Papers by Jinghao Lin
RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback (2025.coling-main)
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| Challenge: | Existing methods to mitigate hallucinations generate erroneous or fabricated information. |
| Approach: | They propose a rank-response-based model that annotates pair-reponses and trains alignment algorithms to improve the correspondence between images and text. |
| Outcome: | The proposed model outperforms the DPO method and outperfies existing methods on two MLLMs of different sizes and four widely used benchmarks. |
DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference (2026.findings-acl)
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Zihan Wang, Hao Wang, Shi Feng, Xiaocui Yang, Daling Wang, Yiqun Zhang, Jinghao Lin, Xiaozhong Ji, Haihua Yang
| Challenge: | Medical reasoning models are constrained by parametric knowledge and can induce hallucinations and spurious attributions. |
| Approach: | They propose a model that uses a multi-hop med-search QA synthesis method to apply the DR paradigm in medical contexts. |
| Outcome: | The proposed model outperforms larger medical reasoning models on medical benchmarks. |
SALMON: A Structure-Aware Language Model with logicality and densification strategy for Temporal Knowledge Graph Reasoning (2024.findings-emnlp)
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| Challenge: | Temporal knowledge graph reasoning (TKGR) is a crucial task that involves reasoning at known timestamps to complete the future facts. |
| Approach: | They propose a temporal knowledge graph reasoning model with logicality and densification strategy that captures temporal evolving pattern and structural information in TKGs. |
| Outcome: | The proposed model outperforms the state-of-the-art models and is based on a structure-aware language model with logicality and densification strategy. |
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms (2026.findings-acl)
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Jinghao Luo, Yuchen Tian, Chuxue Cao, Ziyang Luo, Hongzhan Lin, Kaixin Li, Chuyi Kong, Ruichao Yang, Jing Ma
| Challenge: | Large Language Models (LLMs)-based agents have fundamentally reshaped artificial intelligence . however, the inherent statelessness of LLMs hinders their ability to maintain logical consistency across complex, multi-step tasks . |
| Approach: | They propose a framework for LLM agent memory mechanisms that formalizes the development process into three stages: storage, reflection, and experience. |
| Outcome: | The proposed framework breaks the development process into three stages . it analyzes the need for long-range consistency, challenges in dynamic environments, and the ultimate goal of continual learning. |