MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts

• URL: http://arxiv.org/abs/2409.11844v1
• Date: Wed, 18 Sep 2024 09:55:48 GMT
• Title: MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts
• Authors: Tianle Gu, Kexin Huang, Ruilin Luo, Yuanqi Yao, Yujiu Yang, Yan Teng, Yingchun Wang,
• Abstract summary: Large Language Models (LLMs) can memorize sensitive information, raising concerns about potential misuse. Previous practices face three key challenges: Utility, efficiency, and robustness. We propose MEOW, a gradient descent-based unlearning method.
• Score: 29.593170782882563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) can memorize sensitive information, raising concerns about potential misuse. LLM Unlearning, a post-hoc approach to remove this information from trained LLMs, offers a promising solution to mitigate these risks. However, previous practices face three key challenges: 1. Utility: successful unlearning often causes catastrophic collapse on unrelated tasks. 2. Efficiency: many methods either involve adding similarly sized models, which slows down unlearning or inference, or require retain data that are difficult to obtain. 3. Robustness: even effective methods may still leak data via extraction techniques. To address these challenges, we propose MEOW, a simple yet effective gradient descent-based unlearning method. Specifically, we use an offline LLM to generate a set of inverted facts. Then, we design a new metric, MEMO, to quantify memorization in LLMs. Finally, based on the signals provided by MEMO, we select the most appropriate set of inverted facts and finetune the model based on them. We evaluate MEOW on the commonly used unlearn benchmark, ToFU, with Llama2-7B-Chat and Phi-1.5B, and test it on both NLU and NLG tasks. Results demonstrate significant improvement of MEOW in forget quality without substantial loss in model utility. Meanwhile, MEOW does not exhibit significant degradation in NLU or NLG capabilities, and there is even a slight improvement in NLU performance.

Related papers

• LLM Unlearning via Loss Adjustment with Only Forget Data [20.310423152885217]
  We introduce Forget data only Loss AjustmenT (FLAT), a "flat" loss adjustment approach which addresses these issues. Empirical results demonstrate that our approach achieves superior unlearning performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-10-14T23:43:33Z)
• Mitigating Memorization In Language Models [37.899013074095336]
  Language models (LMs) can "memorize" information, encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. We introduce TinyMem, a suite of small, computationally-efficient LMs for the rapid development and evaluation of memorization-mitigation methods. We show, in particular, that our proposed unlearning method BalancedSubnet outperforms other mitigation methods at removing memorized information while preserving performance on target tasks.
  arXiv  Detail & Related papers  (2024-10-03T02:53:51Z)
• Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models [25.91643745340183]
  Large Language Models (LLMs) have demonstrated strong reasoning and memorization capabilities via pretraining on massive textual corpora. This poses risk of privacy and copyright violations, highlighting the need for efficient machine unlearning methods. We propose two novel techniques for robust and efficient unlearning for LLMs.
  arXiv  Detail & Related papers  (2024-08-13T04:18:32Z)
• SNAP: Unlearning Selective Knowledge in Large Language Models with Negative Instructions [37.172662930947446]
  Instruction-following large language models (LLMs) inadvertently disclose personal or copyrighted information. We propose SNAP, an innovative framework designed to selectively unlearn information. We evaluate our framework on various NLP benchmarks and demonstrate that our approach retains the original LLM capabilities.
  arXiv  Detail & Related papers  (2024-06-18T06:54:05Z)
• Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference [39.29939437034823]
  We propose a novel unlearning framework called Unlearning from Logit Difference (ULD) Our method efficiently achieves the intended forgetting while preserving the LLM's overall capabilities, reducing training time by more than threefold.
  arXiv  Detail & Related papers  (2024-06-12T19:26:35Z)
• LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement [79.31084387589968]
  Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. We propose LLM2LLM, a data augmentation strategy that uses a teacher LLM to enhance a small seed dataset. We achieve improvements up to 24.2% on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC and 39.8% on SST-2 over regular fine-tuning in the low-data regime.
  arXiv  Detail & Related papers  (2024-03-22T08:57:07Z)
• Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs [61.04246774006429]
  We introduce a black-box prompt optimization method that uses an attacker LLM agent to uncover higher levels of memorization in a victim agent. We observe that our instruction-based prompts generate outputs with 23.7% higher overlap with training data compared to the baseline prefix-suffix measurements. Our findings show that instruction-tuned models can expose pre-training data as much as their base-models, if not more so, and using instructions proposed by other LLMs can open a new avenue of automated attacks.
  arXiv  Detail & Related papers  (2024-03-05T19:32:01Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• Unlearn What You Want to Forget: Efficient Unlearning for LLMs [92.51670143929056]
  Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data. This process might suffer from privacy issues and violations of data protection regulations. We propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals.
  arXiv  Detail & Related papers  (2023-10-31T03:35:59Z)
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
  Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
  arXiv  Detail & Related papers  (2023-10-10T16:38:49Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.