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Scientists, meet LLEMMA: your new math assistant.
On October 20, 2023, researchers from various universities and Eleuther AI, a company known for its open models, presented LLEMMA, an open large language learning (LLM) model specifically designed for solving mathematical problems.
LLEMMA outperforms other leading mathematical models, including Google's Minerva, by providing a solid platform for further research. Although LLEMMA is not an ideal mathematical problem solver, it is an important step in the development of specialized models and can stimulate AI research in new directions.
LLEMMA was created on the basis of Code Llama, an adaptation of the open Llama 2 model, configured for specific code datasets. The researchers developed two versions of the model, one with 7 billion parameters and the other with 34 billion. These models were further tuned to Proof-Pile-2, a data set created by researchers that consists of scientific articles, web data with mathematical content, and mathematical code.
In their experiments, the researchers found that LLEMMA shows better performance compared to all known open models on mathematical benchmarks. LLEMMA can also use tools and prove formal theorems without additional configuration, as well as use computational tools such as the Python interpreter to solve mathematical problems.
The researchers released all of their assets, including models with 7 and 34 billion parameters, a Proof-of-Pile - 2 dataset, and code to reproduce their experiments. According to the researchers, LLEMMA is the first open model to match the performance of the latest generation of closed models.
They expressed the hope that LLEMMA and Proof-Pile-2 will provide a useful basis for future work on understanding the generalization of language models, exploring the limits of domain-specific language models, and improving the mathematical capabilities of language models.
Overall, LLEMMA is part of a broader initiative to develop LLMs that specialize in a specific field, demonstrating that with improved data and larger datasets, even smaller models can produce significant results.
On October 20, 2023, researchers from various universities and Eleuther AI, a company known for its open models, presented LLEMMA, an open large language learning (LLM) model specifically designed for solving mathematical problems.
LLEMMA outperforms other leading mathematical models, including Google's Minerva, by providing a solid platform for further research. Although LLEMMA is not an ideal mathematical problem solver, it is an important step in the development of specialized models and can stimulate AI research in new directions.
LLEMMA was created on the basis of Code Llama, an adaptation of the open Llama 2 model, configured for specific code datasets. The researchers developed two versions of the model, one with 7 billion parameters and the other with 34 billion. These models were further tuned to Proof-Pile-2, a data set created by researchers that consists of scientific articles, web data with mathematical content, and mathematical code.
In their experiments, the researchers found that LLEMMA shows better performance compared to all known open models on mathematical benchmarks. LLEMMA can also use tools and prove formal theorems without additional configuration, as well as use computational tools such as the Python interpreter to solve mathematical problems.
The researchers released all of their assets, including models with 7 and 34 billion parameters, a Proof-of-Pile - 2 dataset, and code to reproduce their experiments. According to the researchers, LLEMMA is the first open model to match the performance of the latest generation of closed models.
They expressed the hope that LLEMMA and Proof-Pile-2 will provide a useful basis for future work on understanding the generalization of language models, exploring the limits of domain-specific language models, and improving the mathematical capabilities of language models.
Overall, LLEMMA is part of a broader initiative to develop LLMs that specialize in a specific field, demonstrating that with improved data and larger datasets, even smaller models can produce significant results.
