mlsanityData to 7 Oct 2026

Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction

arXiv 2406.12197 ↗First posted 18 Jun 2024cs.CL cs.AIcited by 10

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

We propose a multi-agent debate as optimization (DAO) system for event extraction, where the primary objective is to iteratively refine the large language models (LLMs) outputs through debating without parameter tuning. In DAO, we introduce two novel modules: the Diverse-RAG (DRAG) module and the Adaptive Conformal Prediction (AdaCP) module. DRAG systematically retrieves supporting information that best fits the debate discussion, while AdaCP enhances the accuracy and reliability of event extraction by effectively rejecting less promising answers. Experimental results demonstrate a significant reduction in the performance gap between supervised approaches and tuning-free LLM-based methods by 18.1% and 17.8% on ACE05 and 17.9% and 15.2% on CASIE for event detection and argument extraction respectively.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 26 of its 61 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

26 of 61 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System8 Dec
  2. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May
  3. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  6. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  7. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  8. 2023PaLM 2 Technical Reportcited by 1,154
  9. 2023ChatDev: Communicative Agents for Software Developmentcited by 893
  10. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
  11. 2023AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviorscited by 580
  12. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 256
11 more
  1. 2023ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTcited by 250
  2. 2023Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedbackcited by 202
  3. 2023InstructUIE: Multi-task Instruction Tuning for Unified Information Extractioncited by 167
  4. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  5. 2023Agents: An Open-source Framework for Autonomous Language Agentscited by 116
  6. 2023Exploring the Feasibility of ChatGPT for Event Extractioncited by 76
  7. 2024Repoformer: Selective Retrieval for Repository-Level Code Completioncited by 55
  8. 2021Selection and Aggregation of Conformal Prediction Setscited by 46
  9. 2023An Empirical Study on Information Extraction using Large Language Modelscited by 46
  10. 2023Retrieval-Augmented Code Generation for Universal Information Extractioncited by 40
  11. 2024C-RAG: Certified Generation Risks for Retrieval-Augmented Language Modelscited by 27

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 4.

10 papers.

  1. 2025Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Modelscited by 8
  2. 2026From Debate to Decision: Conformal Social Choice for Safe Multi-Agent Deliberationcited by 6
  3. 2025Event Extraction in Large Language Modelcited by 5
  4. 2026Multi-Agent Debate Strategies: Survey, Taxonomy, and Challengescited by 3
  5. 2025Extracting Events Like Code: A Multi-Agent Programming Framework for Zero-Shot Event Extractioncited by 2
  6. 2025PD3: A Project Duplication Detection Framework via Adapted Multi-Agent Debatecited by 1
  7. 2025A Multi-Agent LLM Framework for Multi-Domain Low-Resource In-Context NER via Knowledge Retrieval, Disambiguation and Reflective Analysiscited by 1
  8. 2026Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extractioncited by 1
  9. 2026Budgeted Act-or-Defer Multi-Agent LLM Deliberation with Local Reliability Boundscited by 1
  10. 2025Reflective Agreement: Combining Self-Mixture of Agents with a Sequence Tagger for Robust Event Extractioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026ARMOR-MAD: Adaptive Routing for Heterogeneous Multi-Agent Debate in Large Language Model Reasoning
  2. 2022Retrieval-Augmented Generative Question Answering for Event Argument Extraction
  3. 2026Statistical Scouting Finds Debate-Safe but Not Debate-Useful Cases: A Matched-Ceiling Study of Open-Weight LLM Reasoning Protocols
  4. 2025DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning
  5. 2025Debate Only When Necessary: Adaptive Multiagent Collaboration for Efficient LLM Reasoning
  6. 2025Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate
  7. 2026From Argument Components to Graphs: A Multi-Agent Debate with Confidence Gating for Argument Relations
  8. 2026Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention