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20242026
most citedMAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

2 citations · 2 across the 6 of their papers we have counts for

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cs.CL2026

Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG

Haotian Zhou, Weiran Huang, Siqi Liu +3

Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved passages remain English. In…

cs.CL2026

SEAL: Synergistic Co-Evolution of Agents and Learning Environments

Yihao Hu, Zhihao Wen, Xiujin Liu +3

Large Language Model (LLM) agents are increasingly improved through interaction, yet most self-evolution methods adapt either the policy or the learning environment in isolation. W…

cs.CL2025

Few-shot LLM Synthetic Data with Distribution Matching

Jiyuan Ren, Zhaocheng Du, Zhihao Wen +4

As large language models (LLMs) advance, their ability to perform in-context learning and few-shot language generation has improved significantly. This has spurred using LLMs to pr…

cs.CL2024

Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models

Yuyan Chen, Qiang Fu, Yichen Yuan +6

Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawb…

cs.CL2024★ 2 cited

MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

Yuyan Chen, Zhihao Wen, Ge Fan +6

Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research prima…