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Workshop: Workshop on Distribution Shifts: New Frontiers with Foundation Models

Understanding Catastrophic Forgetting in Language Models via Implicit Inference

Suhas Kotha · Jacob Springer · Aditi Raghunathan

Keywords: [ catastrophic forgetting ] [ Fine-tuning ] [ implicit inference in language models ]


We lack a systematic understanding of the effects of fine-tuning (via methods such as instruction-tuning or reinforcement learning from human feedback), particularly on tasks outside the narrow fine-tuning distribution. In a simplified scenario, we demonstrate that improving performance on fine-tuning tasks comes at the expense of other pretraining capabilities. We hypothesize that models implicitly infer the task of the prompt and that fine-tuning skews this inference towards fine-tuning tasks. We find that artificially making the task look farther from the fine-tuning distribution while requiring the same capability can recover some of the pretraining capabilities on our synthetic setup. Since real fine-tuning distributions are predominantly English, we apply conjugate prompting to recover pretrained capabilities in LLMs by simply translating the prompts to different languages. This allows us to recover the in-context learning abilities lost via instruction tuning, and more concerningly, recover harmful content generation suppressed by safety fine-tuning in chatbots like ChatGPT.

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