Retrieval Augmented Generation (RAG) has become prevalent in
question-answering (QA) tasks due to its ability of utilizing search engine to
enhance the quality of long-form question-answering (LFQA). Despite the
emergence of various open source methods and web-enhanced commercial systems
such as Bing Chat, two critical problems remain unsolved, i.e., the lack of
factuality and clear logic in the generated long-form answers. In this paper,
we remedy these issues via a systematic study on answer generation in
web-enhanced LFQA. Specifically, we first propose a novel outline-enhanced
generator to achieve clear logic in the generation of multifaceted answers and
construct two datasets accordingly. Then we propose a factuality optimization
method based on a carefully designed doubly fine-grained RLHF framework, which
contains automatic evaluation and reward modeling in different levels of
granularity. Our generic framework comprises conventional fine-grained RLHF
methods as special cases. Extensive experiments verify the superiority of our
proposed \textit{Factuality-optimized RAG (FoRAG)} method on both English and
Chinese benchmarks. In particular, when applying our method to Llama2-7B-chat,
the derived model FoRAG-L-7B outperforms WebGPT-175B in terms of three commonly
used metrics (i.e., coherence, helpfulness, and factuality), while the number
of parameters is much smaller (only 1/24 of that of WebGPT-175B). Our datasets
and models are made publicly available for better reproducibility:
this https URL

通过研究网络增强的长格式问答，本文提出了一种新的轮廓增强生成器以实现多方面答案的清晰逻辑，并基于精心设计的双重细粒度 RLHF 框架提出了一种基于事实性优化的检索增强生成方法（FoRAG），在英文和中文基准测试中验证了其优越性。

FoRAG: 面向增强长文本问答的事实性优化检索生成

FoRAG: Factuality-optimized Retrieval Augmented Generation for  Web-enhanced Long-form Question Answering

Conducting literature reviews for scientific papers is essential for
understanding research, its limitations, and building on existing work. It is a
tedious task which makes an automatic literature review generator appealing.
Unfortunately, many existing works that generate such reviews using Large
Language Models (LLMs) have significant limitations. They tend to
hallucinate-generate non-actual information-and ignore the latest research they
have not been trained on. To address these limitations, we propose a toolkit
that operates on Retrieval Augmented Generation (RAG) principles, specialized
prompting and instructing techniques with the help of LLMs. Our system first
initiates a web search to retrieve relevant papers by summarizing user-provided
abstracts into keywords using an off-the-shelf LLM. Authors can enhance the
search by supplementing it with relevant papers or keywords, contributing to a
tailored retrieval process. Second, the system re-ranks the retrieved papers
based on the user-provided abstract. Finally, the related work section is
generated based on the re-ranked results and the abstract. There is a
substantial reduction in time and effort for literature review compared to
traditional methods, establishing our toolkit as an efficient alternative. Our
open-source toolkit is accessible at this https URL
and Huggingface space (this https URL)
with the video demo at this https URL

利用大型语言模型（LLM）的检索增强生成（RAG）原理和专门的提示与指示技术，我们提出了一个工具包来减少文献综述的时间和工作量，建立了我们的工具包作为一种高效的替代方法。