Large language models (LLMs) have been successfully applied for rescoring
automatic speech recognition (ASR) hypotheses. However, their ability to
rescore ASR hypotheses of casual conversations has not been sufficiently
explored. In this study, we reveal it by performing N-best ASR hypotheses
rescoring using Llama2 on the CHiME-7 distant ASR (DASR) task. Llama2 is one of
the most representative LLMs, and the CHiME-7 DASR task provides datasets of
casual conversations between multiple participants. We investigate the effects
of domain adaptation of the LLM and context carry-over when performing N-best
rescoring. Experimental results show that, even without domain adaptation,
Llama2 outperforms a standard-size domain-adapted Transformer-LM, especially
when using a long context. Domain adaptation shortens the context length needed
with Llama2 to achieve its best performance, i.e., it reduces the computational
cost of Llama2.

大语言模型已成功应用于重新评分自动语音识别假设，本研究揭示了它在非正式谈话中重新评分自动语音识别假设的能力，证明了 Llama2 在 CHiME-7 远程 ASR 任务上的优越性。

应用 LLMs 对非正式对话的 ASR 候选解进行再评分：领域适应和上下文传递的影响

Applying LLMs for Rescoring N-best ASR Hypotheses of Casual  Conversations: Effects of Domain Adaptation and Context Carry-over

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by
incorporating additional information from retrieval. However, studies have
shown that LLMs still face challenges in effectively using the retrieved
information, even ignoring it or being misled by it. The key reason is that the
training of LLMs does not clearly make LLMs learn how to utilize input
retrieved texts with varied quality. In this paper, we propose a novel
perspective that considers the role of LLMs in RAG as ``Information Refiner'',
which means that regardless of correctness, completeness, or usefulness of
retrieved texts, LLMs can consistently integrate knowledge within the retrieved
texts and model parameters to generate the texts that are more concise,
accurate, and complete than the retrieved texts. To this end, we propose an
information refinement training method named InFO-RAG that optimizes LLMs for
RAG in an unsupervised manner. InFO-RAG is low-cost and general across various
tasks. Extensive experiments on zero-shot prediction of 11 datasets in diverse
tasks including Question Answering, Slot-Filling, Language Modeling, Dialogue,
and Code Generation show that InFO-RAG improves the performance of LLaMA2 by an
average of 9.39\% relative points. InFO-RAG also shows advantages in in-context
learning and robustness of RAG.

提出了一种名为 InFO-RAG 的信息优化训练方法，通过优化大语言模型在检索增强生成中的作用，实现了对输入的检索文本进行信息精简，提高了生成的文本的准确性、完整性和简洁性，相对于 LLaMA2 平均提升 9.39% 的性能，并展现了在上下文学习和鲁棒性方面的优势。

大规模语言模型的无监督信息精炼训练用于检索增强生成

Unsupervised Information Refinement Training of Large Language Models  for Retrieval-Augmented Generation

Large Language Models (LLMs) demonstrate strong capability across multiple
tasks, including machine translation. Our study focuses on evaluating Llama2's
machine translation capabilities and exploring how translation depends on
languages in its training data. Our experiments show that the 7B Llama2 model
yields above 10 BLEU score for all languages it has seen, but not always for
languages it has not seen. Most gains for those unseen languages are observed
the most with the model scale compared to using chat versions or adding shot
count. Furthermore, our linguistic distance analysis reveals that syntactic
similarity is not always the primary linguistic factor in determining
translation quality. Interestingly, we discovered that under specific
circumstances, some languages, despite having significantly less training data
than English, exhibit strong correlations comparable to English. Our
discoveries here give new perspectives for the current landscape of LLMs,
raising the possibility that LLMs centered around languages other than English
may offer a more effective foundation for a multilingual model.

基于大型语言模型（LLMs），本研究评估了 Llama2 在机器翻译方面的能力，并探讨了对训练数据中语言的依赖性。实验证明，7B 规模的 Llama2 模型对其已见过的所有语言都具有 10 BLEU 分数以上，但对未见过的语言不一定如此。我们的语言距离分析表明，句法相似性并非决定翻译质量的主要语言因素。有趣的是，我们发现在特定条件下，某些语言虽然训练数据明显少于英语，却与英语具有可比较的强相关性。本研究结果为目前 LLMs 的发展提供了新的视角，提出了以非英语语言为中心构建多语言模型的可能性。