Papers by Yushi Cao
LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA (2025.findings-acl)
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Jiajie Zhang, Yushi Bai, Xin Lv, Wanjun Gu, Danqing Liu, Minhao Zou, Shulin Cao, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li
| Challenge: | Current long-context large language models lack citations to support their responses, making verification difficult due to potential hallucinations. |
| Approach: | They propose to use off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations and leverage this pipeline to construct a large-scale SFT dataset for LQAC. |
| Outcome: | The proposed pipeline can generate responses with fine-grained citations on the fly, surpassing existing models including GPT-4o. |
Unveiling Project-Specific Bias in Neural Code Models (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) based neural code models struggle to generalize effectively to real-world inter-project out-of-distribution data. |
| Approach: | They propose a Cond-Idf measurement to measure the relatedness of a token with a label and its project-specificness. |
| Outcome: | The proposed framework improves both inter-project OOD generalization and adversarial robustness while not sacrificing accuracy on intra-project IID data. |
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks (2025.acl-long)
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Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
| Challenge: | Existing benchmarks on longcontext large language models fail to reflect their deep understanding capabilities across diverse tasks. |
| Approach: | They propose a benchmark to assess the ability of long-context large language models to handle long-text problems. |
| Outcome: | The proposed model achieves 50.1% accuracy when directly answering the questions . human experts achieve only 53.7% accuracy under a 15-minute time constraint . |
ReMedi: Reasoner for Medical Clinical Prediction (2026.findings-acl)
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| Challenge: | Existing approaches to predicting future clinical outcomes from EHRs focus on enhancing medical knowledge through distillation or RAG while relying on the model’s internal ability to interpret contextual information. |
| Approach: | They propose a framework for improving clinical outcome prediction from EHR using a sample regeneration mechanism that leverages ground-truth answers as hints to enhance reasoning. |
| Outcome: | Experiments on multiple EHR prediction tasks show significant gains of up to 19.9% over state-of-the-art baselines in terms of F1 score, underscoring ReMedi’s effectiveness in real-world clinical prediction. |
CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly used to judge code, but their reliability remains poorly understood. |
| Approach: | They propose a benchmark to evaluate Large Language Models as code judges . they find that small reasoning models outperform larger non-reasoning models . |
| Outcome: | The proposed benchmark evaluates LLM-as-a-Judge models across three coding tasks. |