We explore the ability of large language models (LLMs) to act as ASR
post-processors that perform rescoring and error correction. Our focus is on
instruction prompting to let LLMs perform these task without fine-tuning, for
which we evaluate different prompting schemes, both zero- and few-shot
in-context learning, and a novel task-activating prompting (TAP) method that
combines instruction and demonstration. Using a pre-trained first-pass system
and rescoring output on two out-of-domain tasks (ATIS and WSJ), we show that
rescoring only by in-context learning with frozen LLMs achieves results that
are competitive with rescoring by domain-tuned LMs. By combining prompting
techniques with fine-tuning we achieve error rates below the N-best oracle
level, showcasing the generalization power of the LLMs.

使用大型语言模型进行 ASR 后处理，通过指令提示和上下文学习改进错误纠正，与领域精调模型相比，显示了语言模型的泛化能力。

利用大型语言模型进行生成式语音识别误差校正

Generative Speech Recognition Error Correction with Large Language  Models

Large Language Models (LLMs), now used daily by millions of users, can encode
societal biases, exposing their users to representational harms. A large body
of scholarship on LLM bias exists but it predominantly adopts a Western-centric
frame and attends comparatively less to bias levels and potential harms in the
Global South. In this paper, we quantify stereotypical bias in popular LLMs
according to an Indian-centric frame and compare bias levels between the Indian
and Western contexts. To do this, we develop a novel dataset which we call
Indian-BhED (Indian Bias Evaluation Dataset), containing stereotypical and
anti-stereotypical examples for caste and religion contexts. We find that the
majority of LLMs tested are strongly biased towards stereotypes in the Indian
context, especially as compared to the Western context. We finally investigate
Instruction Prompting as a simple intervention to mitigate such bias and find
that it significantly reduces both stereotypical and anti-stereotypical biases
in the majority of cases for GPT-3.5. The findings of this work highlight the
need for including more diverse voices when evaluating LLMs.

对大型语言模型的研究发现，它们往往存在社会偏见，尤其在印度和西方语境下，而引入一种称为 Instruction Prompting 的简单干预方法能够显著减少这种偏见。