Papers by Chonghua Wang
Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks (2024.naacl-long)
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| Challenge: | Existing long-text evaluation benchmarks, such as L-Eval and LongBench, focus on QA and summarization tasks. |
| Approach: | They propose a length-adaptable benchmark for evaluating the long-context understanding of large language models. |
| Outcome: | The proposed benchmarks do not cover ultralong settings (100k+ tokens) and are difficult to evaluate across different length ranges. |
BotChat: Evaluating LLMs’ Capabilities of Having Multi-Turn Dialogues (2024.findings-naacl)
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Haodong Duan, Jueqi Wei, Chonghua Wang, Hongwei Liu, Yixiao Fang, Songyang Zhang, Dahua Lin, Kai Chen
| Challenge: | Modern Large Language Models (LLMs) facilitate high-quality, multi-turn dialogues with humans, but human-based evaluation of such a capability requires substantial manual effort. |
| Approach: | They propose to evaluate LLMs' ability to emulate human-like, multi-turn conversations using an LLM-centric approach. |
| Outcome: | The proposed model emulates human-like, multi-turn conversations using an LLM-centric approach. |
MOA: Multi-Objective Alignment for Role-Playing Agents (2026.acl-long)
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| Challenge: | Prior work on role-playing agents relies on supervised fine-tuning or reinforcement learning with scalarized rewards, but these approaches do not address the coordination of multiple reward dimensions during optimization. |
| Approach: | They propose a reinforcement-learning framework that enables multi-dimensional, fine-grained rubric optimization for general RPAs. |
| Outcome: | Experiments on PersonaGym and RoleMRC show that MOA improves multi-dimensional role-playing performance over supervised and standard RL baselines. |