Efficient Sequential Decision Making with Large Language Models (2024.emnlp-main)
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| Challenge: | Existing approaches to retrain or finetune large language models (LLMs) for decision making suffer from computational burden of gradient updates. |
| Approach: | They propose a model selection algorithm that leverages online model selection algorithms to efficiently incorporate LLMs agents into sequential decision making. |
| Outcome: | The proposed approach outperforms both traditional decision making algorithms and vanilla LLM agents on a large-scale Amazon dataset. |
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