Learn When (not) to Trust Language Models: A Privacy-Centric Adaptive Model-Aware Approach

• URL: http://arxiv.org/abs/2404.03514v1
• Date: Thu, 4 Apr 2024 15:21:22 GMT
• Title: Learn When (not) to Trust Language Models: A Privacy-Centric Adaptive Model-Aware Approach
• Authors: Chengkai Huang, Rui Wang, Kaige Xie, Tong Yu, Lina Yao,
• Abstract summary: Retrieval-augmented large language models (LLMs) have been remarkably competent in various NLP tasks. Previous work has proposed to determine when to do/skip the retrieval in a data-aware manner by analyzing the LLMs' pretraining data. These data-aware methods pose privacy risks and memory limitations, especially when requiring access to sensitive or extensive pretraining data. We hypothesize that token embeddings are able to capture the model's intrinsic knowledge, which offers a safer and more straightforward way to judge the need for retrieval without the privacy risks associated with accessing pre-training data.
• Score: 23.34505448257966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented large language models (LLMs) have been remarkably competent in various NLP tasks. Despite their great success, the knowledge provided by the retrieval process is not always useful for improving the model prediction, since in some samples LLMs may already be quite knowledgeable and thus be able to answer the question correctly without retrieval. Aiming to save the cost of retrieval, previous work has proposed to determine when to do/skip the retrieval in a data-aware manner by analyzing the LLMs' pretraining data. However, these data-aware methods pose privacy risks and memory limitations, especially when requiring access to sensitive or extensive pretraining data. Moreover, these methods offer limited adaptability under fine-tuning or continual learning settings. We hypothesize that token embeddings are able to capture the model's intrinsic knowledge, which offers a safer and more straightforward way to judge the need for retrieval without the privacy risks associated with accessing pre-training data. Moreover, it alleviates the need to retain all the data utilized during model pre-training, necessitating only the upkeep of the token embeddings. Extensive experiments and in-depth analyses demonstrate the superiority of our model-aware approach.

Related papers

• Gradual Learning: Optimizing Fine-Tuning with Partially Mastered Knowledge in Large Language Models [51.20499954955646]
  Large language models (LLMs) acquire vast amounts of knowledge from extensive text corpora during the pretraining phase. In later stages such as fine-tuning and inference, the model may encounter knowledge not covered in the initial training. We propose a two-stage fine-tuning strategy to improve the model's overall test accuracy and knowledge retention.
  arXiv  Detail & Related papers  (2024-10-08T08:35:16Z)
• Adaptive Pre-training Data Detection for Large Language Models via Surprising Tokens [1.2549198550400134]
  Large language models (LLMs) are extensively used, but there are concerns regarding privacy, security, and copyright due to their opaque training data. Current solutions to this problem leverage techniques explored in machine learning privacy such as Membership Inference Attacks (MIAs) We propose an adaptive pre-training data detection method which alleviates this reliance and effectively amplify the identification.
  arXiv  Detail & Related papers  (2024-07-30T23:43:59Z)
• Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models [37.172662930947446]
  Language models (LMs) are potentially vulnerable to extraction attacks, which represent a significant privacy risk. We propose Privacy Protection via Optimal Parameters (POP), a novel unlearning method that effectively forgets the target token sequences from the pretrained LM. POP exhibits remarkable retention performance post-unlearning across 9 classification and 4 dialogue benchmarks, outperforming the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2024-06-20T08:12:49Z)
• Large Language Models Must Be Taught to Know What They Don't Know [97.90008709512921]
  We show that fine-tuning on a small dataset of correct and incorrect answers can create an uncertainty estimate with good generalization and small computational overhead. We also investigate the mechanisms that enable reliable uncertainty estimation, finding that many models can be used as general-purpose uncertainty estimators.
  arXiv  Detail & Related papers  (2024-06-12T16:41:31Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization [51.34904967046097]
  Continual learning seeks to overcome the challenge of catastrophic forgetting, where a model forgets previously learnt information. We introduce a novel prior-based method that better constrains parameter growth, reducing catastrophic forgetting. Results show that BAdam achieves state-of-the-art performance for prior-based methods on challenging single-headed class-incremental experiments.
  arXiv  Detail & Related papers  (2023-09-15T17:10:51Z)
• Knowledge Unlearning for Mitigating Privacy Risks in Language Models [31.322818016245087]
  We propose knowledge unlearning as an alternative method to reduce privacy risks for language models. We show that simply applying the unlikelihood training objective to target token sequences is effective at forgetting them. We show that unlearning can give a stronger empirical privacy guarantee in scenarios where the data vulnerable to extraction attacks are known a priori.
  arXiv  Detail & Related papers  (2022-10-04T10:18:11Z)
• Knowledge-driven Active Learning [70.37119719069499]
  Active learning strategies aim at minimizing the amount of labelled data required to train a Deep Learning model. Most active strategies are based on uncertain sample selection, and even often restricted to samples lying close to the decision boundary. Here we propose to take into consideration common domain-knowledge and enable non-expert users to train a model with fewer samples.
  arXiv  Detail & Related papers  (2021-10-15T06:11:53Z)
• REALM: Retrieval-Augmented Language Model Pre-Training [37.3178586179607]
  We augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend over documents from a large corpus such as Wikipedia. For the first time, we show how to pre-train such a knowledge retriever in an unsupervised manner. We demonstrate the effectiveness of Retrieval-Augmented Language Model pre-training (REALM) by fine-tuning on the challenging task of Open-domain Question Answering (Open-QA)
  arXiv  Detail & Related papers  (2020-02-10T18:40:59Z)

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.