Papers by Yihuai Hong
Intrinsic Test of Unlearning Using Parametric Knowledge Traces (2025.emnlp-main)
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| Challenge: | Existing methods for “unlearning” information captured in large language models rely on behavioral tests without monitoring residual knowledge in model parameters. |
| Approach: | They propose a general evaluation methodology that uses vocabulary projections to inspect concepts encoded in model parameters. |
| Outcome: | The proposed method detects changes in parametric traces of unlearned concepts and localizes them in two open-source LLMs. |
The Reasoning-Memorization Interplay in Language Models Is Mediated by a Single Direction (2025.findings-acl)
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| Challenge: | Large language models excel on a variety of reasoning benchmarks, but struggle to generalize to unseen questions due to over-reliance on memorized training examples. |
| Approach: | They propose to identify a set of linear features in the model’s residual stream that govern the balance between genuine reasoning and memory recall. |
| Outcome: | The proposed model can be manipulated to activate the most relevant problem-solving capabilities during answer generation. |
Interpretability-based Tailored Knowledge Editing in Transformers (2024.emnlp-main)
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| Challenge: | Existing methods for modifying in-context learning fail to analyze the instability of in-constitu learning outcomes. |
| Approach: | They propose a model-based knowledge editing method that considers the unique information flow of each sample and aims to correct errors without costly retraining. |
| Outcome: | The proposed method exploits the critical role of feed-forward MLPs in decoder-only models and reveals diverse attribute recall across transformer layers, guiding edits to specific features at different depths and mitigating over-editing issues. |
Dissecting Fine-Tuning Unlearning in Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods for fine-tuning-based unlearning are ineffective at completely erasing model-embedded knowledge, but their true effectiveness remains unclear. |
| Approach: | They propose to use activation patching and parameter restoration experiments to examine the limitations of fine-tuning-based unlearning methods for erasing harmful, sensitive, or copyrighted information within large language models. |
| Outcome: | The proposed methods alter the model’s knowledge retrieval process rather than genuinely erasing the problematic knowledge embedded in the model parameters. |
Precise In-Parameter Concept Erasure in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods for erasing concepts from model parameters are either too coarse, shallow or ineffective. |
| Approach: | They propose a framework for precisely erasing entire concepts from model parameters by directly editing directions that encode them in parameter space. |
| Outcome: | The proposed framework reduces errors on the target concept to as low as 7.7% while improving specificity and robustness. |