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cs.IR2024
FIRST: Faster Improved Listwise Reranking with Single Token Decoding
Revanth Gangi Reddy, JaeHyeok Doo, Yifei Xu +4
Large Language Models (LLMs) have significantly advanced the field of information retrieval, particularly for reranking. Listwise LLM rerankers have showcased superior performance…
cs.IR2024
Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels
Jasper Xian, Saron Samuel, Faraz Khoubsirat +7
We develop a method for training small-scale (under 100M parameter) neural information retrieval models with as few as 10 gold relevance labels. The method depends on generating sy…
cs.IR2024
ReFIT: Relevance Feedback from a Reranker during Inference
Revanth Gangi Reddy, Pradeep Dasigi, Md Arafat Sultan +4
Retrieve-and-rerank is a prevalent framework in neural information retrieval, wherein a bi-encoder network initially retrieves a pre-defined number of candidates (e.g., K=100), whi…