Cheating LLM Benchmarks

A research project that explores cheating behaviors in automated language model benchmarking.

CommonProductProgrammingNatural Language ProcessingMachine Learning

Cheating LLM Benchmarks is a research initiative aimed at exploring cheating behaviors in automated language model (LLM) benchmarking by constructing what are known as 'null models.' The project’s experiments have revealed that even simple null models can achieve high win rates in these benchmarks, challenging the validity and reliability of current benchmarking practices. This research is crucial for understanding the limitations of current language models and improving benchmarking methodologies.

Construct null models to participate in language model benchmarking.
Provide experimental steps and code via Jupyter Notebook.
Utilize the AlpacaEval tool to evaluate model outputs.
Calculate and analyze win rates and standard errors of models.
Provide detailed experimental results and analytical data.
Support further re-evaluation and analysis of experimental results.

The target audience primarily consists of researchers and developers in the field of Natural Language Processing (NLP) as well as tech enthusiasts interested in evaluating the performance of language models. This project provides them with a platform to test and understand the benchmark performance of existing language models and explore ways to improve these testing methods.

Researchers use this project to test and analyze the performance of different language models on specific tasks.
Developers leverage the project's code and tools to build and evaluate their own language models.
Educational institutions may utilize this project as a teaching case to help students understand the complexities of language model evaluation.

1. Visit the project's GitHub page and clone or download the project code.
2. Install necessary dependencies such as Jupyter Notebook and AlpacaEval.
3. Run the Jupyter Notebook file in the project
such as '01_prepare_submission.ipynb'
to create null model submissions.

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