Papers by Jiuyong Li
Logit Space Constrained Fine-Tuning for Mitigating Hallucinations in LLM-Based Recommender Systems (2025.emnlp-main)
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| Challenge: | Existing LLM-based recommender systems rely on standard fine-tuning methodologies, often ignoring hallucination issues during the fine-uning process. |
| Approach: | They propose a logit space constraint-based fine-tuning framework to mitigate hallucination in LLM-based recommenders by incorporating Kullback–Leibler divergence into the training objective. |
| Outcome: | Experiments on two recommendation models with distinct LLM backbones and four real-world datasets show that LCFT reduces hallucination and enhances recommendation performance. |
Trustworthy and Explainable Causal Representation Learning in Transformers (2026.findings-acl)
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| Challenge: | Existing approaches to interpretable representation learning rely on masks that weight the significance of input features, but the origin of these masks is uncertain. |
| Approach: | They propose a causal framework that directly learns identifiable representations from attention weights rather than relying on importance masks. |
| Outcome: | The proposed framework learns identifiable and explainable representations from attention weights, rather than masks, and guarantees faithfulness on real-world datasets. |