Conditional Language Learning with Context

• URL: http://arxiv.org/abs/2406.01976v1
• Date: Tue, 4 Jun 2024 05:22:24 GMT
• Title: Conditional Language Learning with Context
• Authors: Xiao Zhang, Miao Li, Ji Wu,
• Abstract summary: We propose a simple modification to causal language modeling called conditional finetuning. We show that a context can "explain away" certain corpus statistics and make the model avoid learning them.
• Score: 19.708303468664088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language models can learn sophisticated language understanding skills from fitting raw text. They also unselectively learn useless corpus statistics and biases, especially during finetuning on domain-specific corpora. In this paper, we propose a simple modification to causal language modeling called conditional finetuning, which performs language modeling conditioned on a context. We show that a context can "explain away" certain corpus statistics and make the model avoid learning them. In this fashion, conditional finetuning achieves selective learning from a corpus, learning knowledge useful for downstream tasks while avoiding learning useless corpus statistics like topic biases. This selective learning effect leads to less forgetting and better stability-plasticity tradeoff in domain finetuning, potentially benefitting lifelong learning with language models.

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