5 citations · 10 across the 6 of their papers we have counts for
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cs.CL2025★ 1 cited
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following
Yun He, Wenzhe Li, Hejia Zhang +22
Recent progress in large language models (LLMs) has led to impressive performance on a range of tasks, yet advanced instruction following (IF)-especially for complex, multi-turn, a…
cs.CL2024★ 5 cited
Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
Yun He, Di Jin, Chaoqi Wang +16
Large Language Models (LLMs) have demonstrated impressive capabilities in various tasks, including instruction following, which is crucial for aligning model outputs with user expe…
cs.CL2024★ 1 cited
Improving Model Factuality with Fine-grained Critique-based Evaluator
Yiqing Xie, Wenxuan Zhou, Pradyot Prakash +9
Factuality evaluation aims to detect factual errors produced by language models (LMs) and hence guide the development of more factual models. Towards this goal, we train a factuali…