A common and fundamental limitation of Generative AI (GenAI) is its
propensity to hallucinate. While large language models (LLM) have taken the
world by storm, without eliminating or at least reducing hallucinations,
real-world GenAI systems may face challenges in user adoption. In the process
of deploying an enterprise application that produces workflows based on natural
language requirements, we devised a system leveraging Retrieval Augmented
Generation (RAG) to greatly improve the quality of the structured output that
represents such workflows. Thanks to our implementation of RAG, our proposed
system significantly reduces hallucinations in the output and improves the
generalization of our LLM in out-of-domain settings. In addition, we show that
using a small, well-trained retriever encoder can reduce the size of the
accompanying LLM, thereby making deployments of LLM-based systems less
resource-intensive.

提出了一种利用检索增强生成（RAG）的系统来改进结构化输出质量、减少生成 AI 中的幻觉，并提高 LLM 在领域外场景中的泛化能力的企业应用部署方法。

通过检索增强生成降低结构化输出中的错觉

Reducing hallucination in structured outputs via Retrieval-Augmented  Generation

State-of-the-art approaches for hate-speech detection usually exhibit poor
performance in out-of-domain settings. This occurs, typically, due to
classifiers overemphasizing source-specific information that negatively impacts
its domain invariance. Prior work has attempted to penalize terms related to
hate-speech from manually curated lists using feature attribution methods,
which quantify the importance assigned to input terms by the classifier when
making a prediction. We, instead, propose a domain adaptation approach that
automatically extracts and penalizes source-specific terms using a domain
classifier, which learns to differentiate between domains, and
feature-attribution scores for hate-speech classes, yielding consistent
improvements in cross-domain evaluation.

提出了一种域自适应方法，通过自动提取和惩罚源特定术语来适应不同领域的仇恨言论检测，以实现跨领域评估的一致提高。

基于领域分类的源特定术语罚款方法，用于仇恨言语检测中的领域适应

Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection

Neural passage retrieval is a new and promising approach in open retrieval
question answering. In this work, we stress-test the Dense Passage Retriever
(DPR) -- a state-of-the-art (SOTA) open domain neural retrieval model -- on
closed and specialized target domains such as COVID-19, and find that it lags
behind standard BM25 in this important real-world setting. To make DPR more
robust under domain shift, we explore its fine-tuning with synthetic training
examples, which we generate from unlabeled target domain text using a
text-to-text generator. In our experiments, this noisy but fully automated
target domain supervision gives DPR a sizable advantage over BM25 in
out-of-domain settings, making it a more viable model in practice. Finally, an
ensemble of BM25 and our improved DPR model yields the best results, further
pushing the SOTA for open retrieval QA on multiple out-of-domain test sets.

本研究针对新的神经通道检索方法加以探讨，发现在 COVID-19 的封闭和专业目标域中，SOTA 模型 Dense Passage Retriever 比标准 BM25 差距较大。通过用文本生成器来生成合成训练示例对其进行微调，使其在对外域数据处理中更具鲁棒性，效果有所提高。最终，BM25 与改进的 DPR 模型的集成产生了最佳结果，进一步推动了开放式检索 QA 的 SOTA，适用于多个出域测试集。

面向开放检索问答的合成目标领域监督

Synthetic Target Domain Supervision for Open Retrieval QA

Recent work in Natural Language Processing has focused on developing
approaches that extract faithful explanations, either via identifying the most
important tokens in the input (i.e. post-hoc explanations) or by designing
inherently faithful models that first select the most important tokens and then
use them to predict the correct label (i.e. select-then-predict models).
Currently, these approaches are largely evaluated on in-domain settings. Yet,
little is known about how post-hoc explanations and inherently faithful models
perform in out-of-domain settings. In this paper, we conduct an extensive
empirical study that examines: (1) the out-of-domain faithfulness of post-hoc
explanations, generated by five feature attribution methods; and (2) the
out-of-domain performance of two inherently faithful models over six datasets.
Contrary to our expectations, results show that in many cases out-of-domain
post-hoc explanation faithfulness measured by sufficiency and comprehensiveness
is higher compared to in-domain. We find this misleading and suggest using a
random baseline as a yardstick for evaluating post-hoc explanation
faithfulness. Our findings also show that select-then predict models
demonstrate comparable predictive performance in out-of-domain settings to
full-text trained models.

该论文探索了自然语言处理中有关如何在不同领域中提取精准的解释的方法，其中侧重于后续解释和固有的忠诚度模型，并且发现后续解释的忠诚度在许多情况下比在领域内更高，而在跨域设置中，选择后预测模型的预测性能与完整训练模型相当。