Is it Possible to Edit Large Language Models Robustly?
- URL: http://arxiv.org/abs/2402.05827v1
- Date: Thu, 8 Feb 2024 17:06:45 GMT
- Title: Is it Possible to Edit Large Language Models Robustly?
- 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 to imitate human behaviors.
Recent studies have delved into the realm of model editing, which manipulates specific memories of language models and changes the related language generation.
This work seeks to understand the strengths and limitations of editing methods, thus facilitating robust, realistic applications of communicative AI.
- Score: 60.36021686516329
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) have played a pivotal role in building
communicative AI to imitate human behaviors but face the challenge of efficient
customization. To tackle this challenge, recent studies have delved into the
realm of model editing, which manipulates specific memories of language models
and changes the related language generation. However, the robustness of model
editing remains an open question. This work seeks to understand the strengths
and limitations of editing methods, thus facilitating robust, realistic
applications of communicative AI. Concretely, we conduct extensive analysis to
address the three key research questions. Q1: Can edited LLMs behave
consistently resembling communicative AI in realistic situations? Q2: To what
extent does the rephrasing of prompts lead LLMs to deviate from the edited
knowledge memory? Q3: Which knowledge features are correlated with the
performance and robustness of editing? Our experimental results uncover a
substantial disparity between existing editing methods and the practical
application of LLMs. On rephrased prompts that are complex and 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.
Related papers
- 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) - 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) - Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue [122.20016030723043]
Model editing is a technique that edits large language models (LLMs) with updated knowledge to alleviate hallucinations without resource-intensive retraining.
Current model editing methods can effectively modify a model's behavior within a specific area of interest.
They often overlook the potential unintended side effects on the general abilities of LLMs.
arXiv Detail & Related papers (2024-01-09T18:03:15Z) - DUnE: Dataset for Unified Editing [3.7346004746366384]
We introduce DUnE-an editing benchmark where edits are natural language sentences.
We show that retrieval-augmented language modeling can outperform specialized editing techniques.
arXiv Detail & Related papers (2023-11-27T18:56:14Z) - 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.