55 citations · 71 across the 9 of their papers we have counts for
6 papers · 1 filter
LLMs are Also Effective Embedding Models: An In-depth Overview
Chongyang Tao, Tao Shen, Shen Gao +6
Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks. Recently, their effectiveness as embedd…
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Qi Chen, Xiubo Geng, Corby Rosset +28
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-s…
Corpus-Steered Query Expansion with Large Language Models
Yibin Lei, Yu Cao, Tianyi Zhou +2
Recent studies demonstrate that query expansions generated by large language models (LLMs) can considerably enhance information retrieval systems by generating hypothetical documen…
Meta-Task Prompting Elicits Embeddings from Large Language Models
Yibin Lei, Di Wu, Tianyi Zhou +4
We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL), for generating high-quality sentence embeddings from Large L…
A Survey on Knowledge Distillation of Large Language Models
Xiaohan Xu, Ming Li, Chongyang Tao +6
In the era of Large Language Models (LLMs), Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities from leading proprietary LLMs, such…
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges
Zhen Li, Xiaohan Xu, Tao Shen +5
In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content qual…