Core Concepts
The author proposes HILL, a tool to identify and highlight hallucinations in Large Language Models (LLMs), enabling users to handle responses with caution. By incorporating user-centered design features, HILL aims to reduce overreliance on LLM responses.
Abstract
HILL is developed to address the issue of hallucinations in LLMs, providing users with tools to detect errors and potential biases in generated responses. The study includes a Wizard of Oz approach, prototype development, artifact generation, and evaluation through surveys and performance validation using SQuAD 2.0 questions.
Large language models (LLMs) like ChatGPT have gained popularity but are prone to hallucinations, leading to errors and misinterpretations. HILL aims to empower users by identifying and highlighting these hallucinations for more cautious interaction with LLM responses.
The study involved developing prototypes based on user feedback, implementing technical requirements for the artifact, conducting online surveys for usability evaluation, and assessing performance using SQuAD 2.0 questions.
Key features of HILL include identifying sources supporting responses, assessing political spectrum and monetary interest, detecting misinformation in responses, and calculating an overall confidence score for response validity.
Overall, HILL offers a user-centered approach to enhance the reliability of LLM responses by empowering users to detect potential errors and biases through innovative design features.
Stats
Users tend to overrely on LLMs which can lead to misinterpretations.
HILL correctly identifies and highlights hallucinations in LLM responses.
The study involved 17 participants for survey evaluation.
SQuAD 2.0 dataset used for performance validation.
Features prioritized based on WOz sessions included source links, monetary interest assessment, response type identification.
Quotes
"Users tend to overrely on Large Language Models (LLMs) which can lead to misinterpretations."
"HILL correctly identifies and highlights hallucinations in LLM responses."
"The study involved 17 participants for survey evaluation."