Papers by Jianpeng Hu
Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph (2026.findings-acl)
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| Challenge: | Existing methods for detecting faithfulness hallucinations are coarse or do not capture the models’ internal reasoning processes, making it difficult to learn. |
| Approach: | They propose a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination using Large language models. |
| Outcome: | The proposed method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k. |
Continual Few-shot Relation Extraction via Adaptive Gradient Correction and Knowledge Decomposition (2024.findings-acl)
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| Challenge: | Existing methods to learn new relations with limited samples neglect the instability of embeddings in the process of different task training, which leads to catastrophic forgetting. |
| Approach: | They propose a method to analyze catastrophic forgetting by limiting embedding instability . they propose to decompose knowledge into general and task-related knowledge . |
| Outcome: | The proposed method outperforms the state-of-the-art model and improves the following degree of embeddings. |
Policy-Guided Stepwise Action Planning for Controllable LLM Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to steering large language model reasoning via high-level reasoning actions fail to outperform standard generation because planners tend to degenerate into repetitive loops or fixed patterns. |
| Approach: | They propose a planner-executor framework that learns to select reasoning actions dynamically while keeping the executor LLM fully frozen. |
| Outcome: | The proposed framework outperforms existing paradigms by preserving the executor LLM frozen . PG-HAP improves accuracy over strong baselines while producing less redundant, more adaptive trajectories. |
Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction (2025.findings-naacl)
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| Challenge: | Existing methods for document-level multi-event extraction neglect the fine-grained difference between events in multi-documents, which leads to event confusion and missing. |
| Approach: | They propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library. |
| Outcome: | The proposed method outperforms the state-of-the-art method in the recall of multi-events. |