Reported Scores
| Model | Score | Source paper | Year |
|---|---|---|---|
| GPT-3.5 | 80.0% | Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications / arxiv.org | 2024 |
| GPT-3.5 | 18.9% | A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot / arxiv.org | 2023 |
| GPT-3.5 | 18.1% | Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation / arxiv.org | 2024 |
| GPT-3.5 | 1.8% | ACE-RLHF: Automated Code Evaluation and Socratic Feedback Generation Tool using Large Language Models and Reinforcement Learning with Human Feedback / arxiv.org | 2025 |
Interpretation
This page groups score claims extracted from papers for the same model and benchmark label. A nonzero spread means the public literature reports different values for this cluster.
Differences are not automatically errors. They may come from prompt choices, dataset versions, evaluation protocol, scoring rule, preprocessing, fine-tuning, or reporting convention. Source papers remain authoritative for their own claims. See the quality guide for how to read evidence links, manifests, and automated assessment fields.
Source coverage is a conservative count of distinct public paper URLs or titles in the cluster. It measures coverage breadth, not correctness.
Source profile reports public URL domains and publication years when they are available in extracted records. It is included for auditability only.