Loading Now

Summary of Hallucination Detection and Hallucination Mitigation: An Investigation, by Junliang Luo et al.


Hallucination Detection and Hallucination Mitigation: An Investigation

by Junliang Luo, Tianyu Li, Di Wu, Michael Jenkin, Steve Liu, Gregory Dudek

First submitted to arxiv on: 16 Jan 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
In this paper, researchers comprehensively review the existing literature on detecting and mitigating hallucinations in large language models (LLMs), such as ChatGPT, Bard, and Llama. Hallucinations refer to incorrect responses generated by these models alongside correct ones. The authors aim to provide a comprehensive reference for engineers and researchers seeking to apply LLMs to real-world tasks. They discuss the current state-of-the-art methods in hallucination detection and mitigation, highlighting both strengths and limitations.
Low GrooveSquid.com (original content) Low Difficulty Summary
Large language models are incredibly good at helping us with many tasks like answering questions or generating text. However, they can also make mistakes and give false information. This is called “hallucination.” In this report, scientists review what we know about finding and fixing these errors in big language models. They want to help people who build and use these models understand how to prevent and detect these mistakes.

Keywords

» Artificial intelligence  » Hallucination  » Llama