Pretraining Large Language Models (LLMs) on large corpora of textual data is
now a standard paradigm. When using these LLMs for many downstream
applications, it is common to additionally bake in new knowledge (e.g.,
time-critical news, or private domain knowledge) into the pretrained model
either through RAG-based-prompting, or fine-tuning. However, the optimal
methodology for the model to gain such new knowledge remains an open question.
In this paper, we present Retrieval Augmented FineTuning (RAFT), a training
recipe that improves the model's ability to answer questions in a "open-book"
in-domain settings. In RAFT, given a question, and a set of retrieved
documents, we train the model to ignore those documents that don't help in
answering the question, which we call, distractor documents. RAFT accomplishes
this by citing verbatim the right sequence from the relevant document that
would help answer the question. This coupled with RAFT's chain-of-thought-style
response helps improve the model's ability to reason. In domain-specific RAG,
RAFT consistently improves the model's performance across PubMed, HotpotQA, and
Gorilla datasets, presenting a post-training recipe to improve pre-trained LLMs
to in-domain RAG. RAFT's code and demo are open-sourced at
github.com/ShishirPatil/gorilla.

在这篇论文中，我们提出了一种名为检索增强微调 (RAFT) 的训练方法，它能够提高模型在领域内回答问题的能力，并处理无关文档干扰，从而提升大规模语言模型的性能。

适应领域特定 RAG 的语言模型 RAF

RAFT: Adapting Language Model to Domain Specific RAG

Parameter efficient learning methods (PERMs) have recently gained significant
attention as they provide an efficient way for pre-trained language models
(PLMs) to adapt to a downstream task. However, these conclusions are mostly
drawn from in-domain evaluations over the full training set. In this paper, we
present comparisons between PERMs and finetuning from three new perspectives:
(1) the effect of sample and model size to in-domain evaluations, (2)
generalization to unseen domains and new datasets, and (3) the faithfulness of
generations. Our results show that for in-domain settings (a) there is a cross
point of sample size for which PERMs will perform better than finetuning when
training with fewer samples, and (b) larger PLMs have larger cross points. For
cross-domain and cross-dataset cases, we show that (a) Adapter (Houlsby et al.,
2019) performs the best amongst all the PERMs studied here, and (b) it
outperforms finetuning if the task dataset is below a certain size. We also
compare the faithfulness of generations and show that PERMs can achieve better
faithfulness score than finetuning, especially for small training set, by as
much as 6%. Finally, we apply Adapter to MT-NLG 530b (Smith et al., 2022) and
achieve new state-of-the-art results on Xsum (Narayan et al., 2018) for all
ROUGE scores (ROUGE-1 49.17, ROUGE-2 27.20, ROUGE-L 40.98).

本文从三个新的角度比较 PERMs 和 finetuning，发现在训练样本较少情况下 PERMs 的表现更好且 PLMs 越大，表现越突出；Adapter 在任务数据集较小时优于 finetuning；PERMs 能够比 finetuning 更好地实现生成的忠实度，特别是对于小训练集而言。最后，作者将 Adapter 应用于 MT-NLG 530b，取得了 Xsum 数据集 ROUGE-1 49.17，ROUGE-2 27.20 以及 ROUGE-L 40.98 的全新 state-of-the-art 结果。