Selfcheckgpt Alternatives

selfcheckgpt reviews and mentions

• How to Detect AI Hallucinations
  5 projects | dev.to | 3 May 2024

  SelfCheckGPT is a method designed for detecting hallucinations in Large Language Models (LLMs) without the need for external resources. It evaluates the consistency of responses generated by LLMs to determine if the information provided is factual or not. SelfCheckGPT outperforms other methods and serves as a strong baseline for assessing the reliability of LLM-generated text. It works by comparing multiple responses generated by a Large Language Model (LLM) in response to a query. It measures the consistency between these responses to determine if the information provided is factual or hallucinated. By sampling multiple responses, SelfCheckGPT can identify inconsistencies and contradictions, indicating potential hallucinations in the generated text. This method does not require external databases and can be used for black-box models, making it a versatile tool for detecting unreliable information generated by LLMs. One of the main features of SelfCheckGPT is MQAG which stands for Multiple-choice Question Answering and Generation. It evaluates information consistency between source and summary using multiple-choice questions. Consists of question generation stage, statistical distance analysis, and answerability threshold setting. Uses total variation as the main statistical distance for comparison. Provides a novel approach to assess information content in summaries through question answering. Comparing Multiple Responses: SelfCheckGPT compares multiple responses generated by an LLM to measure consistency and identify potential hallucinations. Sampling Responses: By sampling multiple responses, SelfCheckGPT can detect inconsistencies and contradictions in the generated text. Utilizing Question Answering: SelfCheckGPT incorporates question answering to assess the consistency of information by generating multiple-choice questions and evaluating the answers. Entropy-based Metrics: It uses entropy-based metrics to analyze the probability distribution of words in the generated text, providing insights into the reliability of the information. Zero-resource Approach: SelfCheckGPT is a zero-resource approach that does not rely on external databases, making it applicable to black-box LLMs ( and that is the exact reason I like it )