The proliferation of online misinformation has posed significant threats to
public interest. While numerous online users actively participate in the combat
against misinformation, many of such responses can be characterized by the lack
of politeness and supporting facts. As a solution, text generation approaches
are proposed to automatically produce counter-misinformation responses.
Nevertheless, existing methods are often trained end-to-end without leveraging
external knowledge, resulting in subpar text quality and excessively repetitive
responses. In this paper, we propose retrieval augmented response generation
for online misinformation (RARG), which collects supporting evidence from
scientific sources and generates counter-misinformation responses based on the
evidences. In particular, our RARG consists of two stages: (1) evidence
collection, where we design a retrieval pipeline to retrieve and rerank
evidence documents using a database comprising over 1M academic articles; (2)
response generation, in which we align large language models (LLMs) to generate
evidence-based responses via reinforcement learning from human feedback (RLHF).
We propose a reward function to maximize the utilization of the retrieved
evidence while maintaining the quality of the generated text, which yields
polite and factual responses that clearly refutes misinformation. To
demonstrate the effectiveness of our method, we study the case of COVID-19 and
perform extensive experiments with both in- and cross-domain datasets, where
RARG consistently outperforms baselines by generating high-quality
counter-misinformation responses.

通过从科学性来源收集支持证据并基于这些证据生成反错误信息响应，RARG（检索增强的在线错误信息响应生成）方法在 COVID-19 案例中展示了在数据集上始终优于基准的产生高质量反错误信息响应。

基于证据驱动的在线虚假信息检索增强响应生成

Evidence-Driven Retrieval Augmented Response Generation for Online  Misinformation

The spread of online misinformation threatens public health, democracy, and
the broader society. While professional fact-checkers form the first line of
defense by fact-checking popular false claims, they do not engage directly in
conversations with misinformation spreaders. On the other hand, non-expert
ordinary users act as eyes-on-the-ground who proactively counter misinformation
-- recent research has shown that 96% counter-misinformation responses are made
by ordinary users. However, research also found that 2/3 times, these responses
are rude and lack evidence. This work seeks to create a counter-misinformation
response generation model to empower users to effectively correct
misinformation. This objective is challenging due to the absence of datasets
containing ground-truth of ideal counter-misinformation responses, and the lack
of models that can generate responses backed by communication theories. In this
work, we create two novel datasets of misinformation and counter-misinformation
response pairs from in-the-wild social media and crowdsourcing from
college-educated students. We annotate the collected data to distinguish poor
from ideal responses that are factual, polite, and refute misinformation. We
propose MisinfoCorrect, a reinforcement learning-based framework that learns to
generate counter-misinformation responses for an input misinformation post. The
model rewards the generator to increase the politeness, factuality, and
refutation attitude while retaining text fluency and relevancy. Quantitative
and qualitative evaluation shows that our model outperforms several baselines
by generating high-quality counter-responses. This work illustrates the promise
of generative text models for social good -- here, to help create a safe and
reliable information ecosystem. The code and data is accessible on
this https URL

本论文旨在通过建立一个基于强化学习的生成式文本模型 MisinfoCorrect 来有效地纠正不实信息，在社交媒体和众包数据的基础上，创造了两个新的数据集，并通过定量和定性评估证明了该模型可以生成高质量的反驳回复，提高了礼貌性，事实性和反驳态度。