Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by
retrieving relevant memories from an external database. However, existing RAG
methods typically organize all memories in a whole database, potentially
limiting focus on crucial memories and introducing noise. In this paper, we
introduce a multiple partition paradigm for RAG (called M-RAG), where each
database partition serves as a basic unit for RAG execution. Based on this
paradigm, we propose a novel framework that leverages LLMs with Multi-Agent
Reinforcement Learning to optimize different language generation tasks
explicitly. Through comprehensive experiments conducted on seven datasets,
spanning three language generation tasks and involving three distinct language
model architectures, we confirm that M-RAG consistently outperforms various
baseline methods, achieving improvements of 11%, 8%, and 12% for text
summarization, machine translation, and dialogue generation, respectively.

通过引入多个分区范例（称为 M-RAG），每个数据库分区作为 RAG 执行的基本单位，本文在 Retrieval-Augmented Generation（RAG）的基础上提出了一个新的框架，利用多智能体强化学习优化不同的语言生成任务。通过在七个数据集上进行的综合实验，涵盖三个语言生成任务和三种不同的语言模型架构，我们证实了 M-RAG 相对于各种基准方法的持续优势，分别在文本摘要、机器翻译和对话生成方面取得了 11％、8％和 12％的改进。

M-RAG: 通过多个分区的检索增强生成加强大型语言模型性能

M-RAG: Reinforcing Large Language Model Performance through  Retrieval-Augmented Generation with Multiple Partitions

We study conversational dialog in which there are many possible responses to
a given history. We present the MultiTalk Dataset, a corpus of over 320,000
sentences of written conversational dialog that balances a high branching
factor (10) with several conversation turns (6) through selective branch
continuation. We make multiple contributions to study dialog generation in the
highly branching setting. In order to evaluate a diverse set of generations, we
propose a simple scoring algorithm, based on bipartite graph matching, to
optimally incorporate a set of diverse references. We study multiple language
generation tasks at different levels of predictive conversation depth, using
textual attributes induced automatically from pretrained classifiers. Our
culminating task is a challenging theory of mind problem, a controllable
generation task which requires reasoning about the expected reaction of the
listener.

研究了具有多种可能响应的对话，并使用多元会话数据集平衡高分歧因素 (10) 和若干次对话 (6)，通过选择性分支延续。为了评估多样化生成的不同任务，我们提出了一个简单的评分算法，基于二分图匹配来最佳整合一组多样参考。最终任务是理解听众期望反应的可控制生成任务，利用文本属性从预训练分类器自动诱导多层次预测会话深度的多个语言生成任务的研究。

MultiTalk: 用于多样对话的高分支对话测试平台

MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations

Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a
powerful technique for natural language understanding and generation. Existing
pre-training techniques employ autoencoding and/or autoregressive objectives to
train Transformer-based models by recovering original word tokens from
corrupted text with some masked tokens. The training goals of existing
techniques are often inconsistent with the goals of many language generation
tasks, such as generative question answering and conversational response
generation, for producing new text given context.
This work presents PALM with a novel scheme that jointly pre-trains an
autoencoding and autoregressive language model on a large unlabeled corpus,
specifically designed for generating new text conditioned on context. The new
scheme alleviates the mismatch introduced by the existing denoising scheme
between pre-training and fine-tuning where generation is more than
reconstructing original text. An extensive set of experiments show that PALM
achieves new state-of-the-art results on a variety of language generation
benchmarks covering generative question answering (Rank 1 on the official MARCO
leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword,
question generation on SQuAD, and conversational response generation on Cornell
Movie Dialogues.

本文提出了一种新颖的方法 PALM，用于在大规模无标签语料库上联合预训练自编码和自回归语言模型，特别为给定上下文生成新文本设计，实验证明该方法在各种语言生成基准测试中均取得了新的最优结果。