Summary of Intrinsic Evaluation Of Unlearning Using Parametric Knowledge Traces, by Yihuai Hong et al.
Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces
by Yihuai Hong, Lei Yu, Haiqin Yang, Shauli Ravfogel, Mor Geva
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper focuses on the task of “unlearning” in large language models (LLMs), aiming to mitigate undesirable behaviors like generating harmful or private information. Current evaluations rely on behavioral tests, neglecting residual knowledge within model parameters. We propose a general evaluation methodology that uses vocabulary projections to inspect concept vectors and construct ConceptVectors, a benchmark dataset containing common concepts’ parametric knowledge traces in two open-source LLMs. Evaluation shows existing unlearning methods minimally impact concept vectors, mostly suppressing them during inference, while ablating these vectors removes associated knowledge and reduces the model’s susceptibility to adversarial manipulation. The results highlight limitations of behavioral-based evaluations and call for parameter-based evaluations. We release our code and benchmark at this https URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure big language models don’t keep learning bad things. Right now, people are testing these models by seeing what they do when given a task. But that’s not enough – the model might still know the bad information it learned earlier. The researchers propose a new way to test these models by looking at what’s actually stored in their “memory”. They create a special dataset called ConceptVectors, which helps them figure out if the model is really forgetting the bad stuff or just hiding it. Their results show that most methods aren’t very good at making the model truly forget bad information. This means we need to come up with new ways to test these models and make sure they’re not secretly learning things we don’t want. |
Keywords
* Artificial intelligence * Inference
