FAME: Towards Factual Multi-Task Model Editing
- URL: http://arxiv.org/abs/2410.10859v2
- Date: Fri, 18 Oct 2024 10:02:03 GMT
- Title: FAME: Towards Factual Multi-Task Model Editing
- Authors: Li Zeng, Yingyu Shan, Zeming Liu, Jiashu Yao, Yuhang Guo,
- Abstract summary: Large language models (LLMs) embed extensive knowledge and utilize it to perform exceptionally well across various tasks.
We present FAME, an factual, comprehensive, and multi-task dataset, which is designed to enhance the practicality of model editing.
We then propose SKEME, a model editing method that uses a novel caching mechanism to ensure synchronization with the real world.
- Score: 4.858226284963096
- License:
- Abstract: Large language models (LLMs) embed extensive knowledge and utilize it to perform exceptionally well across various tasks. Nevertheless, outdated knowledge or factual errors within LLMs can lead to misleading or incorrect responses, causing significant issues in practical applications. To rectify the fatal flaw without the necessity for costly model retraining, various model editing approaches have been proposed to correct inaccurate knowledge within LLMs in a cost-efficient way. To evaluate these model editing methods, previous work introduced a series of datasets. However, most of the previous datasets only contain fabricated data in a single format, which diverges from real-world model editing scenarios, raising doubts about their usability in practice. To facilitate the application of model editing in real-world scenarios, we propose the challenge of practicality. To resolve such challenges and effectively enhance the capabilities of LLMs, we present FAME, an factual, comprehensive, and multi-task dataset, which is designed to enhance the practicality of model editing. We then propose SKEME, a model editing method that uses a novel caching mechanism to ensure synchronization with the real world. The experiments demonstrate that SKEME performs excellently across various tasks and scenarios, confirming its practicality.
Related papers
- The Mirage of Model Editing: Revisiting Evaluation in the Wild [70.17413507444704]
We study the effectiveness of model editing in question answering applications.
Our single editing experiments indicate that current editing methods perform substantially worse than previously reported.
Our analysis provides a fundamental reexamination of both the real-world applicability of existing model editing methods and their evaluation practices.
arXiv Detail & Related papers (2025-02-16T15:57:55Z) - LLMs are Also Effective Embedding Models: An In-depth Overview [40.53941563464671]
Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks.
Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift from traditional encoder-only models like ELMo and BERT to decoder-only, large-scale LLMs like GPT, LLaMA, and Mistral.
arXiv Detail & Related papers (2024-12-17T06:48:24Z) - Unified Parameter-Efficient Unlearning for LLMs [25.195126838721492]
Large Language Models (LLMs) have revolutionized natural language processing, enabling advanced understanding and reasoning capabilities across a variety of tasks.
This raises significant privacy and security concerns, as models may inadvertently retain and disseminate sensitive or undesirable information.
We introduce a novel instance-wise unlearning framework, LLMEraser, which systematically categorizes unlearning tasks and applies precise adjustments using influence functions.
arXiv Detail & Related papers (2024-11-30T07:21:02Z) - ELDER: Enhancing Lifelong Model Editing with Mixture-of-LoRA [55.697627106315004]
Large language models (LLMs) require model editing to efficiently update specific knowledge within them and avoid factual errors.
Previous approaches manage sequential edits by freezing original parameters and discretely allocating new parameters for each knowledge update.
We propose ELDER, a novel approach to create a continuous association between data and adapters.
arXiv Detail & Related papers (2024-08-19T02:27:00Z) - MoExtend: Tuning New Experts for Modality and Task Extension [61.29100693866109]
MoExtend is an effective framework designed to streamline the modality adaptation and extension of Mixture-of-Experts (MoE) models.
MoExtend seamlessly integrates new experts into pre-trained MoE models, endowing them with novel knowledge without the need to tune pretrained models.
arXiv Detail & Related papers (2024-08-07T02:28:37Z) - Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models [7.41744853269583]
We propose an Adversarial Representation Engineering (ARE) framework to provide a unified and interpretable approach for conceptual model editing.
Experiments on multiple tasks demonstrate the effectiveness of ARE in various model editing scenarios.
arXiv Detail & Related papers (2024-04-21T19:24:15Z) - The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse [58.0132400208411]
Even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks.
benchmarking Large Language Models after each edit is impractically time-consuming and resource-intensive.
We have utilized GPT-3.5 to develop a new dataset, HardEdit, based on hard cases.
arXiv Detail & Related papers (2024-02-15T01:50:38Z) - Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue [122.20016030723043]
We evaluate the side effects of model editing on large language models (LLMs)
Our analysis reveals that the side effects are caused by model editing altering the original model weights excessively.
To mitigate this, a method named RECT is proposed to regularize the edit update weights.
arXiv Detail & Related papers (2024-01-09T18:03:15Z) - Editing Large Language Models: Problems, Methods, and Opportunities [51.903537096207]
This paper embarks on a deep exploration of the problems, methods, and opportunities related to model editing for LLMs.
We provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal.
Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
arXiv Detail & Related papers (2023-05-22T16:00:00Z)
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.