Papers by Junfeng Zhan
Eval-RAR: Evaluation-Driven Retrieval-Augmented Reasoning via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing methods for retrieval-augmented generation fail to provide explicit supervision for internal reasoning process. |
| Approach: | They propose an Evaluation-driven Retrieval-Augmented Reasoning framework that uses reinforcement learning and a fine-grained evaluation reward to optimize the process. |
| Outcome: | Eval-RAR outperforms existing methods on QA benchmarks on seven single-hop and multi-hop tasks. |
StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation (2024.findings-acl)
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| Challenge: | Current evaluations for large language models use a single-item assessment paradigm . current evaluations struggle to discern whether a model possesses the required capabilities or merely memorizes/guesses the answers to specific questions. |
| Approach: | They propose a framework to evaluate large language models using atomic test objectives. |
| Outcome: | The proposed evaluation framework resists data contamination and reduces interference of potential biases, and sheds light on the design of future principled and trustworthy LLM evaluation protocols. |