Papers by Jiazheng Xu
Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring (2024.findings-emnlp)
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| Challenge: | Existing methods for generating rationales that justify scoring decisions are not accurate and often contain hallucinated information. |
| Approach: | They propose a framework capable of generating more faithful rationales and matching performance with classifier-based scoring systems. |
| Outcome: | The proposed framework achieves 38% improvement in QWK score compared to prior work . it can be used to match performance with classifier-based scoring systems . |
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives (2024.findings-acl)
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| Challenge: | Existing datasets for narrative understanding fail to represent complexity and uncertainty of relationships in real-life social scenarios. |
| Approach: | They propose a benchmark for extracting and analysing intricate character relation graphs from detective narratives using large-scale large-language models. |
| Outcome: | The proposed dataset extracts and analyses character relation graphs from detective narratives using advanced Large Language Models like GPT-3.5, GPT-4, and Llama2 . |
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks (2025.acl-long)
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Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
| Challenge: | Existing benchmarks on longcontext large language models fail to reflect their deep understanding capabilities across diverse tasks. |
| Approach: | They propose a benchmark to assess the ability of long-context large language models to handle long-text problems. |
| Outcome: | The proposed model achieves 50.1% accuracy when directly answering the questions . human experts achieve only 53.7% accuracy under a 15-minute time constraint . |
EnigmaToM: Improve LLMs’ Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States (2025.findings-acl)
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| Challenge: | Existing ToM reasoning methods rely excessively on off-the-shelf LLMs, reducing their efficiency and limiting their applicability to high-order ToM. |
| Approach: | They propose a neuro-symbolic framework that integrates a Neural Knowledge Base of Entity States and knowledge injection to enhance ToM reasoning. |
| Outcome: | The proposed framework improves ToM reasoning on ToMi, HiToM, and FANToM benchmarks. |
SALT: Step-level Advantage Assignment for Long-horizon Agents via Trajectory Graph (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities, but their application to complex, multi-step, and long-horizon tasks remains challenging. |
| Approach: | They propose a framework that provides a finer-grained advantage assignment derived solely from outcome rewards. |
| Outcome: | The proposed framework provides a finer-grained advantage assignment, derived solely from outcome rewards. |
AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models (2025.acl-long)
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| Challenge: | Existing benchmarks focus on basic abilities using nonverbal methods, such as yes-no and multiple-choice questions. |
| Approach: | They propose a benchmark that provides more nuanced evaluations of alignment capabilities for large Vision-Language Models (VLMs) they use a rule-calibrated evaluator that exceeds GPT-4's evaluation ability and a “alignment score” to assess the robustness and stability of models across diverse prompts. |
| Outcome: | The proposed benchmark covers 13 tasks across three categories and includes both single-turn and multi-turn dialogue scenarios. |