We lifted 9 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lum1104/eibench | canonical | 5 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| calculate_average_score | Ran | lum1104/eibench/EIBench/EI_Basic/get_scores.py code served (permissive licence) · get_code("3842caa4b493f017") |
| encode_image | Ran | lum1104/eibench/EIBench/baselines/ChatGPT-4/gpt4-score-complex.py code served (permissive licence) · get_code("f41cb1a19b154297") |
| extract_scores_from_jsonl | Ran | lum1104/eibench/EIBench/EI_Basic/get_scores.py code served (permissive licence) · get_code("03387f6b6c21f170") |
| extract_scores_from_jsonl | Ran | lum1104/eibench/EIBench/EI_Complex/get_scores_complex.py code served (permissive licence) · get_code("4807f06cfd55795c") |
| get_ann | Ran | lum1104/eibench/EIBench/human_eval/web_ann_basic.py code served (permissive licence) · get_code("c1a40518b7d33c75") |
| ask_chatgpt | Not yet run | lum1104/eibench/EIBench/EI_Basic/gpt-eval.py code served (permissive licence) · get_code("d12384275cefed30") |
| ask_chatgpt | Not yet run | lum1104/eibench/EIBench/baselines/ChatGPT-4/gpt4-score-complex.py code served (permissive licence) · get_code("6b60d87b77a0ce1c") |
| load_jsonl | Not yet run | lum1104/eibench/EIBench/human_eval/web_ann_basic.py code served (permissive licence) · get_code("e85a5515b1b5db8b") |
| switch_label_based_on_dropdown | Not yet run | lum1104/eibench/EIBench/human_eval/web_ann_basic.py code served (permissive licence) · get_code("e2378b5484a0e5e6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Most existing emotion analysis emphasizes which emotion arises (e.g., happy, sad, angry) but neglects the deeper why. We propose Emotion Interpretation (EI), focusing on causal factors-whether explicit (e.g., observable objects, interpersonal interactions) or implicit (e.g., cultural context, off-screen events)-that drive emotional responses. Unlike traditional emotion recognition, EI tasks require reasoning about triggers instead of mere labeling. To facilitate EI research, we present EIBench, a large-scale benchmark encompassing 1,615 basic EI samples and 50 complex EI samples featuring multifaceted emotions. Each instance demands rationale-based explanations rather than straightforward categorization. We further propose a Coarse-to-Fine Self-Ask (CFSA) annotation pipeline, which guides Vision-Language Models (VLLMs) through iterative question-answer rounds to yield high-quality labels at scale. Extensive evaluations on open-source and proprietary large language models under four experimental settings reveal consistent performance gaps-especially for more intricate scenarios-underscoring EI's potential to enrich empathetic, context-aware AI applications. Our benchmark and methods are publicly available at: https://github.com/Lum1104/EIBench, offering a foundation for advanced multimodal causal analysis and next-generation affective computing.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2504.07521")
get_code_for_paper("2504.07521")
have("2504.07521")
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