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JG

Jinjie Gu

ACL 2026×6AAAI 2026×2ICLR 2026×2KDD 2026SIGIR 2026效率与系统评测与基准推理强化学习与后训练社会与人本
新星分资深
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核心/参与44
方向广度19
合作网络12
0 一作 · 12 篇 · ACL 2026 / AAAI 2026 / ICLR 2026 / KDD 2026 / SIGIR 2026

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2026 顶会论文

12
作者位次 #12/12ACL 2026Poster· Natural Language Processing

W eb C lipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning

Junjie Wang · Zequn Xie · Dan Yang · Jie Feng · YUE SHEN · Duolin Sun · Meixiu Long · Yihan Jiao · Zhehao Tan · Jian Wang · Peng Wei · Jinjie Gu

摘要

Deep Research systems based on web agents have shown strong potential in solving complex information-seeking tasks, yet their search efficiency remains underexplored. We observe that many state-of-the-art open-source web agents rely on long tool-call trajectories with cyclic reasoning loops and e...

效率与系统智能体与工具推理OpenReview 项目页
作者位次 #7/8ACL 2026Poster· Natural Language Processing

Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training

Lei Liu · Hao Zhu · Xiaoyan Yang · YUE SHEN · Zhixuan Chu · Jian Wang · Jinjie Gu · Kui Ren

摘要

Continual Pre-training (CPT) serves as a fundamental approach for adapting foundation models to domain-specific applications. Scaling laws for pre-training define a power-law relationship between dataset size and the test loss of an LLM. However, the marginal gains from simply increasing data for...

数据与预训练效率与系统评测与基准OpenReview 项目页
作者位次 #8/9ACL 2026Poster· ACL Industry Track

HAD : HA llucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy

Fan Xu · Xinyu Hu · Zhenghan Yu · Li Lin · Xu Zhang · Yang Zhang · Wei Zhou · Jinjie Gu · Xiaojun Wan

摘要

The increasing reliance on natural language generation (NLG) models, particularly large language models, has raised concerns about the reliability and accuracy of their outputs. A key challenge is hallucination, where models produce plausible but incorrect information. As a result, hallucination...

数据与预训练安全效率与系统OpenReview 项目页
作者位次 #7/9ACL 2026Findings· Findings of ACL

C lin A lign: Scaling Healthcare Alignment from Clinician Preference

Shiwei Lyu · Xidong Wang · Hao Zhu · Lei Liu · Chaohe Zhang · Jian Wang · Jinjie Gu · Benyou Wang · YUE SHEN

摘要

Although large language models (LLMs) demonstrate expert-level medical knowledge, aligning their open-ended outputs with fine-grained clinician preferences remains challenging. Existing methods often rely on coarse objectives or unreliable automated judges that are weakly grounded in professional...

社会与人本对齐与偏好效率与系统OpenReview 项目页
作者位次 #9/11ACL 2026Findings· Findings of ACL

B alance SFT : Improving LLM Function Calling with Balanced Training Signals and Data Hardness

Bingguang Hao · Zengzhuang Xu · Maolin WANG · Yuntao Wen · Yicheng Chen · Cunyin Peng · Long Chen · an Jiaotong University) · Jinjie Gu · Chenyi Zhuang · Ji Zhang

摘要

While Supervised Fine-Tuning (SFT) is the prevailing method for equipping Large Language Models (LLMs) with function calling capabilities, its effectiveness is often compromised by two critical challenges: 1) Imbalanced Training Signals , where lengthy Chain-of-Thought (CoT) reasoning tokens domi...

推理强化学习与后训练数据与预训练OpenReview 项目页
作者位次 #15/15ACL 2026Findings· Findings of ACL

G roup R ank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLM s

Meixiu Long · Duolin Sun · Dan Yang · Yihan Jiao · Lei Liu · Jiahai Wang · Binbin Hu · YUE SHEN · Jie Feng · Zhehao Tan · Junjie Wang · Lianzhen Zhong · Jian Wang · Peng Wei · Jinjie Gu

摘要

Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasoning capabilities to address the limitations of conventional models on complex queries. However, current LLM-based reranking paradigms are fundamentally const...

效率与系统强化学习与后训练推理OpenReview 项目页
作者位次 #5/6SIGIR 2026Poster· Information Retrieval (Industry)

MedNQS: Medical State-Aware Dual-Stage Next-Turn Question Suggestion for Online Medical Consultations

Yue He · Dongsheng Bi · Minghui Yang · Jian Wang · Jinjie Gu · Junwei Liu

摘要

High-quality follow-up questions are essential for efficient online medical consultations, yet they are difficult for non-professional users to ask. Next-turn Question Suggestion (NQS) addresses this by recommending candidate questions to guide multi-turn interactions. In production, NQS faces tw...

对齐与偏好效率与系统评测与基准OpenReview
作者位次 #26/27KDD 2026Oral· Knowledge Discovery & Data Mining

MLB: A Scenario-Driven Benchmark for Evaluating Large Language Models in Clinical Applications

Qing He · Dongsheng Bi · Jianrong Lu · Minghui Yang · Zixiao Chen · Jiacheng Lu · Jing Chen · Nannan Du · Xiao Cui · Sijing Wu · Peng Xiang · Yingying Hu · Yi Guo · Chunpu Li · Shaoyang Li · Zhuo Dong · Ming Jiang · Shuai Guo · Liyun Feng · Jin Peng · Zhou Yang · Han Ying · Jie Zheng · Yujie Yang · Jian Wang · Jinjie Gu · Junwei Liu

评测与基准检索与知识社会与人本OpenReview

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