Can Large Language Models perform Relation-based Argument Mining?

• URL: http://arxiv.org/abs/2402.11243v1
• Date: Sat, 17 Feb 2024 10:37:51 GMT
• Title: Can Large Language Models perform Relation-based Argument Mining?
• Authors: Deniz Gorur, Antonio Rago, Francesca Toni
• Abstract summary: Argument mining (AM) is the process of automatically extracting arguments, their components and/or relations amongst arguments and components from text. Relation-based AM (RbAM) is a form of AM focusing on identifying agreement (support) and disagreement (attack) relations amongst arguments. We show that general-purpose Large Language Models (LLMs), appropriately primed and prompted, can significantly outperform the best performing (RoBERTa-based) baseline.
• Score: 15.362683263839772
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Argument mining (AM) is the process of automatically extracting arguments, their components and/or relations amongst arguments and components from text. As the number of platforms supporting online debate increases, the need for AM becomes ever more urgent, especially in support of downstream tasks. Relation-based AM (RbAM) is a form of AM focusing on identifying agreement (support) and disagreement (attack) relations amongst arguments. RbAM is a challenging classification task, with existing methods failing to perform satisfactorily. In this paper, we show that general-purpose Large Language Models (LLMs), appropriately primed and prompted, can significantly outperform the best performing (RoBERTa-based) baseline. Specifically, we experiment with two open-source LLMs (Llama-2 and Mistral) with ten datasets.

Related papers

• The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs [54.59207567677249]
  Large language models (LLMs) still struggle across tasks outside of high-resource languages.<n>In this work, we investigate cross-lingual transfer to lower-resource languages where task-specific post-training data is scarce.
  arXiv  Detail & Related papers  (2025-05-23T20:28:31Z)
• New Dataset and Methods for Fine-Grained Compositional Referring Expression Comprehension via Specialist-MLLM Collaboration [49.180693704510006]
  Referring Expression (REC) is a cross-modal task that evaluates the interplay of language understanding, image comprehension, and language-to-image grounding.<n>It serves as an essential testing ground for Multimodal Large Language Models (MLLMs)
  arXiv  Detail & Related papers  (2025-02-27T13:58:44Z)
• Demystifying Multilingual Chain-of-Thought in Process Reward Modeling [71.12193680015622]
  We tackle the challenge of extending process reward models (PRMs) to multilingual settings. We train multilingual PRMs on a dataset spanning seven languages, which is translated from English. Our results highlight the sensitivity of multilingual PRMs to both the number of training languages and the volume of English data.
  arXiv  Detail & Related papers  (2025-02-18T09:11:44Z)
• Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation [81.18701211912779]
  We introduce an Adaptive Multi-Aspect Retrieval-augmented over KGs (Amar) framework. This method retrieves knowledge including entities, relations, and subgraphs, and converts each piece of retrieved text into prompt embeddings. Our method has achieved state-of-the-art performance on two common datasets.
  arXiv  Detail & Related papers  (2024-12-24T16:38:04Z)
• MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale [66.73529246309033]
  multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. Existing instruction-tuning datasets only provide phrase-level answers without any intermediate rationales. We introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales.
  arXiv  Detail & Related papers  (2024-12-06T18:14:24Z)
• A Generative Marker Enhanced End-to-End Framework for Argument Mining [0.8213829427624407]
  Argument Mining (AM) involves identifying and extracting Argumentative Components (ACs) and their corresponding Argumentative Relations (ARs) This work introduces a generative paradigm-based end-to-end framework argTANL. It frames the argumentative structures into label-augmented text, called Augmented Natural Language (ANL)
  arXiv  Detail & Related papers  (2024-06-12T19:22:29Z)
• Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning [49.3242278912771]
  We introduce a novel multimodal RAG framework named RMR (Retrieval Meets Reasoning) The RMR framework employs a bi-modal retrieval module to identify the most relevant question-answer pairs. It significantly boosts the performance of various vision-language models across a spectrum of benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-31T14:23:49Z)
• MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time [51.5039731721706]
  MindStar is a purely inference-based searching method for large language models. It formulates reasoning tasks as searching problems and proposes two search ideas to identify the optimal reasoning paths. It significantly enhances the reasoning abilities of open-source models, such as Llama-2-13B and Mistral-7B, and achieves comparable performance to GPT-3.5 and Grok-1.
  arXiv  Detail & Related papers  (2024-05-25T15:07:33Z)
• Assisted Debate Builder with Large Language Models [11.176301807521462]
  We introduce ADBL2, an assisted debate builder tool. It is based on the capability of large language models to generalise and perform relation-based argument mining. As a by-product, we provide the first fine-tuned Mistral-7B large language model for relation-based argument mining.
  arXiv  Detail & Related papers  (2024-05-14T13:42:12Z)
• Analyzing the Role of Semantic Representations in the Era of Large Language Models [104.18157036880287]
  We investigate the role of semantic representations in the era of large language models (LLMs) We propose an AMR-driven chain-of-thought prompting method, which we call AMRCoT. We find that it is difficult to predict which input examples AMR may help or hurt on, but errors tend to arise with multi-word expressions.
  arXiv  Detail & Related papers  (2024-05-02T17:32:59Z)
• DMON: A Simple yet Effective Approach for Argument Structure Learning [33.96187185638286]
  Argument structure learning (ASL) entails predicting relations between arguments. Despite its broad utilization, ASL remains a challenging task because it involves examining the complex relationships between the sentences in a potentially unstructured discourse. We have developed a simple yet effective approach called Dual-tower Multi-scale cOnvolution neural Network(DMON) for the ASL task.
  arXiv  Detail & Related papers  (2024-05-02T11:56:16Z)
• Efficient argument classification with compact language models and ChatGPT-4 refinements [0.0]
  This paper presents comparative studies between a few deep learning-based models in argument mining. The main novelty of this paper is the ensemble model which is based on BERT architecture and ChatGPT-4 as fine tuning model. The presented results show that BERT+ChatGPT-4 outperforms the rest of the models including other Transformer-based and LSTM-based models.
  arXiv  Detail & Related papers  (2024-03-20T16:24:10Z)
• CLadder: Assessing Causal Reasoning in Language Models [82.8719238178569]
  We investigate whether large language models (LLMs) can coherently reason about causality. We propose a new NLP task, causal inference in natural language, inspired by the "causal inference engine" postulated by Judea Pearl et al.
  arXiv  Detail & Related papers  (2023-12-07T15:12:12Z)
• Exploring the Potential of Large Language Models in Computational Argumentation [54.85665903448207]
  Large language models (LLMs) have demonstrated impressive capabilities in understanding context and generating natural language. This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models, and LLaMA2 models, in both zero-shot and few-shot settings.
  arXiv  Detail & Related papers  (2023-11-15T15:12:15Z)
• Multimodal Chain-of-Thought Reasoning in Language Models [94.70184390935661]
  We propose Multimodal-CoT that incorporates language (text) and vision (images) modalities into a two-stage framework. Experimental results on ScienceQA and A-OKVQA benchmark datasets show the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2023-02-02T07:51:19Z)
• Full-Text Argumentation Mining on Scientific Publications [3.8754200816873787]
  We introduce a sequential pipeline model combining ADUR and ARE for full-text SAM. We provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges.
  arXiv  Detail & Related papers  (2022-10-24T10:05:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.