Thailand

As new research on Large Language Models LLMs continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have become numerous. In this paper, we develop a method to automatically assign survey papers to a taxonomy. We collect the metadata of 144 LLMs survey papers and explore three paradigms to classify papers within the taxonomy. Our work indicates that leveraging graph structure information on co-category graphs can significantly outperform the language models in two paradigms; pre-trained language models&#39; fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our model surpasses an average human recognition level and that fine-tuning LLMs using weak labels generated by a smaller model, such as the GCN in this study, can be more effective than using ground-truth labels, revealing the potential of weak-to-strong generalization in the taxonomy classification task.

ACL 2024

Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning

taxonomy classication

graph neural networks

large language models

As new research on Large Language Models LLMs continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have become numerous. In this paper, we develop a method to automatically assign survey papers to a taxonomy. We collect the metadata of 144 LLMs survey papers and explore three paradigms to classify papers within the taxonomy. Our work indicates that leveraging graph structure information on co-category graphs can significantly outperform the language models in two paradigms; pre-trained language models' fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our model surpasses an average human recognition level and that fine-tuning LLMs using weak labels generated by a smaller model, such as the GCN in this study, can be more effective than using ground-truth labels, revealing the potential of weak-to-strong generalization in the taxonomy classification task.

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

We address the challenge of interpreting and reasoning over scientific tables with Large Language Models (LLMs), a crucial aspect of scholarly documents. Despite significant progress in natural language processing, the integration of tabular data into scientific LLMs remains limited. We propose an innovative approach leveraging intermediate task pre-training on table question-answering datasets, followed by model adaptation to comprehend tables in computer science literature. Our findings reveal that incorporating table understanding substantially improves the performance of LLMs on scientific literature understanding tasks, which we showcase in peer-review score prediction. This improvement underscores the importance of utilizing tabular data in the training of scientific language models. The code and models are publicly available at https://github.com/buseskorkmaz/Integrating-Table-Representations-into-LLMs.

Integrating Table Representations into Large Language Models for Improved Scholarly Document Comprehension

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Knowing whether scientific claims are supported by evidence is fundamental to scholarly communication and evidence-based decision-making. We present our approach to Task 1 of the Context24 Shared Task---Contextualizing Scientific Figures and Tables (SDP@ACL2024), which focuses on identifying multimodal evidence from scientific publications that support claims. We finetune CLIP, a state-of-the-art model for image-text similarity tasks, to identify and rank figures and tables in papers that substantiate specific claims. Our methods focus on text and image preprocessing techniques and augmenting the organizer-provided training data with labeled examples from the SciMMIR and MedICaT datasets. Our best-performing model achieved NDCG@5 and NDCG@10 values of 0.26 and 0.30, respectively, on the Context24 test split. Our findings underscore the effectiveness of data augmentation and preprocessing in improving the model's ability in evidence matching.

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OSX at Context24: How Well Can GPT Tackle Contexualizing Scientific Figures and Tables

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Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning

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