| Challenge: | Recent work has focused on the emergence of language in cooperative tasks where neural network agents learn a communication protocol from scratch to solve problems together. |
| Approach: | They propose a task transfer method and symbolic mapping architecture to help agents learn a compositional and symmetric language in dialog games. |
| Outcome: | The proposed method can help agents learn a compositional and symmetric language in complex settings like dialog games and the proposed architecture promotes vocabulary expansion. |
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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. |
Co-evolution of language and agents in referential games (2021.eacl-main)
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| Challenge: | Referential games allow neural agents to learn language, but they do not take into account the learning biases of the learners. |
| Approach: | They propose to model cultural and architectural evolution in a population of agents to take into account learning biases of the language learners and let them co-evolve. |
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Language Agents: Foundations, Prospects, and Risks (2024.emnlp-tutorials)
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| Challenge: | Language agents are autonomous agents that can follow language instructions to perform diverse tasks in real-world or simulated environments. |
| Approach: | They propose to provide a conceptual framework for language agents and a comprehensive discussion on key topics. |
| Outcome: | The proposed tutorial provides a conceptual framework of language agents and comprehensive discussion on important topic areas. |
Countering Language Drift via Visual Grounding (D19-1)
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| Challenge: | Emergent multi-agent communication protocols are different from natural language . a long-standing goal of artificial intelligence research is to develop agents that can cooperate with other agents . |
| Approach: | They propose to use syntactic and semantic constraints to improve communication . they propose to combine these constraints with auxiliary training constraints to reduce language drift . |
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Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use (2024.emnlp-main)
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| Challenge: | Existing methods for embodied agents to learn and perform tasks use low-level instructions, which may not reflect natural human communication. |
| Approach: | They propose to use different types of language inputs to facilitate reinforcement learning (RL) embodied agents. |
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MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning (2025.acl-long)
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| Challenge: | Existing studies focus on prompting and developing workflows with frozen LLMs. |
| Approach: | They propose a multi-agentic framework for collaborative LLMs with reinforcement learning that leverages multi-gendered frameworks to enhance collaboration. |
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Emergent Language-Based Coordination In Deep Multi-Agent Systems (2022.emnlp-tutorials)
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| Challenge: | Pre-trained deep networks are the standard building blocks of modern AI applications. |
| Approach: | This tutorial will introduce deep net emergent communication and discuss current shortcomings . participants will implement and analyze two emergentic communication setups from the literature . |
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Multitasking Inhibits Semantic Drift (2021.naacl-main)
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| Challenge: | Existing studies have found that LLP training is prone to semantic drift (use of messages inconsistent with their natural language meanings) |
| Approach: | They propose to use latent language policies to train neural LLPs to eliminate semantic drift in a well-studied family of signaling games to reduce drift and improve sample efficiency. |
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Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models (2025.acl-long)
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| Challenge: | Existing methods to fine-tune Large Language Models without human annotations are lacking in the field of natural language training. |
| Approach: | They propose an environment-guided neural-symbolic self-training framework to overcome two main challenges: the scarcity of symbolic data and the limited proficiency of LLMs in processing symbolic language. |
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Agents generalize to novel levels of abstraction by using adaptive linguistic strategies (2025.findings-acl)
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| Challenge: | Abstract: Abstracts are fundamental to building well-generalizing models. |
| Approach: | They propose to use a concept-level reference game to generalize concepts . they find that agents can learn robust concepts based on which they can generalize . |
| Outcome: | The proposed model can generalize from generic to very specific concepts, while reusing many messages from training. |