Papers by Jingchi Jiang
RLKGF: Reinforcement Learning from Knowledge Graph Feedback Without Human Annotations (2025.findings-acl)
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| Challenge: | Lack of human preference labels remains a significant bottleneck when applying RLHF to a downstream domain. |
| Approach: | They propose a method that leverages human priors encoded in Knowledge Graphs (KGs) to derive RL rewards in the absence of manual annotations. |
| Outcome: | Experiments on three public and one private medical dialogue datasets show that the proposed method outperforms the competitive RLAIF in improving LLM diagnostic accuracy. |
HiEdit: Lifelong Model Editing with Hierarchical Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing approaches to lifelong model editing apply parameter perturbations to static and dense layers for all instances. |
| Approach: | They propose a hierarchical reinforcement learning framework that identifies the most knowledge-relevant layers for each editing instance. |
| Outcome: | The proposed framework boosts the performance of the competitive RLEdit by 8.48% with perturbing only half of the layers per edit. |
Agri-CM3: A Chinese Massive Multi-modal, Multi-level Benchmark for Agricultural Understanding and Reasoning (2025.acl-long)
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| Challenge: | Existing benchmarks lack comprehensive evaluations, particularly in multi-level reasoning, making it difficult to identify model limitations. |
| Approach: | They propose to use Agri-CM3 to assess multi-level reasoning in agricultural management by integrating multiple data modalities. |
| Outcome: | The Agri-CM3 benchmark includes 3,939 images and 15,901 multi-level multiple-choice questions with detailed explanations. |
KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling (2025.emnlp-main)
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| Challenge: | Existing approaches to multi-hop question answering focus on generating simple questions and neglecting the integration of essential knowledge, such as relevant sentences within documents. |
| Approach: | They propose a framework to expand the diversity of generated multi-hop questions by sampling varied knowledge compositions within a given context. |
| Outcome: | The proposed framework improves the overall accuracy of knowledge composition selection by 3.9% on hotpotQA and 2WikiMultihopQA datasets. |
Feasible is Not Enough: Cost-Aware Optimal Tool-Chain Planning on Multi-Solution Tool Graphs (2026.findings-acl)
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| Challenge: | Existing tools and benchmarks often form tool learning (TL) as a single-solution setting . exploring large-scale TG is computationally expensive, especially under constrained context budgets. |
| Approach: | They propose a framework for learning optimal TL policies over large tool graphs . they train a reinforcement learning agent to acquire transferable expansion skills . |
| Outcome: | The proposed framework improves task success and solution optimality by 46.21% and 66.34% on multiSoTLBench. |