CollabEval: Enhancing LLM-as-a-Judge via Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2603.00993v1
• Date: Sun, 01 Mar 2026 08:45:13 GMT
• Title: CollabEval: Enhancing LLM-as-a-Judge via Multi-Agent Collaboration
• Authors: Yiyue Qian, Shinan Zhang, Yun Zhou, Haibo Ding, Diego Socolinsky, Yi Zhang,
• Abstract summary: Large Language Models (LLMs) have revolutionized AI-generated content evaluation, with the LLM-as-a-Judge paradigm becoming increasingly popular.<n>We propose CollabEval, a novel multi-agent evaluation framework that implements a three-phase Collaborative Evaluation process.<n>Our experiments demonstrate that CollabEval consistently outperforms single-LLM approaches across multiple dimensions.
• Score: 8.457834313970165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have revolutionized AI-generated content evaluation, with the LLM-as-a-Judge paradigm becoming increasingly popular. However, current single-LLM evaluation approaches face significant challenges, including inconsistent judgments and inherent biases from pre-training data. To address these limitations, we propose CollabEval, a novel multi-agent evaluation framework that implements a three-phase Collaborative Evaluation process: initial evaluation, multi-round discussion, and final judgment. Unlike existing approaches that rely on competitive debate or single-model evaluation, CollabEval emphasizes collaboration among multiple agents with strategic consensus checking for efficiency. Our extensive experiments demonstrate that CollabEval consistently outperforms single-LLM approaches across multiple dimensions while maintaining robust performance even when individual models struggle. The framework provides comprehensive support for various evaluation criteria while ensuring efficiency through its collaborative design.

Related papers

• MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning [68.91090643731987]
  Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems.<n>Existing approaches are limited to separate fields and can only handle multi-agent decision-making with a single objective.<n>We propose MO-mix to solve the multi-objective multi-agent reinforcement learning (MOMARL) problem.
  arXiv  Detail & Related papers  (2026-02-28T16:25:22Z)
• MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains [35.511656323075506]
  We have developed a large-scale, domain-adaptive benchmark for multimodal evaluation.<n>This benchmark integrates over 30 datasets, encompassing 15 modalities and 20 predictive tasks.<n>We have also developed an open-source, unified, and automated evaluation pipeline.
  arXiv  Detail & Related papers  (2025-11-09T16:37:09Z)
• A Multi-To-One Interview Paradigm for Efficient MLLM Evaluation [63.76972456980632]
  We propose a multi-to-one interview paradigm for efficient MLLM evaluation.<n>Our framework consists of (i) a two-stage interview strategy with pre-interview and formal interview phases, (ii) dynamic adjustment of weights to ensure fairness, and (iii) an adaptive mechanism for question difficulty-level chosen.
  arXiv  Detail & Related papers  (2025-09-18T12:07:40Z)
• Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple Judges [29.71813688074828]
  We propose an efficient multi-turn dialogue evaluator that captures the collective wisdom of multiple LLM judges by aggregating their preference knowledge into a single model.<n>Our approach preserves the advantages of diverse multi-judge feedback while drastically reducing the evaluation cost, enabling fast and flexible dialogue quality assessment.
  arXiv  Detail & Related papers  (2025-08-01T09:26:01Z)
• Offline Multi-agent Reinforcement Learning via Score Decomposition [51.23590397383217]
  offline cooperative multi-agent reinforcement learning (MARL) faces unique challenges due to distributional shifts.<n>This work is the first work to explicitly address the distributional gap between offline and online MARL.
  arXiv  Detail & Related papers  (2025-05-09T11:42:31Z)
• MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration [63.31211701741323]
  We extend multi-agent multi-model reasoning to generation, specifically to improving faithfulness through refinement.<n>We design intrinsic evaluations for each subtask, with our findings indicating that both multi-agent (multiple instances) and multi-model (diverse LLM types) approaches benefit error detection and critiquing.<n>We consolidate these insights into a final "recipe" called Multi-Agent Multi-Model Refinement (MAMM-Refine), where multi-agent and multi-model collaboration significantly boosts performance.
  arXiv  Detail & Related papers  (2025-03-19T14:46:53Z)
• MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents [59.825725526176655]
  Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents.<n>Existing benchmarks either focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.<n>We introduce MultiAgentBench, a benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
  arXiv  Detail & Related papers  (2025-03-03T05:18:50Z)
• Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents [21.328894420020443]
  Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks.<n>This paper proposes a new benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments.
  arXiv  Detail & Related papers  (2025-02-27T13:31:13Z)
• MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation [22.19073789961769]
  generative Large Language Models (LLMs) have been remarkable, however, the quality of the text generated by these models often reveals persistent issues. We propose the MATEval: A "Multi-Agent Text Evaluation framework" Our framework incorporates self-reflection and Chain-of-Thought strategies, along with feedback mechanisms, to enhance the depth and breadth of the evaluation process.
  arXiv  Detail & Related papers  (2024-03-28T10:41:47Z)
• Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration [28.299379264080603]
  Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have explored human-like problem-solving strategies, such as self-correct, to push further the boundary of single-model reasoning ability. We introduce a multi-agent collaboration strategy that emulates the academic peer review process.
  arXiv  Detail & Related papers  (2023-11-14T13:27:07Z)
• Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
  Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes. We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-30T07:11:39Z)
• ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate [57.71597869337909]
  We build a multi-agent referee team called ChatEval to autonomously discuss and evaluate the quality of generated responses from different models. Our analysis shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments.
  arXiv  Detail & Related papers  (2023-08-14T15:13:04Z)

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.