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pro vyhledávání: '"de Rijke, A."'
Neural ranking models (NRMs) have been shown to be highly effective in terms of retrieval performance. Unfortunately, they have also displayed a higher degree of sensitivity to attacks than previous generation models. To help expose and address this
Externí odkaz:
http://arxiv.org/abs/2412.18770
Generative information retrieval methods retrieve documents by directly generating their identifiers. Much effort has been devoted to developing effective generative IR models. Less attention has been paid to the robustness of these models. It is cri
Externí odkaz:
http://arxiv.org/abs/2412.18768
Retrieving relevant context is a common approach to reduce hallucinations and enhance answer reliability. Explicitly citing source documents allows users to verify generated responses and increases trust. Prior work largely evaluates citation correct
Externí odkaz:
http://arxiv.org/abs/2412.18004
Autor:
Deng, Songgaojun, de Rijke, Maarten
Time series forecasting is vital in many real-world applications, yet developing models that generalize well on unseen relevant domains -- such as forecasting web traffic data on new platforms/websites or estimating e-commerce demand in new regions -
Externí odkaz:
http://arxiv.org/abs/2412.11171
Generating diverse and effective clarifying questions is crucial for improving query understanding and retrieval performance in open-domain conversational search (CS) systems. We propose AGENT-CQ (Automatic GENeration, and evaluaTion of Clarifying Qu
Externí odkaz:
http://arxiv.org/abs/2410.19692
Attributing answers to source documents is an approach used to enhance the verifiability of a model's output in retrieval augmented generation (RAG). Prior work has mainly focused on improving and evaluating the attribution quality of large language
Externí odkaz:
http://arxiv.org/abs/2410.12380
Autor:
Lyu, Yougang, Yan, Lingyong, Wang, Zihan, Yin, Dawei, Ren, Pengjie, de Rijke, Maarten, Ren, Zhaochun
As large language models (LLMs) are rapidly advancing and achieving near-human capabilities, aligning them with human values is becoming more urgent. In scenarios where LLMs outperform humans, we face a weak-to-strong alignment problem where we need
Externí odkaz:
http://arxiv.org/abs/2410.07672
Electronic commerce, or e-commerce, is the buying and selling of goods and services, or the transmitting of funds or data online. E-commerce platforms come in many kinds, with global players such as Amazon, Airbnb, Alibaba, eBay and platforms targeti
Externí odkaz:
http://arxiv.org/abs/2410.05763
TableQA is the task of answering questions over tables of structured information, returning individual cells or tables as output. TableQA research has focused primarily on high-resource languages, leaving medium- and low-resource languages with littl
Externí odkaz:
http://arxiv.org/abs/2410.03576
Despite large language models (LLMs) increasingly becoming important components of news recommender systems, employing LLMs in such systems introduces new risks, such as the influence of cognitive biases in LLMs. Cognitive biases refer to systematic
Externí odkaz:
http://arxiv.org/abs/2410.02897