Despite the remarkable ability of large language models (LMs) to comprehend
and generate language, they have a tendency to hallucinate and create factually
inaccurate output. Augmenting LMs by retrieving information from external
knowledge resources is one promising solution. Most existing
retrieval-augmented LMs employ a retrieve-and-generate setup that only
retrieves information once based on the input. This is limiting, however, in
more general scenarios involving generation of long texts, where continually
gathering information throughout the generation process is essential. There
have been some past efforts to retrieve information multiple times while
generating outputs, which mostly retrieve documents at fixed intervals using
the previous context as queries. In this work, we provide a generalized view of
active retrieval augmented generation, methods that actively decide when and
what to retrieve across the course of the generation. We propose
Forward-Looking Active REtrieval augmented generation (FLARE), a generic
retrieval-augmented generation method which iteratively uses a prediction of
the upcoming sentence to anticipate future content, which is then utilized as a
query to retrieve relevant documents to regenerate the sentence if it contains
low-confidence tokens. We test FLARE along with baselines comprehensively over
4 long-form knowledge-intensive generation tasks/datasets. FLARE achieves
superior or competitive performance on all tasks, demonstrating the
effectiveness of our method. Code and datasets are available at
this https URL

该论文提出了基于预测未来内容并利用其作为查询来检索相关文档的 Forward-Looking Active Retrieval augmented generation (FLARE) 方法，用以提高在生成长文本时控制语言模型虚构和不准确性的能力。测试证明，FLARE 在 4 个长篇知识密集型生成任务 / 数据集上表现优异，证明了该方法的有效性。

主动检索增强生成

Active Retrieval Augmented Generation

The Track-1 of DSTC9 aims to effectively answer user requests or questions
during task-oriented dialogues, which are out of the scope of APIs/DB. By
leveraging external knowledge resources, relevant information can be retrieved
and encoded into the response generation for these out-of-API-coverage queries.
In this work, we have explored several advanced techniques to enhance the
utilization of external knowledge and boost the quality of response generation,
including schema guided knowledge decision, negatives enhanced knowledge
selection, and knowledge grounded response generation. To evaluate the
performance of our proposed method, comprehensive experiments have been carried
out on the publicly available dataset. Our approach was ranked as the best in
human evaluation of DSTC9 Track-1.

本研究探索了多种高级技术，包括基于模式的知识决策、增强负例选择和基于知识的回答生成，利用外部知识资源来回答任务对话中 API/DB 无法覆盖的查询。在公共数据集上进行全面的实验后，本文提出的方法在 DSTC9 跟踪 1 中获得了最佳结果。

多尺度负采样的外部知识选择学习

Learning to Select External Knowledge with Multi-Scale Negative Sampling

Recent developments in pre-trained neural language modeling have led to leaps
in accuracy on commonsense question-answering benchmarks. However, there is
increasing concern that models overfit to specific tasks, without learning to
utilize external knowledge or perform general semantic reasoning. In contrast,
zero-shot evaluations have shown promise as a more robust measure of a model's
general reasoning abilities. In this paper, we propose a novel neuro-symbolic
framework for zero-shot question answering across commonsense tasks. Guided by
a set of hypotheses, the framework studies how to transform various
pre-existing knowledge resources into a form that is most effective for
pre-training models. We vary the set of language models, training regimes,
knowledge sources, and data generation strategies, and measure their impact
across tasks. Extending on prior work, we devise and compare four constrained
distractor-sampling strategies. We provide empirical results across five
commonsense question-answering tasks with data generated from five external
knowledge resources. We show that, while an individual knowledge graph is
better suited for specific tasks, a global knowledge graph brings consistent
gains across different tasks. In addition, both preserving the structure of the
task as well as generating fair and informative questions help language models
learn more effectively.

本文提出了一种基于神经符号框架的零样本问题回答方法，利用预训练模型的外部知识资源，获得了在不同任务中的一致性提升，并且在任务结构保持的同时还生成了公平和信息丰富的问题，从而提高了语言模型的学习效果。