most citedLearning to Tokenize for Generative Retrieval

16 citations · 28 across the 9 of their papers we have counts for

collaborators

13 papers

cs.IR2024

Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System

Xin Yang, Heng Chang, Zhijian Lai +6

Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and it has been g…

cs.IR20242 cited

When Search Engine Services meet Large Language Models: Visions and Challenges

Haoyi Xiong, Jiang Bian, Yuchen Li +5

Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we sear…

cs.CL20241 cited

XLBench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies

Xuanfan Ni, Hengyi Cai, Xiaochi Wei +3

Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks but are constrained by their small context window sizes. Various efforts have been propos…

cs.CR20246 cited

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)

Shenglai Zeng, Jiankun Zhang, Pengfei He +8

Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model with proprietary and private data, where data privacy is a pivotal concern. Whereas extens…

cs.IR20233 cited

Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers

Weiwei Sun, Zheng Chen, Xinyu Ma +6

Recent studies have demonstrated the great potential of Large Language Models (LLMs) serving as zero-shot relevance rankers. The typical approach involves making comparisons betwee…

cs.CL2023

DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment

Yukun Zhao, Lingyong Yan, Weiwei Sun +5

Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment…