Papers by Yanhong Li
Forget for Get: A Lightweight Two-phase Gradient Method for Knowledge Editing in Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing methodologies often encounter parameter conflict during knowledge overwriting and excessive computational overhead. |
| Approach: | They propose a method that erases outdated knowledge and inserts new knowledge at the location that corresponds to the target knowledge. |
| Outcome: | The proposed method achieves more effective knowledge editing at a lower cost compared to previous methods across various base models. |
Context-Efficient Retrieval with Factual Decomposition (2025.naacl-short)
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| Challenge: | Existing models that use dynamically expanding text can be incorporated into large language models. |
| Approach: | They show that pre-processing external corpus into semi-structured "atomic facts" reduces the size of the context and improves inference efficiency. |
| Outcome: | The proposed form of atomic facts improves on question answering tasks when the amount of retrieved text is limited. |
How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise Gradients (2026.acl-long)
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| Challenge: | Spectral properties of low/high-quality instruction and reasoning data are used to explain finetuning dynamics in large language models. |
| Approach: | They propose to analyze layer-wise gradients induced by low/high-quality instruction and reasoning data for LLM post-training. |
| Outcome: | The results show that higher-quality data are associated with lower nuclear norms and higher effective ranks. |
Text or Pixels? Evaluating Efficiency and Understanding of LLMs with Visual Text Inputs (2025.findings-emnlp)
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| Challenge: | *visual text representations* are a practical and surprisingly effective form of input compression for decoder LLMs. |
| Approach: | They exploit visual representations to render long text inputs as a single image and provide it directly to the model. |
| Outcome: | The proposed method reduces token usage while preserving performance. |
What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective (2025.acl-long)
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| Challenge: | Xu et al., 2024) study shows that slow thinking can distinguish correct and irrelevant reasoning paths. |
| Approach: | They investigate how fast vs. slow thinking affects layer-wise gradients in large language models . they find that slow thinking can distinguish correct and irrelevant reasoning paths . |
| Outcome: | The results show that slow thinking can distinguish correct and irrelevant reasoning paths. |
When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models (2024.findings-naacl)
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| Challenge: | Recent studies suggest that self-reflective prompting can significantly enhance the reasoning capabilities of Large Language Models (LLMs). |
| Approach: | They propose guidelines for when to implement self-reflection in Large Language Models. |
| Outcome: | The proposed approach improves the reasoning capabilities of Large Language Models under a more stringent evaluation setting, and reduces tendency toward majority voting. |