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.

本文对 LLMs 的自我信息更新任务进行了全面的研究，并评估了其持续微调方法。作者发现，普通的持续微调方法可能存在暴露偏差问题。因此，他们提出了一种有效的方法来缓解这个问题，进一步开发了新闻文章数据集来评估信息更新。实验结果表明，所提出的方法能显著提高事实一致性分数（0 到 1）0.16，对与新信息不直接相关的指令的性能几乎没有影响。

通过减轻曝光偏差来更新大型语言模型的自我信息

Self Information Update for Large Language Models through Mitigating  Exposure Bias

This paper presents a novel optimization framework for automatic speech
recognition (ASR) with the aim of reducing hallucinations produced by an ASR
model. The proposed framework optimizes the ASR model to maximize an expected
factual consistency score between ASR hypotheses and ground-truth
transcriptions, where the factual consistency score is computed by a separately
trained estimator. Experimental results using the AMI meeting corpus and the
VoxPopuli corpus show that the ASR model trained with the proposed framework
generates ASR hypotheses that have significantly higher consistency scores with
ground-truth transcriptions while maintaining the word error rates close to
those of cross entropy-trained ASR models. Furthermore, it is shown that
training the ASR models with the proposed framework improves the speech
summarization quality as measured by the factual consistency of meeting
conversation summaries generated by a large language model.

本文提出了一种优化框架来减少自动语音识别模型产生的幻听，该框架通过使用先前训练好的评估器计算现实一致性得分，最大限度地优化 ASR 模型，以最大限度地提高 ASR 假设与基础事实一致性的期望得分，实验结果表明，使用提出的框架训练的 ASR 模型产生了与交叉熵训练的 ASR 模型相当的单词错误率，同时生成的 ASR 假设与基准真实转录的一致性得分显著提高，而且通过大型语言模型生成的会话摘要的可靠性也得到了提升。