Model Evaluation Insights
Current benchmarks like MMLU often fail to reflect real-world applications of language models, which are needed for tasks like structuring documents and tool interaction. Instead of creating specialized models for specific fields, leveraging retrieval augmented generation allows for a more flexible approach, enabling models to access relevant information as needed. Expanding context windows can enhance performance, but practical limitations still exist regarding the volume of documents that can be processed effectively.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Cohere co-founder Nick Frosst on building LLM apps for business
Related Questions
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?