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cs.CL2024
MultiContrievers: Analysis of Dense Retrieval Representations
Seraphina Goldfarb-Tarrant, Pedro Rodriguez, Jane Dwivedi-Yu +1
Dense retrievers compress source documents into (possibly lossy) vector representations, yet there is little analysis of what information is lost versus preserved, and how it affec…
cs.CL2024
SnapKV: LLM Knows What You are Looking for Before Generation
Yuhong Li, Yingbing Huang, Bowen Yang +6
Large Language Models (LLMs) have made remarkable progress in processing extensive contexts, with the Key-Value (KV) cache playing a vital role in enhancing their performance. Howe…
cs.CL2024
Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
Pat Verga, Sebastian Hofstatter, Sophia Althammer +6
As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe parti…