Summary of Why Would You Suggest That? Human Trust in Language Model Responses, by Manasi Sharma et al.
Why Would You Suggest That? Human Trust in Language Model Responses
by Manasi Sharma, Ho Chit Siu, Rohan Paleja, Jaime D. Peña
First submitted to arxiv on: 4 Jun 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 The proposed paper investigates how explanations affect user trust and model performance in creative decision-making scenarios with Large Language Models (LLMs). The study focuses on the News Headline Generation task from the LaMP benchmark, analyzing the impact of framing and explanation presence on user trust. Results show that adding explanations to model responses increases self-reported user trust when users can compare various responses, highlighting the importance of position and faithfulness of these explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how humans and AI work together in creative tasks like generating news headlines. The study finds that when AI provides explanations for its answers, people tend to trust it more if they get to see different options. However, this trust disappears when users only see one answer at a time. These findings suggest that we need to think carefully about how humans and machines work together in the future. |
