Papers by Qisheng Hu
From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models (2024.acl-long)
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| Challenge: | Prior work on timeline summarization has neglected the potential synergy between the two forms of timelines. |
| Approach: | They propose a timeline summarization approach that leverages large language models to generate both event and topic timelines. |
| Outcome: | The proposed approach outperforms the best existing approaches in four TLS benchmarks. |
Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance? (2025.naacl-long)
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| Challenge: | Fact-checking pipelines adopt the Decompose-Then-Verify paradigm, where texts are broken down into smaller claims for individual verification and subsequently combined for a veracity decision. |
| Approach: | They propose to categorize decomposition errors and to reveal a trade-off between accuracy gains and noise introduced by decomposing. |
| Outcome: | The proposed analysis provides new insights into understanding current system’s instability and offers guidance for future studies toward improving claim decomposition in fact-checking pipelines. |
InstructCoder: Instruction Tuning Large Language Models for Code Editing (2024.acl-srw)
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| Challenge: | InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. |
| Approach: | They propose to use Large Language Models to edit code based on user instructions . they use a dataset to adapt LLMs to general-purpose code editing . |
| Outcome: | The proposed model can significantly improve code editing performance compared to proprietary models . the proposed model is based on a human-written execution-based benchmark . |
Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification (2026.acl-long)
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| Challenge: | Existing methods for multi-hop claim verification require multi-step reasoning to construct verification chains while iterating for information to uncover hidden bridging facts. |
| Approach: | They propose a hierarchical agent reasoning and information search model that integrates reasoning and search-informed reasoning. |
| Outcome: | Experimental results show that HARIS improves multi-hop claim verification accuracy and interpretability. |
MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems (2024.findings-emnlp)
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| Challenge: | Programming often involves translating detailed and complex specifications into code . current state-of-the-art models struggle to solve these problems, a new study shows . |
| Approach: | They propose a multi-modal coding dataset to evaluate algorithmic problem-solving skills in visually rich contexts. |
| Outcome: | The proposed model lacks powerful vision-code models due to the extreme demand for reasoning abilities. |
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. |
STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignment (2025.emnlp-main)
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| Challenge: | Existing methods for incontext learning often overlook structural alignment, leading to poor generalization and suboptimal performance. |
| Approach: | They propose a two-stage exemplar selection strategy that achieves a strong balance between efficiency, generalizability and performance. |
| Outcome: | The proposed method outperforms baselines on semantic parsing tasks on four benchmarks. |