Papers by Chenjun Xiao

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Challenge:	Large language models (LLMs) have been used to sequential decision-making tasks like multi-armed bandits where an LLM is tasked with selecting arms in each iteration is often suboptimal.
Approach:	They propose to combine MAB and LLMs to leverage the in-context learning capability of LLM for reward prediction.
Outcome:	The proposed approach outperforms LLM-based direct arm selection on synthetic tasks where only preference feedback between arm pairs is available.