Summary of Generative Echo Chamber? Effects Of Llm-powered Search Systems on Diverse Information Seeking, by Nikhil Sharma et al.
Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking
by Nikhil Sharma, Q. Vera Liao, Ziang Xiao
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 study investigates the potential risks of large language models (LLMs) powered conversational search systems in increasing selective exposure to biased information. The authors conducted two experiments to compare conventional search with LLM-powered conversational search. The results show that participants engaged in more biased information querying when using LLM-powered conversational search, and an opinionated LLM reinforcing their views exacerbated this bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how a new type of search system called large language models (LLMs) can affect what we see online. LLMs are like super smart computers that understand natural language and can answer questions. The researchers wanted to know if using these systems makes us more likely to only look at information that agrees with our own opinions, instead of seeing different viewpoints. They did two experiments and found that people do engage in more biased searching when using LLM-powered conversational search. This is important because it could change how we get our news and what we think about the world. |
