Summary of Llm Targeted Underperformance Disproportionately Impacts Vulnerable Users, by Elinor Poole-dayan et al.
LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users
by Elinor Poole-Dayan, Deb Roy, Jad Kabbara
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the quality of Large Language Model (LLM) responses based on user traits such as English proficiency, education level, and country of origin. The study focuses on three state-of-the-art LLMs and two datasets to evaluate truthfulness and factuality. The results show that undesirable behaviors like hallucinations and bias occur more frequently for users with lower English proficiency, lower education status, and non-US origins, making these models unreliable sources of information for vulnerable user groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well large language models answer questions based on the person asking them. They tested three top models and two types of data to see if they tell the truth. The results show that these models are less reliable when people have lower English skills, less education, or come from outside the US. This means that these models might not be trustworthy for people who need accurate information. |
Keywords
* Artificial intelligence * Large language model
