Papers by Wenjing Zhu
How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning (2023.emnlp-main)
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| Challenge: | Existing models excel at capturing semantic correlations within utterance embeddings but fail to determine specific causal relationships. |
| Approach: | They propose to incorporate i.i.d. noise terms into conversation process to build a structural causal model . they propose to use unstructured conversation data to facilitate deep learning . |
| Outcome: | The proposed approach can be implemented in unstructured conversation data and a synthetic dataset that includes i.i.d. noise. |
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs (2025.findings-emnlp)
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| Challenge: | Gradient-based data influence approximation is not feasible in practice. |
| Approach: | They propose a gradient-based data selection framework with clustering and a modified Upper Confidence Bound algorithm to solve this problem. |
| Outcome: | The proposed framework can achieve comparable results to the original gradient-based data selection methods while reducing computational consumption. |
CFBench: A Comprehensive Constraints-Following Benchmark for LLMs (2025.acl-long)
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Tao Zhang, ChengLIn Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Tao Zhang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou
| Challenge: | Existing evaluations of Large Language Models (LLMs) focus on fragmented constraints or narrow scenarios, but they overlook the comprehensiveness and authenticity of constraints from the user’s perspective. |
| Approach: | They propose a Chinese Comprehensive Constraints Following Benchmark for LLMs that compiles constraints from real-world instructions and constructs a systematic framework for constraint types. |
| Outcome: | The proposed framework integrates multi-dimensional assessment criteria with requirement prioritization, covering various perspectives of constraints, instructions, and requirement fulfillment. |
From Awareness to Adaptability: Enhancing Tool Utilization for Scientific Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches enhance reasoning through Chain-of-Thought, Program-ofThough, and Tool-Integration. |
| Approach: | They propose a tool-awareness training method that leverages both forward and backward data generation strategies to strengthen the model’s conscious and selective tool utilization in multi-step reasoning tasks. |
| Outcome: | The proposed method improves the model's tool utilization capabilities, including proactivity and execution success rates. |