Papers by Jizhi Zhang
K-order Ranking Preference Optimization for Large Language Models (2025.findings-acl)
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| Challenge: | Existing list-wise methods focus on optimizing list ranking consistency for LLMs to improve ranking abilities. |
| Approach: | They propose to extend the Plackett-Luce model to accommodate top-K ranking by extending the DPO’s Plact-Lucer model to dynamically determine appropriate K for different samples. |
| Outcome: | The proposed model can be extended to accommodate top-K ranking and improve training efficiency. |
GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation (2024.emnlp-main)
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| Challenge: | Existing datasets are too challenging for direct model learning or suffer from misalignment between text and images. |
| Approach: | They propose a pipeline that leverages GPT-4 and GPT4V to generate geometry problems with aligned text and images, facilitating model learning. |
| Outcome: | The proposed pipeline generates 4.9K geometry problems with aligned text and images, facilitating model learning. |
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue in Recommendations for Large Language Models (2024.emnlp-main)
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| Challenge: | Existing approaches to adapt Large Language Models (LLMs) for recommendation encounter significant challenges such as amplification bias and homogeneity. |
| Approach: | They propose a new decoding approach called Debiasing-Diversifying Decoding (D3) that disables length normalization for ghost tokens to alleviate amplification bias and incorporates a text-free assistant model to encourage tokens less frequently generated by LLMs for counteracting recommendation homogeneity. |
| Outcome: | Extensive experiments on real-world datasets demonstrate the proposed approach’s effectiveness in enhancing accuracy and diversity. |
Leveraging Unpaired Feedback for Long-Term LLM-based Recommendation Tuning (2025.findings-emnlp)
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| Challenge: | a recent study highlights unpaired feedback as a key challenge for long-term LLM-based recommenders . unpaired user feedback is crucial for improving LLMs in dynamic user environments, authors say . |
| Approach: | They propose a framework that incorporates unpaired feedback into LLMs to improve long-term recommendation performance. |
| Outcome: | The proposed framework improves long-term recommendation performance by incorporating unpaired feedback without requiring paired supervision. |
Customizing In-context Learning for Dynamic Interest Adaption in LLM-based Recommendation (2025.findings-acl)
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| Challenge: | Existing Large Language Model (LLM)-based recommender systems face challenges to adapt to dynamic user interests without any model-level updates. |
| Approach: | They propose a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats. |
| Outcome: | The proposed model adapts to dynamic user interests without model updates without any model updates and is available online at https://anonymous.4open.science/r/RecICL-8003. |
Robust Prompt Optimization for Large Language Models Against Distribution Shifts (2023.emnlp-main)
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| Challenge: | Existing research has explored automatic prompt optimization methods to eliminate manual effort in identifying effective prompts for a given task. |
| Approach: | They propose a framework for prompt optimization that can be generalized to an unlabeled target group. |
| Outcome: | The proposed framework improves on target group and source group while generalizing to unlabeled target group. |
Empowering Language Understanding with Counterfactual Reasoning (2021.findings-acl)
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| Challenge: | Existing methods for language understanding use the recognized patterns in the testing phase that are inherently different from us humans who have counterfactual thinking. |
| Approach: | They propose a counterfactual Reasoning Model which mimics counterfactive thinking by learning from few counterffact samples. |
| Outcome: | The proposed model can detect and make predictions from textual patterns . it can also detect negative sarcastic puns by comparing them with imaginations . |