NLP LLM Leaderboard
Embrace Federated LLM Fine-Tuning and Secure Your Spot on the Leaderboard!
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| Rank | Team | Base Model Size | Comm. Costs | Average (↑) | STEM | Social Sciences | Humanities | Code | Date |
|---|---|---|---|---|---|---|---|---|---|
1 | Baseline | 7B | 40.7 GB | 12.82 | 12.37 | 13.49 | 12.60 | link | 01.10.24 |
In the realm of Natural Language Processing (NLP), developing models that can effectively understand and generate human language is foundational. Federated LLM fine-tuning of models trained on general NLP tasks is vital as it democratizes LLM training across a diverse set of downstream tasks while preserving data privacy. This approach enable that the fine-tuned language models are not only robust and generalizable across various linguistic contexts but also attuned to nuances and colloquialisms present in different datasets.
