Papers by Albert No
SEPS: A Separability Measure for Robust Unlearning in LLMs (2025.emnlp-main)
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| Challenge: | Existing unlearning metrics assess whether a model correctly answers retain queries and rejects forget queries, but they fail to capture real-world scenarios where forget queries rarely appear in isolation. |
| Approach: | They propose an evaluation framework that explicitly measures a model’s ability to both forget and retain information within a single prompt. |
| Outcome: | The proposed approach significantly improves unlearning effectiveness, demonstrating robustness even in complex settings with up to eight mixed forget and retain queries in a single prompt. |
Assigning Distinct Roles to Quantized and Low-Rank Matrices Toward Optimal Weight Decomposition (2025.findings-acl)
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| Challenge: | Existing joint optimization methods prioritize one component at the expense of the other, resulting in suboptimal decompositions that fail to leverage each component’s unique strengths. |
| Approach: | They introduce Outlier-Driven Low-Rank Initialization (ODLRI) which assigns low-rank components the specific role of capturing activation-sensitive weights. |
| Outcome: | Experiments on Llama2 (7B, 13B, 70B, and Mistral-7B) and Llma3-8B show that incorporating ODLRI into the joint optimization framework reduces activation-aware error, minimizes quantization scale, and improves perplexity and zero-shot accuracy in low-bit settings. |
R-TOFU: Unlearning in Large Reasoning Models (2025.emnlp-main)
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| Challenge: | Large Reasoning Models embed private or copyrighted information in their final answers and throughout multi-step chain-of-thought (CoT) traces. |
| Approach: | They propose a benchmark for Large Reasoning Models that augments existing unlearning tasks with realistic CoT annotations and step-wise metrics that expose residual knowledge invisible to answer-level checks. |
| Outcome: | The proposed benchmark shows that answer-only objectives leave substantial forget traces in reasoning. |
DUSK: Do Not Unlearn Shared Knowledge (2026.findings-acl)
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| Challenge: | Recent work suggests that machine learning models are indistinguishable from models trained on retain sets. |
| Approach: | They propose a benchmark to evaluate machine unlearning under realistic knowledge overlap . they construct documents containing both shared and unique knowledge . |
| Outcome: | The proposed model is indistinguishable from a model retrained on the retain set while only forget-specific content is removed. |