247 citations · 316 across the 24 of their papers we have counts for
4 papers · 1 filter
LLatrieval: LLM-Verified Retrieval for Verifiable Generation
Xiaonan Li, Changtai Zhu, Linyang Li +3
Verifiable generation aims to let the large language model (LLM) generate text with supporting documents, which enables the user to flexibly verify the answer and makes the LLM's o…
Unified Demonstration Retriever for In-Context Learning
Xiaonan Li, Kai Lv, Hang Yan +6
In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction.…
MoT: Memory-of-Thought Enables ChatGPT to Self-Improve
Xiaonan Li, Xipeng Qiu
Large Language Models (LLMs) have shown impressive abilities in various tasks. However, fundamentally improving them depends on high-quality datasets or computationally expensive f…
Finding Support Examples for In-Context Learning
Xiaonan Li, Xipeng Qiu
Additionally, the strong dependency among in-context examples makes it an NP-hard combinatorial optimization problem and enumerating all permutations is infeasible. Hence we propos…