4 papers
Lighting the Way for BRIGHT: Reproducible Baselines with Anserini, Pyserini, and RankLLM
Sahel Sharifymoghaddam, Yijun Ge, Jimmy Lin
Retrieval benchmarks for large language models (LLMs) should reflect the long, reasoning-intensive queries typical of retrieval-augmented generation (RAG). We present a systematic…
Efficient Hyperparameter Search for Non-Stationary Model Training
Berivan Isik, Matthew Fahrbach, Dima Kuzmin +4
Online learning is the cornerstone of applications like recommendation and advertising systems, where models continuously adapt to shifting data distributions. Model training for s…
REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives
Kun Su, Krishna Sayana, Hubert Pham +8
This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Model…
Beyond Retrieval: Generating Narratives in Conversational Recommender Systems
Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski +6
The recent advances in Large Language Model's generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems. However, effectiv…