TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models
Keywords: continual learning, benchmark, LLM
TL;DR: The paper introduces CLIMB, a novel benchmark for evaluating the continual learning abilities of Large Language Models, highlighting the sensitivity of LLMs to incremental task learning and proposing a Process-Informed Training strategy.
Abstract: Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. However, the continual learning aspect of these aligned LLMs has been largely overlooked. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs, owing to both their simplicity and the models' potential exposure during instruction tuning.
In this paper, we introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs. TRACE consists of 8 distinct datasets spanning challenging tasks including domain-specific tasks, multilingual capabilities, code generation, and mathematical reasoning. All datasets are standardized into a unified format, allowing for effortless automatic evaluation of LLMs.
Our experiments show that after training on TRACE, aligned LLMs exhibit significant declines in both general ability and instruction-following capabilities. For example, the accuracy of llama2-chat 13B on gsm8k dataset declined precipitously from 28.8\% to 2\% after training on our datasets. This highlights the challenge of finding a suitable tradeoff between achieving performance on specific tasks while preserving the original prowess of LLMs. Empirical analysis reveals that training on reasoning tasks effectively mitigates the loss of general abilities of LLMs. Motivated by this, we introduce the Reasoning-augmented Continual Learning (RCL) approach. RCL integrates task-specific cues with meta-rationales, effectively reducing catastrophic forgetting in LLMs while expediting convergence on novel tasks.
Supplementary Material: zip
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 571
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