A recent series of tweets from various users highlighted the introduction of FollowIR, a new benchmark and dataset aimed at evaluating and improving the ability of Information Retrieval (IR) models to understand and follow complex instructions. This initiative, involving contributions from entities such as @allen_ai, addresses the challenge that most Large Language Models (LLMs) in information retrieval are currently not optimized for processing detailed, instruction-based queries but rather short, keyword-focused searches. The development of FollowIR signifies a significant step towards enhancing the functionality of IR models to cater to more sophisticated user queries.
Great work from @allen_ai et al. to design proper benchmarks for information retrieval on advanced queries. This is a massive use case of LLMs in production. https://t.co/xuNhZpOGmr
Search is more than just a single query: users have complex information needs! Sadly, most LLMs in information retrieval are not designed to work with rich instruction, and only support short, keyword-heavy queries 😭 We introduce benchmark & model for instruction-based… https://t.co/d0suTrLMMX
FollowIR Evaluating and Teaching Information Retrieval Models to Follow Instructions Modern Large Language Models (LLMs) are capable of following long and complex instructions that enable a diverse amount of user tasks. However, despite Information Retrieval (IR) models https://t.co/VUyfaWgrRp
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions In summary, the paper highlights the importance of enabling IR models to follow detailed instructions, introduces FOLLOWIR as a benchmark/training dataset for this task, and shows that while… https://t.co/O4HstH8XcJ
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions Presents a dataset and benchmark that evaluates how well IR models can follow complex instructions to determine relevant documents. 📝https://t.co/5xVDEpc91D 👨🏽💻https://t.co/ySF46sdDeB https://t.co/6AiylTOur6