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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Papilusion at DAGPap24: Paper or Illusion? Detecting AI-generated Scientific Papers

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CiteAssist: A System for Automated Preprint Citation and BibTeX Generation

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.

Harnessing CLIP for Evidence Identification in Scientific Literature: A Multimodal Approach to Context24 Shared Task

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

Author affiliation information plays a key role in bibliometric analyses and is essential for evaluating studies. However, as author affiliation information has not been standardized, which leads to difficulties such as synonym ambiguity and incomplete data during automated processing. To address the challenge, this paper proposes an end-to-end entity recognition and disambiguation framework for identifying author affiliation from literature publications. For entity disambiguation, an algorithm combining word embedding and spatial embedding is presented considering that author affiliation texts often contain rich geographic information. The disambiguation algorithm utilizes the semantic information and geographic information, which effectively enhances entity recognition and disambiguation effect. In addition, the proposed framework facilitates the effective utilization of the extensive literature in the PubMed database for comprehensive bibliometric analysis. The experimental results verify the robustness and effectiveness of the algorithm.

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Large Language Models (LLMs) have shown remarkable potential across various domains, yet their application in addressing complex scientific problems remains a formidable challenge. This paper presents a novel methodology to augment the problem-solving capabilities of LLMs by assigning them roles as domain-specific experts. By simulating a panel of experts, each LLM is tasked with delivering professional and cautious responses to scientific inquiries. Our approach involves querying multiple LLMs and assessing the consistency of their responses. High agreement among the LLMs suggests greater confidence in the proposed solution, whereas discrepancies prompt a collaborative discussion among the LLMs to reach a consensus. This method emulates real-world scientific problem-solving processes, fostering a more reliable and robust mechanism for LLMs to tackle scientific questions. Our experimental results show that assigning roles to multiple LLMs as domain-specific experts significantly improves their accuracy and reliability in solving scientific problems. This framework has the potential to advance the application of AI in scientific research, enhancing its effectiveness and trustworthiness.

Simulating Expert Discussions with Multi-agent for Enhanced Scientific Problem Solving

Scientific extreme summarization, the task of generating concise one-sentence summaries (TLDRs) for scientific papers, presents significant challenges due to the need for deep domain-specific understanding and the ability to distill salient information. This study identifies the critical role of titles and keywords in enhancing TLDR generation through quantitative analysis. We propose a novel method, External Attention Prompting (EAP), which leverages LLMs by guiding them to focus on the most critical parts of the source text through varying degrees of attention signals. Our method employs Markdown emphasis syntax to annotate attention levels, enabling LLMs to prioritize salient information effectively. Extensive experiments demonstrate that EAP significantly outperforms baseline methods across various LLMs and metrics in both zero-shot and few-shot settings. Further evaluations by GPT-4 demonstrate that EAP can enable LLMs to generate TLDRs of higher human-aligned quality.

Guiding Large Language Models via External Attention Prompting for Scientific Extreme Summarization

Generative AI, as it becomes increasingly integrated into our lives, has brought convenience, though some concerns have arisen regarding its potential impact on the rigor and authenticity of scientific research. To encourage the development of robust and reliable automatically-generated scientific text detection systems, the "DAGPap24: Detecting Automatically Generated Scientific Papers" competition was held and shared the same task with the 4th Workshop on Scholarly Document Processing (SDP 2024) to be held at ACL 2024. In the DAGPap24 competition, participants were tasked with constructing a generative text detection model that could accurately distinguish between the human written fragment, the synonym replacement fragment, the ChatGPT rewrite fragment, and the generated summary fragment of a paper. In this competition, we first conducted a comprehensive analysis of the training set to build a generative paper detection model. Then we tried various language models, including SciBERT, ALBERT, DeBERTa, RoBERTa, etc. After that, we introduced an Anomalous Label Smoothing (ALS) method and a majority voting method to improve the final results. Finally, we achieved 0.9948 and 0.9944 F1 scores during the development and testing phases respectively, and we achieved second place in the competition.

Utilizing an Ensemble Model with Anomalous Label Smoothing to Detect Generated Scientific Papers

Research papers are long documents that contain information about various aspects such as background, prior work, methodology, and results. Existing works on scientific document representation learning only leverage the title and abstract of the paper. We present CoSAEmb, a model that learns representations from the full text of 97402 scientific papers from the S2ORC dataset. We present a novel supervised contrastive training framework for long documents using triplet loss and margin gradation. Our framework can be used to learn representations of long documents with any existing encoder-only transformer model without retraining it from scratch. CoSAEmb shows improved performance on information retrieval from the paper's full text in comparison to models trained only on paper titles and abstracts. We also evaluate CoSAEmb on SciRepEval and CSFCube benchmarks, showing comparable performance with existing state-of-the-art models.

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

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

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