| Challenge: | Current methods for learning human language are too data inefficient to learn it in this way. |
| Approach: | They propose to train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol. |
| Outcome: | The proposed algorithm minimizes the number of on-policy interactions while learning human language while minimizing the number on-political interactions. |
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| Challenge: | Recent work has focused on word-based conversational agents that tend to invent their language rather than leveraging natural language. |
| Approach: | They propose two methods to counter language drift by combining S2P and Seeded Iterated Learning to minimize their weaknesses. |
| Outcome: | The proposed methods reduce late-stage training collapses and higher negative likelihood when evaluated on human corpus. |
Self-imitation Learning for Action Generation in Text-based Games (2023.eacl-main)
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| Challenge: | Text-based games are situated systems where the game agents observe textual descriptions, and generate textual commands to interact with the environment. |
| Approach: | They propose a confidence-based self-imitation model to generate action candidates for the RL agent by exploiting past valuable trajectories to adapt a pre-trained language model towards a target game. |
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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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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. |
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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. |
Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks (2026.acl-short)
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| Challenge: | Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. |
| Approach: | They propose a framework that integrates Language Learning Tasks alongside standard next-token prediction to stimulate the acquisition of morphological, syntactic, and semantic knowledge. |
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Language Models are Few-Shot Butlers (2021.emnlp-main)
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| Challenge: | Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. |
| Approach: | They propose a two-stage procedure to learn from a small set of demonstrations and a simple reinforcement learning algorithm to improve by interacting with an environment. |
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Reader: Model-based language-instructed reinforcement learning (2023.emnlp-main)
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| Challenge: | Existing models of RL are limited and need to be re-trained for every new problem. |
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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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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. |
| Outcome: | The proposed model outperforms cultural transmission in a population of agents and takes into account learning biases of the learners. |