History Matters: Temporal Knowledge Editing in Large Language Model

• URL: http://arxiv.org/abs/2312.05497v3
• Date: Thu, 14 Dec 2023 12:06:24 GMT
• Title: History Matters: Temporal Knowledge Editing in Large Language Model
• Authors: Xunjian Yin, Jin Jiang, Liming Yang, Xiaojun Wan
• Abstract summary: We introduce the task of Temporal Knowledge Editing (TKE) and establish a benchmark AToKe to evaluate current model editing methods. We find that while existing model editing methods are effective at making models remember new knowledge, the edited model catastrophically forgets historical knowledge. To address this gap, we propose a simple and general framework termed Multi-Editing with Time Objective (METO) for enhancing existing editing models.
• Score: 42.74144542674756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The imperative task of revising or updating the knowledge stored within large language models arises from two distinct sources: intrinsic errors inherent in the model which should be corrected and outdated knowledge due to external shifts in the real world which should be updated. Prevailing efforts in model editing conflate these two distinct categories of edits arising from distinct reasons and directly modify the original knowledge in models into new knowledge. However, we argue that preserving the model's original knowledge remains pertinent. Specifically, if a model's knowledge becomes outdated due to evolving worldly dynamics, it should retain recollection of the historical knowledge while integrating the newfound knowledge. In this work, we introduce the task of Temporal Knowledge Editing (TKE) and establish a benchmark AToKe (Assessment of TempOral Knowledge Editing) to evaluate current model editing methods. We find that while existing model editing methods are effective at making models remember new knowledge, the edited model catastrophically forgets historical knowledge. To address this gap, we propose a simple and general framework termed Multi-Editing with Time Objective (METO) for enhancing existing editing models, which edits both historical and new knowledge concurrently and optimizes the model's prediction for the time of each fact. Our assessments demonstrate that while AToKe is still difficult, METO maintains the effectiveness of learning new knowledge and meanwhile substantially improves the performance of edited models on utilizing historical knowledge.

Related papers

• Should We Really Edit Language Models? On the Evaluation of Edited Language Models [15.63231238452797]
  Existing editing methods lead to inevitable performance deterioration on general benchmarks. Instruction-tuned models are more robust to editing, showing less performance drop on general knowledge after editing. Our findings indicate that current editing methods are only suitable for small-scale knowledge updates within language models.
  arXiv  Detail & Related papers  (2024-10-24T14:36:48Z)
• 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)
• Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization [48.07144492109635]
  Large language models need to be updated regularly. Model editing is challenging as it might also affect knowledge that is unrelated to the new data. We propose SAUL, a streamlined model editing method that uses sentence concatenation with augmented random facts for generation regularization.
  arXiv  Detail & Related papers  (2024-10-03T12:28:13Z)
• Outdated Issue Aware Decoding for Reasoning Questions on Edited Knowledge [93.54427119091174]
  We propose outDated ISsue aware deCOding to enhance the performance of edited models on reasoning questions. We capture the difference in the probability distribution between the original and edited models. We amplify the difference of the token prediction in the edited model to alleviate the outdated issue.
  arXiv  Detail & Related papers  (2024-06-05T03:00:15Z)
• 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)
• A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
  Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches. We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
  arXiv  Detail & Related papers  (2024-01-02T16:54:58Z)
• Adaptively Integrated Knowledge Distillation and Prediction Uncertainty for Continual Learning [71.43841235954453]
  Current deep learning models often suffer from catastrophic forgetting of old knowledge when continually learning new knowledge. Existing strategies to alleviate this issue often fix the trade-off between keeping old knowledge (stability) and learning new knowledge (plasticity)
  arXiv  Detail & Related papers  (2023-01-18T05:36:06Z)

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.