Papers with multi-agent communication
Know your audience: specializing grounded language models with listener subtraction (2023.eacl-main)
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| Challenge: | Effective communication requires adapting to the idiosyncrasies of each communicative context. |
| Approach: | They propose a method for specializing grounded language models without supervision . they fine-tune an attention-based adapter between a CLIP vision encoder and a large language model . |
| Outcome: | The proposed method allows a speaker to adapt to the idiosyncracies of the listeners without supervision. |
Tandem Training for Language Models (2026.eacl-long)
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| Challenge: | As language models improve, their actions and reasoning will become difficult or impossible for weaker agents and humans to follow, undermining interpretability and oversight. |
| Approach: | They propose a tandem training paradigm that allows models to adapt their language to weaker partners by intermittently and randomly sampling a frozen weak model instead of the strong model being trained. |
| Outcome: | The proposed model is able to produce solutions that are intelligible to weaker agents and humans while keeping task accuracy high. |
AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction (2025.emnlp-main)
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| Challenge: | Existing methods for multi-agent collaboration rely on static or graph-based topologies lacking flexibility and adaptability. |
| Approach: | They propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure. |
| Outcome: | The proposed method achieves superior performance while significantly reducing communication overhead. |
Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication (2024.findings-emnlp)
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Weize Chen, Chenfei Yuan, Jiarui Yuan, Yusheng Su, Chen Qian, Cheng Yang, Ruobing Xie, Zhiyuan Liu, Maosong Sun
| Challenge: | Natural language (NL) has long been the predominant format for human cognition and communication, but its utility in LLMs has not been thoroughly examined. |
| Approach: | They propose to allow LLMs to choose the most suitable format before reasoning or communicating, and to automate the selection process. |
| Outcome: | The proposed format improves reasoning efficiency and reduces token usage while maintaining communicative effectiveness. |
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning (2020.acl-main)
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| Challenge: | a new method for combining multi-agent communication with traditional data-driven approaches to natural language learning is proposed . we combine the two types of learning with a goal of teaching agents to communicate with humans in natural language. |
| Approach: | They propose a method that combines traditional data-driven approaches to natural language learning with multi-agent self-play environments. |
| Outcome: | The proposed method outperforms other methods in communicating with humans in natural language. |