Summary of Quantitative Assessment Of Intersectional Empathetic Bias and Understanding, by Vojtech Formanek and Ondrej Sotolar
Quantitative Assessment of Intersectional Empathetic Bias and Understanding
by Vojtech Formanek, Ondrej Sotolar
First submitted to arxiv on: 8 Nov 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 paper proposes an empathy evaluation framework that operationalizes empathy based on its psychological origins, addressing issues with current definitions that affect dataset quality and model robustness. The framework measures variance in responses from language models (LLMs) to prompts using existing metrics for empathy and emotional valence. By controlling prompt generation, the authors ensure high theoretical validity of constructs in the prompt dataset. This approach enables high-quality translation into languages lacking evaluation methods, such as Slavonic family languages. Using various LLMs and prompt types, the paper demonstrates the framework’s effectiveness, including multiple-choice answers and free generation. Although initial results show small variance and no significant differences between social groups, the models’ ability to adjust their reasoning chains in response to subtle prompt changes is promising for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines can be more empathetic towards humans. Right now, we have trouble measuring empathy because definitions are vague and affect how well AI works. The authors suggest a new way to measure empathy by using language models that respond to prompts in different ways. They control the prompts to make sure they’re relevant and accurate, which helps with translation into languages that don’t have good empathy evaluation methods. The results show that machines can adjust their thinking when given subtle changes in prompts, which is promising for future research. |
Keywords
» Artificial intelligence » Prompt » Translation
