Rates the intensity of four emotions a character feels at the end of a GPT-4-written dialogue, scored by distance from an author-set reference; the version harnesses run today is now legacy on the publisher's own site.
unassessed
| Category | reasoning |
|---|---|
| Subcategory | emotional and social intelligence (dialogue emotion-intensity prediction) |
| Page status | superseded |
| Metric | EQ-Bench score (distance from reference ratings) |
| Direction | higher_is_better |
| Unit | points |
| Dataset size | 171 |
| Dataset licence | MIT |
| Publisher | Independent researcher (no institutional affiliation given in the paper) |
EQ-Bench shows a model a short GPT-4-generated dialogue depicting a scene of conflict or tension between two characters, then asks it to rate the intensity (0-10) of four named emotions one character is likely feeling at the end of the scene -- typically one clearly present emotion, one clearly absent one, and two that require a careful, nuanced reading of the exchange. The format was designed as an improvement on an earlier psychometric test (SECEU) adapted for LLMs: it removes the requirement that ratings sum to a fixed total, uses reference answers set by the benchmark's author rather than averaged from a human crowd, and can be scored objectively without a human or model assessor interpreting free text. It is a single-turn, English-language, text-only task that the paper frames as testing "emotional understanding" specifically, one of four branches of the psychological construct of emotional intelligence.
Given a dialogue and four named emotions, output an intensity rating from 0 to 10 for each in a fixed format; scored by a distance-from-reference formula rather than exact match (see How it is scored). The original protocol requests both a first-pass and a self-revised answer and keeps whichever scores higher, though later pipeline versions disable the revision step by default since it was found to help only about 8% of the time.
No model card in ModelSpec reports this benchmark yet.