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Status: Unchecked ×Keyword: open-source language models ×Clear all

4 claims from 2 papers

1. Computer Science › Topic Modeling

   LLaMA: Open and Efficient Foundation Language Models

   Touvron, Lavril, Izacard et al. · arXiv (Cornell University) · 2023

   The paper introduces LLaMA, language models of 7B to 65B parameters trained on public data, which it reports match or beat larger models on most benchmarks, and releases them to researchers.

   Unchecked2 claims
   Show 2 claims

   1. UncheckedThe authors say state-of-the-art language models can be trained using only publicly available data, with no proprietary or inaccessible datasets.“We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets.”
   2. UncheckedThe paper reports that LLaMA-13B beats the much larger GPT-3 on most benchmarks, and LLaMA-65B is competitive with Chinchilla-70B and PaLM-540B.“In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B.”

2. Computer Science › Topic Modeling

   Evaluation of Medium-Sized Language Models in German and English Language

   Peinl and Wirth · International Journal on Natural Language Computing · 2024

   Unchecked2 claims
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   1. Unchecked“Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT.”
   2. Unchecked“The best MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters.”