Papers by Jiangning Zhang
Optimizing Entity Resolution in Voice Interfaces: An ASR-Aware Entity Reference Expansion Approach (2024.emnlp-industry)
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| Challenge: | Automatic Speech Recognition (ASR) errors in voice-based dialog systems pose significant impediments to downstream tasks. |
| Approach: | They propose an automatic speech recognition (ASR) error-aware loss function to inject failed mentions and resolved entity names into the knowledge graph to enhance its awareness of unresolved mentions. |
| Outcome: | The proposed system enhances the knowledge graph's awareness of unresolved mentions by injecting pairs of failed mentions and resolved entities into the knowledge map. |
Disco-RAG: Discourse-Aware Retrieval-Augmented Generation (2026.acl-long)
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Dongqi Liu, Hang Ding, Qiming Feng, Xurong Xie, Zhucun Xue, Chengjie Wang, Jian Li, Jiangning Zhang, Yabiao Wang
| Challenge: | Existing RAG strategies treat retrieved passages in a flat and unstructured way, which prevents the model from capturing structural cues and constrains its ability to synthesize knowledge from dispersed evidence across documents. |
| Approach: | They propose a framework that explicitly injects discourse signals into the generation process. |
| Outcome: | Experiments on question answering and long-document summarization benchmarks show the efficacy of the proposed framework. |
Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies focus on prompt engineering or framework scheduling of one/multiple LLMs. |
| Approach: | They propose to integrate LLMs as agents into their training corpus by decomposition and redesigning the training corpu . they propose to use LLM-FLAN to effectively fine-tune LANguage models for Agents by reducing hallucinations. |
| Outcome: | The proposed model outperforms prior best models by 3.5% across agent evaluation datasets. |
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step (2024.acl-long)
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Zehui Chen, Weihua Du, Wenwei Zhang, Kuikun Liu, Jiangning Liu, Miao Zheng, Jingming Zhuo, Songyang Zhang, Dahua Lin, Kai Chen, Feng Zhao
| Challenge: | Existing studies evaluate the tool utilization ability of large language models based on the final output or only consider the single-step tool calling. |
| Approach: | They propose a new approach to evaluate the tool utilization capability of large language models (LLMs) they decompose the tool usage into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. |
| Outcome: | The proposed model exhibits consistency with the outcome-oriented evaluation and provides a more fine-grained analysis of the capabilities of LLMs. |