mlsanityData to 7 Oct 2026

ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading

arXiv 2306.07875 ↗First posted 13 Jun 2023cs.IR cs.AI cs.CLcited by 3

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Abstract (arXiv)

With the rapid growth and spread of online misinformation, people need tools to help them evaluate the credibility and accuracy of online information. Lateral reading, a strategy that involves cross-referencing information with multiple sources, may be an effective approach to achieving this goal. In this paper, we present ReadProbe, a tool to support lateral reading, powered by generative large language models from OpenAI and the Bing search engine. Our tool is able to generate useful questions for lateral reading, scour the web for relevant documents, and generate well-attributed answers to help people better evaluate online information. We made a web-based application to demonstrate how ReadProbe can help reduce the risk of being misled by false information. The code is available at https://github.com/DakeZhang1998/ReadProbe. An earlier version of our tool won the first prize in a national AI misinformation hackathon.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 13 Jun 2023.

Builds on

6 of 20 references.

References · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  4. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  5. 2019Document Expansion by Query Predictioncited by 225
  6. 2020Zero-shot Neural Passage Retrieval via Domain-targeted Synthetic Question Generationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.

3 papers.