Retrieval-augmented generation models have shown state-of-the-art performance
across many knowledge-intensive NLP tasks such as open question answering and
fact verification. These models are trained to generate the final output given
the retrieved passages, which can be irrelevant to the original query, leading
to learning spurious cues or answer memorization. This work introduces a method
to incorporate the evidentiality of passages -- whether a passage contains
correct evidence to support the output -- into training the generator. We
introduce a multi-task learning framework to jointly generate the final output
and predict the evidentiality of each passage, leveraging a new task-agnostic
method to obtain silver evidentiality labels for supervision. Our experiments
on five datasets across three knowledge-intensive tasks show that our new
evidentiality-guided generator significantly outperforms its direct counterpart
with the same-size model and advances the state of the art on FaVIQ-Ambig. We
attribute these improvements to both the auxiliary multi-task learning and
silver evidentiality mining techniques.

本文介绍一种新的方法，通过多任务学习框架和银标签法来预测每个句子的证据性，并将其集成到生成模型的训练中，从而提高了知识密集型 NLP 任务中的生成模型的表现，尤其是在 FaVIQ-Ambig 上。

基于证据性引导的知识密集型自然语言处理任务生成

Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks

This paper studies the bias problem of multi-hop question answering models,
of answering correctly without correct reasoning. One way to robustify these
models is by supervising to not only answer right, but also with right
reasoning chains. An existing direction is to annotate reasoning chains to
train models, requiring expensive additional annotations. In contrast, we
propose a new approach to learn evidentiality, deciding whether the answer
prediction is supported by correct evidences, without such annotations.
Instead, we compare counterfactual changes in answer confidence with and
without evidence sentences, to generate "pseudo-evidentiality" annotations. We
validate our proposed model on an original set and challenge set in HotpotQA,
showing that our method is accurate and robust in multi-hop reasoning.

本文研究了多跳问答模型中的偏差问题，提出了一种不需要额外昂贵的注释即可学习证据性的新方法，在 HotpotQA 验证了该方法准确，鲁棒性强。