On the Robustness of Editing Large Language Models

• URL: http://arxiv.org/abs/2402.05827v2
• Date: Fri, 25 Oct 2024 13:24:58 GMT
• Title: On the Robustness of Editing Large Language Models
• Authors: Xinbei Ma, Tianjie Ju, Jiyang Qiu, Zhuosheng Zhang, Hai Zhao, Lifeng Liu, Yulong Wang,
• Abstract summary: Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. This work seeks to understand the strengths and limitations of editing methods, facilitating practical applications of communicative AI.
• Score: 57.477943944826904
• License:
• Abstract: Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation of specific knowledge memories and the behavior of language generation without retraining. However, the robustness of model editing remains an open question. This work seeks to understand the strengths and limitations of editing methods, facilitating practical applications of communicative AI. We focus on three key research questions. RQ1: Can edited LLMs behave consistently resembling communicative AI in realistic situations? RQ2: To what extent does the rephrasing of prompts lead LLMs to deviate from the edited knowledge memory? RQ3: Which knowledge features are correlated with the performance and robustness of editing? Our empirical studies uncover a substantial disparity between existing editing methods and the practical application of LLMs. On rephrased prompts that are flexible but common in realistic applications, the performance of editing experiences a significant decline. Further analysis shows that more popular knowledge is memorized better, easier to recall, and more challenging to edit effectively. Code is publicly available at https://github.com/xbmxb/edit_analysis .

Related papers

• Relation Also Knows: Rethinking the Recall and Editing of Factual Associations in Auto-Regressive Transformer Language Models [15.698183471185066]
  The storage and recall of factual associations in auto-regressive transformer language models (LMs) have drawn a great deal of attention. Most editing works achieve knowledge editing under the guidance of existing interpretations of knowledge recall that mainly focus on subject knowledge. In this work, we discover a novel relation-focused perspective to interpret the knowledge recall of transformer LMs during inference and apply it on knowledge editing to avoid over-generalizing.
  arXiv  Detail & Related papers  (2024-08-27T14:22:02Z)
• How Well Can Knowledge Edit Methods Edit Perplexing Knowledge? [18.022428746019582]
  This study investigates the capability of knowledge editing methods to incorporate new knowledge with varying degrees of "perplexingness" We find significant negative correlations between the "perplexingness" of the new knowledge and the edit efficacy across all 12 scenarios. Further exploration into the influence of knowledge hierarchy on editing outcomes indicates that knowledge positioned at higher hierarchical levels is more challenging to modify in some scenarios.
  arXiv  Detail & Related papers  (2024-06-25T03:41:02Z)
• WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models [78.22291694903659]
  Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses. Where the updated knowledge resides in memories is a fundamental question for model editing. We propose WISE to bridge the gap between memories.
  arXiv  Detail & Related papers  (2024-05-23T16:35:52Z)
• Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning [30.554641380670315]
  We introduce RECIPE, a ContInuous Prompt lEarning method to boost editing efficacy and inference efficiency in lifelong learning. RECIPE first converts knowledge statements into short and informative continuous prompts, prefixed to the LLM's input query embedding. It further integrates the Knowledge Sentinel (KS) that acts as an intermediary to calculate a dynamic threshold. Our retriever and prompt encoder are jointly trained to achieve editing properties, i.e. reliability, generality, and locality.
  arXiv  Detail & Related papers  (2024-05-06T08:52:11Z)
• Detecting Edited Knowledge in Language Models [5.260519479124422]
  Knowledge editing methods (KEs) can update language models' obsolete or inaccurate knowledge learned from pre-training. Knowing whether a generated output is based on edited knowledge or first-hand knowledge from pre-training can increase users' trust in generative models. We propose a novel task: detecting edited knowledge in language models.
  arXiv  Detail & Related papers  (2024-05-04T22:02:24Z)
• Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering [47.199078631274745]
  Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge. We propose the Retrieval-Augmented model Editing (RAE) framework for multi-hop question answering.
  arXiv  Detail & Related papers  (2024-03-28T17:47:19Z)
• Robust and Scalable Model Editing for Large Language Models [75.95623066605259]
  We propose EREN (Edit models by REading Notes) to improve the scalability and robustness of LLM editing. Unlike existing techniques, it can integrate knowledge from multiple edits, and correctly respond to syntactically similar but semantically unrelated inputs.
  arXiv  Detail & Related papers  (2024-03-26T06:57:23Z)
• Learning to Edit: Aligning LLMs with Knowledge Editing [101.96620267293731]
  We propose a Learning to Edit (LTE) framework, focusing on teaching large language models to apply updated knowledge into input questions. LTE features a two-phase process: (i) the Alignment Phase, which fine-tunes LLMs on a meticulously curated parallel dataset to make reliable, in-scope edits. We demonstrate LTE's superiority in knowledge editing performance, robustness in both batch and sequential editing, minimal interference on general tasks, and rapid editing speeds.
  arXiv  Detail & Related papers  (2024-02-19T07:45:17Z)
• Cross-Lingual Knowledge Editing in Large Language Models [73.12622532088564]
  Knowledge editing has been shown to adapt large language models to new knowledge without retraining from scratch. It is still unknown the effect of source language editing on a different target language. We first collect a large-scale cross-lingual synthetic dataset by translating ZsRE from English to Chinese.
  arXiv  Detail & Related papers  (2023-09-16T11:07:52Z)
• Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs [54.22416829200613]
  Eva-KELLM is a new benchmark for evaluating knowledge editing of large language models. Experimental results indicate that the current methods for knowledge editing using raw documents are not effective in yielding satisfactory results.
  arXiv  Detail & Related papers  (2023-08-19T09:17:19Z)

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.