Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal

• URL: http://arxiv.org/abs/2403.01244v2
• Date: Sat, 25 May 2024 12:17:29 GMT
• Title: Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal
• Authors: Jianheng Huang, Leyang Cui, Ante Wang, Chengyi Yang, Xinting Liao, Linfeng Song, Junfeng Yao, Jinsong Su,
• Abstract summary: Large language models (LLMs) suffer from catastrophic forgetting during continual learning. We propose a framework called Self-Synthesized Rehearsal (SSR) that uses the LLM to generate synthetic instances for rehearsal.
• Score: 49.24054920683246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training data to retain the model's ability, which may not be feasible in real-world applications. When conducting continual learning based on a publicly-released LLM checkpoint, the availability of the original training data may be non-existent. To address this challenge, we propose a framework called Self-Synthesized Rehearsal (SSR) that uses the LLM to generate synthetic instances for rehearsal. Concretely, we first employ the base LLM for in-context learning to generate synthetic instances. Subsequently, we utilize the latest LLM to refine the instance outputs based on the synthetic inputs, preserving its acquired ability. Finally, we select diverse high-quality synthetic instances for rehearsal in future stages. Experimental results demonstrate that SSR achieves superior or comparable performance compared to conventional rehearsal-based approaches while being more data-efficient. Besides, SSR effectively preserves the generalization capabilities of LLMs in general domains.

Related papers

• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression. LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model. Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis [26.505386645322506]
  Large Language Models (LLMs) have garnered increasing attention in the field of natural language processing. In this paper, we shed light on a comprehensive evaluation of LLMs in the ABSA field, involving 13 datasets, 8 ABSA subtasks, and 6 LLMs. Our experiments demonstrate that LLMs achieve a new state-of-the-art performance compared to fine-tuned Small Language Models (SLMs) in the fine-tuning-dependent paradigm.
  arXiv  Detail & Related papers  (2024-12-03T08:54:17Z)
• Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting [21.04933334040135]
  We introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within Large Language Models. Our framework employs a three-stage diversity approach to prompt LLMs, generating multiple synthetic samples that encapsulate specific relations from scratch. Experimental evaluations on benchmark datasets show our approach outperforms existing LLM-based zero-shot RE methods.
  arXiv  Detail & Related papers  (2024-10-02T01:12:54Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery [5.2387832710686695]
  In this work, we introduce the first comprehensive framework that utilizes Large Language Models (LLMs) for the task of Symbolic Regression. We propose In-Context Symbolic Regression (ICSR), an SR method which iteratively refines a functional form with an external LLM and determines its coefficients with an external LLM. Our findings reveal that LLMs are able to successfully find symbolic equations that fit the given data, matching or outperforming the overall performance of the best SR baselines on four popular benchmarks.
  arXiv  Detail & Related papers  (2024-04-29T20:19:25Z)
• FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs [6.689848416609951]
  We study the interplay between unlearning and fairness for large language models (LLMs) We focus on a popular unlearning framework known as SISA, which creates an ensemble of models trained on disjoint shards. We propose post-processing bias mitigation techniques for ensemble models produced by SISA.
  arXiv  Detail & Related papers  (2023-12-12T16:44:47Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning [79.32236399694077]
  Low-quality data in the training set are usually detrimental to instruction tuning. We propose a novel method, termed "reflection-tuning" This approach utilizes an oracle LLM to recycle the original training data by introspecting and enhancing the quality of instructions and responses in the data.
  arXiv  Detail & Related papers  (2023-10-18T05:13:47Z)
• On Learning to Summarize with Large Language Models as References [101.79795027550959]
  Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
  arXiv  Detail & Related papers  (2023-05-23T16:56:04Z)

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.