MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2503.15272v1
• Date: Wed, 19 Mar 2025 14:46:53 GMT
• Title: MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration
• Abstract summary: 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.
• Score: 63.31211701741323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent collaboration among models has shown promise in reasoning tasks but is underexplored in long-form generation tasks like summarization and question-answering. We extend multi-agent multi-model reasoning to generation, specifically to improving faithfulness through refinement, i.e., revising model-generated outputs to remove factual inconsistencies. We investigate how iterative collaboration among multiple instances and types of large language models (LLMs) enhances subtasks in the refinement process, such as error detection, critiquing unfaithful sentences, and making corrections based on critiques. 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. Additionally, reframing critiquing and refinement as reranking rather than generation tasks improves multi-agent performance. 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 on three summarization datasets as well as on long-form question answering, demonstrating the effectiveness and generalizability of our recipe.

Related papers

• Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning [112.16686518063456]
  We introduce textbfMulti-Agent Test-Time Reinforcement Learning (MATTRL), a framework that injects structured textual experience into multi-agent deliberation at inference time.<n>MATTRL forms a multi-expert team of specialists for multi-turn discussions, retrieves and integrates test-time experiences, and reaches consensus for final decision-making.<n>Across challenging benchmarks in medicine, math, and education, MATTRL improves accuracy by an average of 3.67% over a multi-agent baseline, and by 8.67% over comparable single-agent baselines
  arXiv  Detail & Related papers  (2026-01-14T17:57:43Z)
• Verification-Aware Planning for Multi-Agent Systems [35.82875628010279]
  We present VeriMAP, a framework for multi-agent collaboration with verification-aware planning.<n>The planner decomposes tasks, models subtask dependencies, and encodes planner-defined passing criteria.<n>We show how verification-aware planning enables reliable coordination and iterative refinement in multi-agent systems.
  arXiv  Detail & Related papers  (2025-10-20T02:54:29Z)
• Learning to Summarize by Learning to Quiz: Adversarial Agentic Collaboration for Long Document Summarization [86.98098988779809]
  We propose SummQ, a novel adversarial multi-agent framework for long document summarization.<n>Our approach employs summary generators and reviewers that work collaboratively to create and evaluate comprehensive summaries.<n>We evaluate SummQ on three widely used long document summarization benchmarks.
  arXiv  Detail & Related papers  (2025-09-25T08:36:19Z)
• GM-PRM: A Generative Multimodal Process Reward Model for Multimodal Mathematical Reasoning [12.724393910603299]
  We introduce the Generative Multimodal Process Reward Model (GM-PRM)<n>Instead of a simple scalar score, GM-PRM provides a fine-grained, interpretable analysis of each reasoning step.<n>We show that GM-PRM achieves state-of-the-art results on multiple multimodal math benchmarks.
  arXiv  Detail & Related papers  (2025-08-06T05:10:29Z)
• Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration [63.90193684394165]
  We introduce multi-agent cross-task experiential learning (MAEL), a novel framework that endows LLM-driven agents with explicit cross-task learning and experience accumulation.<n>During the experiential learning phase, we quantify the quality for each step in the task-solving workflow and store the resulting rewards.<n>During inference, agents retrieve high-reward, task-relevant experiences as few-shot examples to enhance the effectiveness of each reasoning step.
  arXiv  Detail & Related papers  (2025-05-29T07:24:37Z)
• MultiConIR: Towards multi-condition Information Retrieval [57.6405602406446]
  We introduce MultiConIR, the first benchmark designed to evaluate retrieval models in multi-condition scenarios.<n>We propose three tasks to assess retrieval and reranking models on multi-condition robustness, monotonic relevance ranking, and query format sensitivity.
  arXiv  Detail & Related papers  (2025-03-11T05:02:03Z)
• Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing [35.686125031177234]
  Multi-Document Summarization (MDS) is a challenging task that focuses on extracting and synthesizing useful information from multiple lengthy documents.<n>We propose a novel framework that leverages inference-time scaling for this task.<n>We also introduce two new evaluation metrics: Consistency-Aware Preference (CAP) score and LLM Atom-Content-Unit (ACU) score.
  arXiv  Detail & Related papers  (2025-02-27T23:34:47Z)
• Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration [81.45763823762682]
  This work aims to bridge the gap by investigating the problem of data synthesis through multi-agent sampling. We introduce Tree Search-based Orchestrated Agents(TOA), where the workflow evolves iteratively during the sequential sampling process. Our experiments on alignment, machine translation, and mathematical reasoning demonstrate that multi-agent sampling significantly outperforms single-agent sampling as inference compute scales.
  arXiv  Detail & Related papers  (2024-12-22T15:16:44Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)<n>We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.<n>We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• MALT: Improving Reasoning with Multi-Agent LLM Training [66.9481561915524]
  MALT (Multi-Agent LLM Training) is a novel post-training strategy that divides the reasoning process into generation, verification, and refinement steps. On MATH, GSM8K, and CSQA, MALT surpasses the same baseline LLM with a relative improvement of 15.66%, 7.42%, and 9.40% respectively.
  arXiv  Detail & Related papers  (2024-12-02T19:30:36Z)
• Towards Robust Multi-Modal Reasoning via Model Selection [7.6621866737827045]
  LLM serves as the "brain" of the agent, orchestrating multiple tools for collaborative multi-step task solving. We propose the $textitM3$ framework as a plug-in with negligible runtime overhead at test-time. Our experiments reveal that our framework enables dynamic model selection, considering both user inputs and subtask dependencies.
  arXiv  Detail & Related papers  (2023-10-12T16:06:18Z)
• AdaMerging: Adaptive Model Merging for Multi-Task Learning [68.75885518081357]
  This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging) It aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data. Compared to the current state-of-the-art task arithmetic merging scheme, AdaMerging showcases a remarkable 11% improvement in performance.
  arXiv  Detail & Related papers  (2023-10-04T04:26:33Z)
• Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions [27.983902791798965]
  We develop a model that generates dilution text that maintains relevance and topical coherence with the image and existing text. We find that the performance of task-specific fusion-based multimodal classifiers drops by 23.3% and 22.5%, respectively, in the presence of dilutions generated by our model. Our work aims to highlight and encourage further research on the robustness of deep multimodal models to realistic variations.
  arXiv  Detail & Related papers  (2022-11-04T17:58:02Z)

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.