MArgE: Meshing Argumentative Evidence from Multiple Large Language Models for Justifiable Claim Verification

• URL: http://arxiv.org/abs/2508.02584v1
• Date: Mon, 04 Aug 2025 16:40:02 GMT
• Title: MArgE: Meshing Argumentative Evidence from Multiple Large Language Models for Justifiable Claim Verification
• Authors: Ming Pok Ng, Junqi Jiang, Gabriel Freedman, Antonio Rago, Francesca Toni,
• Abstract summary: We introduce MArgE, a novel framework to provide formal structure to the evidence from each large language model.<n>We show experimentally that MArgE can significantly outperform single LLMs.
• Score: 12.449402503089164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Leveraging outputs from multiple large language models (LLMs) is emerging as a method for harnessing their power across a wide range of tasks while mitigating their capacity for making errors, e.g., hallucinations. However, current approaches to combining insights from multiple LLMs often involve unstructured interactions (e.g., free debate), resulting in model generations that are not faithfully justifiable. In this work, we introduce MArgE, a novel framework to provide formal structure to the evidence from each LLM, in the form of a tree of extracted arguments, for the task of claim verification. We use a variant of Argumentative LLMs (ArgLLMs), i.e. LLMs driven by frameworks and semantics from the field of computational argumentation, to construct structured argument trees for given claims. This process creates an inspectable pathway from the initial arguments to the final claim verification decisions, providing a faithful justification thereof. We show experimentally that MArgE can significantly outperform single LLMs, including three open-source models (4B to 8B parameters), GPT-4o-mini and existing ArgLLMs, as well as prior methods for unstructured multi-LLM debates. We thus demonstrate the advantages of incorporating formal, argumentative reasoning mechanisms when combining multiple LLM outputs.

Related papers

• CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
  We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
  arXiv  Detail & Related papers  (2025-06-05T17:02:52Z)
• Reasoning LLMs are Wandering Solution Explorers [5.3795217858078805]
  This paper formalizes what constitutes systematic problem solving and identifies common failure modes that reveal reasoning LLMs to be wanderers rather than systematic explorers.<n>Our findings suggest that current models' performance can appear to be competent on simple tasks yet degrade sharply as complexity increases.
  arXiv  Detail & Related papers  (2025-05-26T17:59:53Z)
• Reasoning-CV: Fine-tuning Powerful Reasoning LLMs for Knowledge-Assisted Claim Verification [17.35114345065597]
  Chain-of-Thought (CoT)-Verify paradigm generates CoT-verification paths for the original complex claim without requiring decompositions into sub-claims and separate verification stages.<n> Reasoning-CV demonstrates superior knowledge-assisted claim verification performances compared to existing Decompose-Then-Verify methods.
  arXiv  Detail & Related papers  (2025-05-18T10:28:54Z)
• LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph [57.382255728234064]
  Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning.<n> Knowledge Graphs (KGs) provide rich and reliable contextual information for the reasoning process of LLMs.<n>We propose a novel Lightweight and efficient Prompt learning-ReasOning Framework for KGQA (LightPROF)
  arXiv  Detail & Related papers  (2025-04-04T03:03:47Z)
• Calling a Spade a Heart: Gaslighting Multimodal Large Language Models via Negation [65.92001420372007]
  This paper systematically evaluates state-of-the-art MLLMs across diverse benchmarks.<n>We introduce the first benchmark GaslightingBench, specifically designed to evaluate the vulnerability of MLLMs to negation arguments.
  arXiv  Detail & Related papers  (2025-01-31T10:37:48Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Can formal argumentative reasoning enhance LLMs performances? [0.3659498819753633]
  We present a pipeline (MQArgEng) to evaluate the effect of introducing computational argumentation semantics on the performance of Large Language Models (LLMs) Exploratory results indicate that MQArgEng provides a moderate performance gain in most of the examined topical categories and, as such, show promise and warrant further research.
  arXiv  Detail & Related papers  (2024-05-16T22:09:31Z)
• Potential and Limitations of LLMs in Capturing Structured Semantics: A Case Study on SRL [78.80673954827773]
  Large Language Models (LLMs) play a crucial role in capturing structured semantics to enhance language understanding, improve interpretability, and reduce bias. We propose using Semantic Role Labeling (SRL) as a fundamental task to explore LLMs' ability to extract structured semantics. We find interesting potential: LLMs can indeed capture semantic structures, and scaling-up doesn't always mirror potential. We are surprised to discover that significant overlap in the errors is made by both LLMs and untrained humans, accounting for almost 30% of all errors.
  arXiv  Detail & Related papers  (2024-05-10T11:44:05Z)
• Argumentative Large Language Models for Explainable and Contestable Claim Verification [13.045050015831903]
  We introduce ArgLLMs, a method for augmenting large language models with argumentative reasoning.<n>ArgLLMs construct argumentation frameworks, which then serve as the basis for formal reasoning in support of decision-making.<n>We evaluate ArgLLMs' performance experimentally in comparison with state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-05-03T13:12:28Z)
• Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification [22.92500697622486]
  We propose a framework designed to break down any claim paired with evidence into atomic reasoning types.<n>We use this framework to create RECV, the first claim verification benchmark, incorporating real-world claims.<n>We evaluate three state-of-the-art proprietary LLMs under multiple prompt settings.
  arXiv  Detail & Related papers  (2024-02-16T14:52:05Z)
• Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning [74.90592233107712]
  We propose a Direct-Indirect Reasoning (DIR) method, which considers Direct Reasoning (DR) and Indirect Reasoning (IR) as multiple parallel reasoning paths that are merged to derive the final answer.<n>Our DIR method is simple yet effective and can be straightforwardly integrated with existing variants of CoT methods.
  arXiv  Detail & Related papers  (2024-02-06T03:41:12Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• ThinkSum: Probabilistic reasoning over sets using large language models [18.123895485602244]
  We propose a two-stage probabilistic inference paradigm, ThinkSum, which reasons over sets of objects or facts in a structured manner. We demonstrate the possibilities and advantages of ThinkSum on the BIG-bench suite of LLM evaluation tasks.
  arXiv  Detail & Related papers  (2022-10-04T00:34:01Z)

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.