Papers by Sergey Levine
Unfamiliar Finetuning Examples Control How Language Models Hallucinate (2025.naacl-long)
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| Challenge: | Large language models (LLMs) generate plausible-sounding responses that are factually incorrect. |
| Approach: | They propose an approach to learn more reliable reward models by modifying how unfamiliar finetuning examples are supervised to influence model responses to unfamiliar queries. |
| Outcome: | The proposed approach improves the efficacy of RL factuality finetuning in long-form biography and book/movie plot generation tasks. |
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems (2022.findings-naacl)
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| Challenge: | Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control. |
| Approach: | They propose a method to fine-tune language models in a goal-aware way . they evaluate a flight-booking method with a context-assisted language model . |
| Outcome: | The proposed method outperforms the state-of-the-art method on a flight-booking task by 7% in terms of task success. |
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning (2022.naacl-main)
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| Challenge: | Existing approaches to training dialogue agents are supervised learning, but this is prohibitively expensive and time-consuming. |
| Approach: | They propose offline reinforcement learning methods that can be used to train dialogue agents . offline reinforcement learn methods can be combined with language models to yield realistic dialogue agents. |
| Outcome: | The proposed method can be combined with language models to produce realistic dialogue agents . the results show that the offline method can achieve the goal of the proposed system . |
Learning with Latent Language (N18-1)
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| Challenge: | Using the space of natural language strings as a parameter space is an effective way to capture natural task structure. |
| Approach: | They propose to use natural language as a parameter space for few-shot learning problems including classification, transduction and policy search. |
| Outcome: | The proposed model outperforms models with a linguistic parameterization on image classification, text editing, and reinforcement learning. |