Why Would You Suggest That? Human Trust in Language Model Responses
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909336205262848 |
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| author | Sharma, Manasi Siu, Ho Chit Paleja, Rohan Peña, Jaime D. |
| author_facet | Sharma, Manasi Siu, Ho Chit Paleja, Rohan Peña, Jaime D. |
| contents | The emergence of Large Language Models (LLMs) has revealed a growing need for human-AI collaboration, especially in creative decision-making scenarios where trust and reliance are paramount. Through human studies and model evaluations on the open-ended News Headline Generation task from the LaMP benchmark, we analyze how the framing and presence of explanations affect user trust and model performance. Overall, we provide evidence that adding an explanation in the model response to justify its reasoning significantly increases self-reported user trust in the model when the user has the opportunity to compare various responses. Position and faithfulness of these explanations are also important factors. However, these gains disappear when users are shown responses independently, suggesting that humans trust all model responses, including deceptive ones, equitably when they are shown in isolation. Our findings urge future research to delve deeper into the nuanced evaluation of trust in human-machine teaming systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02018 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Why Would You Suggest That? Human Trust in Language Model Responses Sharma, Manasi Siu, Ho Chit Paleja, Rohan Peña, Jaime D. Computation and Language Artificial Intelligence Human-Computer Interaction The emergence of Large Language Models (LLMs) has revealed a growing need for human-AI collaboration, especially in creative decision-making scenarios where trust and reliance are paramount. Through human studies and model evaluations on the open-ended News Headline Generation task from the LaMP benchmark, we analyze how the framing and presence of explanations affect user trust and model performance. Overall, we provide evidence that adding an explanation in the model response to justify its reasoning significantly increases self-reported user trust in the model when the user has the opportunity to compare various responses. Position and faithfulness of these explanations are also important factors. However, these gains disappear when users are shown responses independently, suggesting that humans trust all model responses, including deceptive ones, equitably when they are shown in isolation. Our findings urge future research to delve deeper into the nuanced evaluation of trust in human-machine teaming systems. |
| title | Why Would You Suggest That? Human Trust in Language Model Responses |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.02018 |