In the 30th episode of Neural Search Talks, we have our very own Arthur Câmara, Senior Research Engineer at Zeta Alpha, presenting a 20-minute guide on how we fine-tune Large Language Models for effective text retrieval. Arthur discusses the common issues with embedding models in a general-purpose RAG pipeline, how to tackle the lack of retrieval-oriented data for fine-tuning with InPars, and how we adapted E5-Mistral to rank in the top 10 on the BEIR benchmark.
## Sources

InPars

• https://github.com/zetaalphavector/InPars
• https://dl.acm.org/doi/10.1145/3477495.3531863
• https://arxiv.org/abs/2301.01820
• https://arxiv.org/abs/2307.04601

Zeta-Alpha-E5-Mistral

• https://zeta-alpha.com/post/fine-tuning-an-llm-for-state-of-the-art-retrieval-zeta-alpha-s-top-10-submission-to-the-the-mteb-be
• https://huggingface.co/zeta-alpha-ai/Zeta-Alpha-E5-Mistral

NanoBEIR

• https://huggingface.co/collections/zeta-alpha-ai/nanobeir-66e1a0af21dfd93e620cd9f6