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- W4380558503 abstract "Large language models (LLMs) offer significant promise as a knowledge source for task learning. Prompt engineering has been shown to be effective for eliciting knowledge from an LLM, but alone it is insufficient for acquiring relevant, situationally grounded knowledge for an embodied agent learning novel tasks. We describe a cognitive-agent approach that extends and complements prompt engineering, mitigating its limitations and thus enabling an agent to acquire new task knowledge matched to its native language capabilities, embodiment, environment, and user preferences. The approach is to increase the response space of LLMs and deploy general strategies, embedded within the autonomous agent, to evaluate, repair, and select among candidate responses produced by the LLM. We describe the approach and experiments that show how an agent, by retrieving and evaluating a breadth of responses from the LLM, can achieve 77-94% task completion in one-shot learning without user oversight. The approach achieves 100% task completion when human oversight (such as an indication of preference) is provided. Further, the type of oversight largely shifts from explicit, natural language instruction to simple confirmation/discomfirmation of high-quality responses that have been vetted by the agent before presentation to a user." @default.
- W4380558503 created "2023-06-14" @default.
- W4380558503 creator A5092160847 @default.
- W4380558503 date "2023-06-11" @default.
- W4380558503 modified "2023-09-25" @default.
- W4380558503 title "Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis" @default.
- W4380558503 doi "https://doi.org/10.48550/arxiv.2306.06770" @default.
- W4380558503 hasPublicationYear "2023" @default.
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