knowledge graph, yet its accurac…

Breathing New Life into Existing Visualizations: A Natural Language-Driven Manipulation Framework
Can Liu, Jiacheng Yu, Yuhan Guo, Jiayi Zhuang, Yuchu Luo, Xiaoru Yuan
https://arxiv.org/abs/2404.06039

Breathing New Life into Existing Visualizations: A Natural Language-Driven Manipulation Framework
We propose an approach to manipulate existing interactive visualizations to answer users' natural language queries. We analyze the natural language tasks and propose a design space of a hierarchical task structure, which allows for a systematic decomposition of complex queries. We introduce a four-level visualization manipulation space to facilitate in-situ manipulations for visualizations, enabling a fine-grained control over the visualization elements. Our methods comprise two essential compo…

Beim Aufräumen gefunden. Muss 2000 gewesen sein. „Die Namen der meisten Interprice-Mitarbeiter bleiben geheim. Headhunter lauern schon.“ Artikel in der #Zitty über #Interprice. Die Firma ist rasant gewachsen. Das war die Dotcom-Zeit. Leider sind die Kunden nicht gewachsen. Die Firma gibt es noch unter anderem Namen. Ein Kollege, der auf dem Bild neben mir steht, arbeitet noch heute dort (nebenberuflich). Ansonsten ist er Professor.
„Aber auch eine Zweitnutzung als neo-dadaistische Sprachmaschine liegt in Reichweite.“
Ich bin übrigens über #Dada zur Linguistik gekommen. Die Dadaisten haben Annagrame gemacht. Das hatte ich dann als Student mit dem Computer perfektioniert. Erst den Duden als Wörterbuch dazugeschaltet und dann versucht, Sätze auszufiltern. Dafür braucht man eine richtige Grammatik.
Ja. Merkwürdig wie man manchmal wohin kommt.

Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages
Sankalp Bahad, Pruthwik Mishra, Karunesh Arora, Rakesh Chandra Balabantaray, Dipti Misra Sharma, Parameswari Krishnamurthy
https://arxiv.org/abs/2405.04829

Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages
Named Entity Recognition (NER) is a useful component in Natural Language Processing (NLP) applications. It is used in various tasks such as Machine Translation, Summarization, Information Retrieval, and Question-Answering systems. The research on NER is centered around English and some other major languages, whereas limited attention has been given to Indian languages. We analyze the challenges and propose techniques that can be tailored for Multilingual Named Entity Recognition for Indian Lang…

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. …

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature
Dawei Li, Shu Yang, Zhen Tan, Jae Young Baik, Sunkwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen
https://arxiv.org/abs/2405.04819

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature
Recent advancements in large language models (LLMs) have achieved promising performances across various applications. Nonetheless, the ongoing challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains. In this work, we introduce DALK, a.k.a. Dynamic Co-Augmentation of LLMs and KG, to address this limitation and demonstrate its ability on studying Alzheimer's Disease (AD), a specialized sub-field in biomedicine and a global health prior…

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature
Dawei Li, Shu Yang, Zhen Tan, Jae Young Baik, Sunkwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen
https://arxiv.org/abs/2405.04819

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature
Recent advancements in large language models (LLMs) have achieved promising performances across various applications. Nonetheless, the ongoing challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains. In this work, we introduce DALK, a.k.a. Dynamic Co-Augmentation of LLMs and KG, to address this limitation and demonstrate its ability on studying Alzheimer's Disease (AD), a specialized sub-field in biomedicine and a global health prior…

Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles
Andrea Zugarini, Kamyar Zeinalipour, Surya Sai Kadali, Marco Maggini, Marco Gori, Leonardo Rigutini
https://arxiv.org/abs/2404.06186

Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles
Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues that distinguish them from traditional crossword puzzles. Despite there exist several publicly available clue-answer pair databases for traditional crosswords, educational clue-answer pairs datasets are missing. In this article, we propose a methodology to build educational clue generation datasets that can be used to …

Text-Based Reasoning About Vector Graphics
Zhenhailong Wang, Joy Hsu, Xingyao Wang, Kuan-Hao Huang, Manling Li, Jiajun Wu, Heng Ji
https://arxiv.org/abs/2404.06479

Text-Based Reasoning About Vector Graphics
While large multimodal models excel in broad vision-language benchmarks, they often struggle with tasks requiring precise perception of low-level visual details, such as comparing line lengths or solving simple mazes. In particular, this failure mode persists in question-answering tasks about vector graphics -- images composed purely of 2D objects and shapes. To address this challenge, we propose the Visually Descriptive Language Model (VDLM), which performs text-based reasoning about vector gr…