Self Information Update for Large Language Models through Mitigating Exposure Bias
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Current LLMs have demonstrated remarkable capabilities in addressing users' requests for various types of information. However, these models are limited by the most recent data available in their pretraining corpora, rendering them incapable of providing up-to-date information. Retraining LLMs from scratch is cost-prohibitive, and the effectiveness of continual fine-tuning on new corpora has not been thoroughly examined. Additionally, current update procedures typically demand significant human input to prepare the information into more structured format, such as knowledge triples, conversational data or responses with human feedback. In this study, we conduct a comprehensive examination of a novel self information update task in LLMs, which only requires the provision of informative text corpora. For instance, we can use the latest news articles to update the LLMs' existing knowledge. We define the self information update task and assess the continual fine-tuning approach for this purpose. We observe that the naive method of continual fine-tuning can be problematic due to LLMs' exposure bias, which prioritizes existing information over new information we aim to integrate and leads to incorrect reasoning chains that ultimately diminish the efficacy of information updates. Based on our analysis, we propose an effective method to mitigate exposure bias by incorporating the selection of relevant facts into training losses. Furthermore, we develop a dataset to evaluate information updates, derived from news articles published after March 2023. Experimental results demonstrate that our proposed approach significantly increases the factual consistency score (0 to 1) by 0.16 while having minimal impact on performance for instructions not directly related to the new information.
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