ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations
Brihi Joshi, Keyu He, Sahana Ramnath, Sadra Sabouri, Kaitlyn Zhou, Souti Chattopadhyay, Swabha Swayamdipta, Xiang Ren

TL;DR
This paper introduces ELI-Why, a benchmark for evaluating language models' ability to generate educational explanations tailored to different learning levels, revealing current models' limitations in pedagogical utility.
Contribution
The paper presents ELI-Why, a new benchmark with extensive human studies to assess and quantify the pedagogical effectiveness of language model explanations across educational levels.
Findings
GPT-4 explanations match intended educational levels only 50% of the time.
Lay human explanations match educational levels 79% of the time.
Automated metrics cannot distinguish explanations across different educational levels.
Abstract
Language models today are widely used in education, yet their ability to tailor responses for learners with varied informational needs and knowledge backgrounds remains under-explored. To this end, we introduce ELI-Why, a benchmark of 13.4K "Why" questions to evaluate the pedagogical capabilities of language models. We then conduct two extensive human studies to assess the utility of language model-generated explanatory answers (explanations) on our benchmark, tailored to three distinct educational grades: elementary, high-school and graduate school. In our first study, human raters assume the role of an "educator" to assess model explanations' fit to different educational grades. We find that GPT-4-generated explanations match their intended educational background only 50% of the time, compared to 79% for lay human-curated explanations. In our second study, human raters assume the role…
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Taxonomy
TopicsNatural Language Processing Techniques
