Reproduce “Factuality Challenges in the Era of Large Language Models” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara and 10 more
A comprehensive, multidisciplinary analysis identifies the multifaceted risks to information veracity posed by Large Language Models, detailing both their inherent factual limitations and potential for malicious exploitation. The analysis concludes with a multi-pronged agenda encompassing technological, regulatory, and educational interventions to mitigate these threats and ensure responsible AI development.
Key insights
- The challenge of LLM factuality is inherently multidisciplinary, requiring integrated approaches from technology, social science, policy, and fact-checking.
- LLMs pose both unintentional risks (e.g., hallucinations, lack of sourcing) and intentional threats (e.g., personalized attacks, large-scale bot generation) that necessitate distinct mitigation strategies.
- While posing risks, LLMs also offer opportunities to augment human fact-checking efforts by assisting with tasks like claim detection and summarization.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.