# You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with a Multi-Agent Conversations arXiv: https://arxiv.org/abs/2502.13001 ยท HTML: https://mlsanity.com/paper/2502.13001/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. - Builds on 28 of its 49 references that are in the map. - First posted on arXiv 18 Feb 2025. ## Placed in - LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/) ## Builds on (most cited first, 28 of 28) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023) - [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024) - [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023) - [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023) - [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018) - [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023) - [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018) - [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021) - [Benchmarking Retrieval-Augmented Generation for Medicine](https://mlsanity.com/paper/2402.13178/) (2024) - [Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://mlsanity.com/paper/2402.18272/) (2024) - [MeetingBank: A Benchmark Dataset for Meeting Summarization](https://mlsanity.com/paper/2305.17529/) (2023) - [PLACES: Prompting Language Models for Social Conversation Synthesis](https://mlsanity.com/paper/2302.03269/) (2023) - [Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs](https://mlsanity.com/paper/2403.05020/) (2024) - [Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective](https://mlsanity.com/paper/2310.19233/) (2023) - [Ten Social Dimensions of Conversations and Relationships](https://mlsanity.com/paper/2001.09954/) (2020) - [Can Large Language Models emulate an inductive Thematic Analysis of semi-structured interviews? An exploration and provocation on the limits of the approach and the model](https://mlsanity.com/paper/2305.13014/) (2023) - [Improving Model Factuality with Fine-grained Critique-based Evaluator](https://mlsanity.com/paper/2410.18359/) (2024) - [Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts](https://mlsanity.com/paper/2308.10410/) (2023) - [What's under the hood: Investigating Automatic Metrics on Meeting Summarization](https://mlsanity.com/paper/2404.11124/) (2024) - [CADS: A Systematic Literature Review on the Challenges of Abstractive Dialogue Summarization](https://mlsanity.com/paper/2406.07494/) (2024) - [Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization](https://mlsanity.com/paper/2410.14545/) (2024) - [What's Wrong? Refining Meeting Summaries with LLM Feedback](https://mlsanity.com/paper/2407.11919/) (2024) - [Who Speaks Next? Multi-party AI Discussion Leveraging the Systematics of Turn-taking in Murder Mystery Games](https://mlsanity.com/paper/2412.04937/) (2024) - [Is my Meeting Summary Good? Estimating Quality with a Multi-LLM Evaluator](https://mlsanity.com/paper/2411.18444/) (2024) - [Leveraging Non-dialogue Summaries for Dialogue Summarization](https://mlsanity.com/paper/2210.09474/) (2022) - [FREDSum: A Dialogue Summarization Corpus for French Political Debates](https://mlsanity.com/paper/2312.04843/) (2023) - [Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization](https://mlsanity.com/paper/2410.18624/) (2024) ## Built on by (most cited first, 3 of 3) - [Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions](https://mlsanity.com/paper/2509.15901/) (2025) - [TIDES: A Longitudinal Bilingual Dataset for Modeling Multi-Party Social Dynamics](https://mlsanity.com/paper/2608.01724/) (2026) - [EvoWiki: Incremental State Overwriting and Traceable Question Answering for Cross-Meeting Knowledge Evolution](https://mlsanity.com/paper/2608.23265/) (2026) ## Abstract (arXiv) Meeting summarization suffers from limited high-quality data, mainly due to privacy restrictions and expensive collection processes. We address this gap with FAME, a dataset of 500 meetings in English and 300 in German produced by MIMIC, our new multi-agent meeting synthesis framework that generates meeting transcripts on a given knowledge source by defining psychologically grounded participant profiles, outlining the conversation, and orchestrating a large language model (LLM) debate. A modular post-processing step refines these outputs, mitigating potential repetitiveness and overly formal tones, ensuring coherent, credible dialogues at scale. We also propose a psychologically grounded evaluation framework assessing naturalness, social behavior authenticity, and transcript difficulties. Human assessments show that FAME approximates real-meeting spontaneity (4.5/5 in naturalness), preserves speaker-centric challenges (3/5 in spoken language), and introduces richer information-oriented difficulty (4/5 in difficulty). These findings highlight that FAME is a good and scalable proxy for real-world meeting conditions. It enables new test scenarios for meeting summarization research and other conversation-centric applications in tasks requiring conversation data or simulating social scenarios under behavioral constraints.