Zobrazeno 1 - 10
of 661
pro vyhledávání: '"Clarke, Charles"'
Autor:
Clarke, Charles L. A.
This paper introduces annotative indexing, a novel framework that unifies and generalizes traditional inverted indexes, column stores, object stores, and graph databases. As a result, annotative indexing can provide the underlying indexing framework
Externí odkaz:
http://arxiv.org/abs/2411.06256
Autor:
Jiang, Chumeng, Wang, Jiayin, Ma, Weizhi, Clarke, Charles L. A., Wang, Shuai, Wu, Chuhan, Zhang, Min
With the rapid development of Large Language Models (LLMs), recent studies employed LLMs as recommenders to provide personalized information services for distinct users. Despite efforts to improve the accuracy of LLM-based recommendation models, rela
Externí odkaz:
http://arxiv.org/abs/2411.00331
Autor:
Rahmani, Hossein A., Yilmaz, Emine, Craswell, Nick, Mitra, Bhaskar, Thomas, Paul, Clarke, Charles L. A., Aliannejadi, Mohammad, Siro, Clemencia, Faggioli, Guglielmo
The LLMJudge challenge is organized as part of the LLM4Eval workshop at SIGIR 2024. Test collections are essential for evaluating information retrieval (IR) systems. The evaluation and tuning of a search system is largely based on relevance labels, w
Externí odkaz:
http://arxiv.org/abs/2408.08896
Autor:
Rahmani, Hossein A., Siro, Clemencia, Aliannejadi, Mohammad, Craswell, Nick, Clarke, Charles L. A., Faggioli, Guglielmo, Mitra, Bhaskar, Thomas, Paul, Yilmaz, Emine
The first edition of the workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) took place in July 2024, co-located with the ACM SIGIR Conference 2024 in the USA (SIGIR 2024). The aim was to bring information retriev
Externí odkaz:
http://arxiv.org/abs/2408.05388
Autor:
Arabzadeh, Negar, Huo, Siqing, Mehta, Nikhil, Wu, Qinqyun, Wang, Chi, Awadallah, Ahmed, Clarke, Charles L. A., Kiseleva, Julia
The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks. However, a significant gap remains in assessing to what extent LLM-po
Externí odkaz:
http://arxiv.org/abs/2405.02178
Conversational prompt-engineering-based large language models (LLMs) have enabled targeted control over the output creation, enhancing versatility, adaptability and adhoc retrieval. From another perspective, digital misinformation has reached alarmin
Externí odkaz:
http://arxiv.org/abs/2404.16859
This paper is a draft of a chapter intended to appear in a forthcoming book on generative information retrieval, co-edited by Chirag Shah and Ryen White. In this chapter, we consider generative information retrieval evaluation from two distinct but i
Externí odkaz:
http://arxiv.org/abs/2404.08137
Information retrieval systems increasingly incorporate generative components. For example, in a retrieval augmented generation (RAG) system, a retrieval component might provide a source of ground truth, while a generative component summarizes and aug
Externí odkaz:
http://arxiv.org/abs/2404.04044
Autor:
Arabzadeh, Negar, Kiseleva, Julia, Wu, Qingyun, Wang, Chi, Awadallah, Ahmed, Dibia, Victor, Fourney, Adam, Clarke, Charles
The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their daily tasks. However, a significant gap remains in assessing whether L
Externí odkaz:
http://arxiv.org/abs/2402.09015
The rapid advancement of natural language processing, information retrieval (IR), computer vision, and other technologies has presented significant challenges in evaluating the performance of these systems. One of the main challenges is the scarcity
Externí odkaz:
http://arxiv.org/abs/2401.17543