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GPT-4 Technical Report

★ ★ ★ ★ ☆

Paper Summary

Paperzilla title
GPT-4: Aceing Exams, But Still Hallucinating Occasionally

GPT-4 demonstrates human-level performance on many academic and professional exams, outperforming existing large language models on various NLP tasks. Despite its capabilities, GPT-4 still exhibits limitations like "hallucinations" and biases, necessitating further research and development in safety and alignment.

Explain Like I'm Five

Scientists made a super-smart computer brain called GPT-4 that can answer questions like a grown-up! But sometimes it makes up silly stories, so they are still teaching it to be perfect and always tell the truth.

Possible Conflicts of Interest

The authors are affiliated with OpenAI, the organization responsible for developing GPT-4, which could potentially introduce bias in the evaluation and reporting of results.

Identified Limitations

Extrapolated Percentile Ranges for Some Exams
The evaluation methodology for certain exams, such as the AMC 10 and 12, relies on extrapolation due to unpublished 2022 score distributions, leading to uncertainty in the reported percentile ranges.
Limited Transparency in Model Details
The report lacks transparency regarding important details like model size, training data, and specific training methods, hindering reproducibility and further analysis by the broader research community.
Brittleness of Current Safety Mitigations
While the report addresses some safety risks, it acknowledges the limitations and potential brittleness of current mitigations, highlighting the need for further research and development in safety and alignment.
Limited Multilingual Evaluation
The report primarily focuses on English-language benchmarks, with limited evaluation of performance in other languages, potentially overlooking cultural biases and language-specific limitations.
Potential Biases in Expert Adversarial Testing
Despite using domain experts for adversarial testing, the report acknowledges potential biases in expert selection and interpretation of risks, potentially overlooking certain vulnerabilities or failure modes.

Rating Explanation

This report presents a significant contribution to the field of large language models, demonstrating impressive capabilities on various benchmarks while also acknowledging limitations and potential risks. The evaluation methodology is generally robust, though some limitations exist, such as the use of extrapolated percentiles and the lack of full transparency in certain model details. The report also raises crucial questions about safety, ethics, and societal impact, making it a valuable resource for future research and development.

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File Information

Original Title: GPT-4 Technical Report
Uploaded: July 16, 2025 at 11:33 AM
Privacy: Public