| 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 . |
| Outcome: | a new study shows that pre-trained agents retain English syntax while learning to convey intended meaning . the proposed training constraints can be used to mitigate language drift . |
Similar Papers
Multitasking Inhibits Semantic Drift (2021.naacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed model eliminates semantic drift in a well-studied family of signaling games while improving sample efficiency. |
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning (2020.acl-main)
Copied to clipboard
| 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. |
Emergent Communication Pretraining for Few-Shot Machine Translation (2020.coling-main)
Copied to clipboard
| Challenge: | state-of-the-art models that rely on multilingual pretrained encoders achieve sample efficiency in downstream applications, but lack abundant amounts of unlabelled text. |
| Approach: | They propose a method to pretrain neural networks via emergent communication from referential games by grounding communication on images as a crude approximation of real-world environments. |
| Outcome: | The proposed method significantly improves machine translation in few-shot settings and provides an evaluation protocol to probe the properties of emergent languages ex vitro. |
Language Agents: Foundations, Prospects, and Risks (2024.emnlp-tutorials)
Copied to clipboard
| 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. |
From Word to World: Can Large Language Models be Implicit Text-based World Models? (2026.acl-long)
Copied to clipboard
Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin, Dongdong Zhang, Cheng Qian, Zeping Li, Xiaoteng Ma, Guanhua Chen, Heng Ji
| Challenge: | Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time. |
| Approach: | They propose a framework that reframes language modeling as next-state prediction under interaction. |
| Outcome: | The proposed framework evaluates world models in text-based environments . it shows that sufficiently trained models capture coherent environment dynamics . |
Co-evolution of language and agents in referential games (2021.eacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed model outperforms cultural transmission in a population of agents and takes into account learning biases of the learners. |
How agents see things: On visual representations in an emergent language game (D18-1)
Copied to clipboard
| Challenge: | Existing studies focus on the agents’ symbol usage, rather than on their representation of visual input. |
| Approach: | They propose to use visual representations of objects to create language-like communication systems by integrating them with the visual input of a game. |
| Outcome: | The proposed model and setup of Lazaridou et al. (2017) show that the representations of the agents' symbols do not capture the conceptual properties of the objects depicted in the input images. |
Augmenting Multi-Agent Communication with State Delta Trajectory (2025.emnlp-main)
Copied to clipboard
| Challenge: | Multi-agent systems based on large language models (LLMs) have shown to be effective in downstream tasks. |
| Approach: | They propose a protocol that transfers both natural language tokens and token-wise state transition trajectory from one agent to another. |
| Outcome: | The proposed protocol can transfer both natural language tokens and token-wise state transition trajectory from one agent to another. |
Emergent Linguistic Phenomena in Multi-Agent Communication Games (D19-1)
Copied to clipboard
| Challenge: | a recent study examines the behavior of linguistic agents in a community-level setting . a linguistic continuum emerges where neighboring languages are more mutually intelligible than farther removed ones . |
| Approach: | They propose a multi-agent communication framework for studying linguistic phenomena at the community level. |
| Outcome: | The proposed framework can reproduce complex linguistic behavior observed in natural language . it can be used to study interactions between perceptually-enabled agents . |
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work has used text-based games as a testbed for developing autonomous agents that operate using natural language. |
| Approach: | They propose an inverse dynamics decoder to regularize representation space and encourage exploration to reduce the amount of semantic information available to a learning agent. |
| Outcome: | The proposed model achieves high scores even in the absence of language semantics on Zork I . |