Evidence-Driven Retrieval Augmented Response Generation for Online
  Misinformation
Zhenrui Yue Huimin Zeng Yimeng Lu Lanyu Shang Yang Zhang Dong Wang
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.