6 papers
Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders
Benjamin Rozonoyer, Chong You, Michael Boratko +5
The success of Large Language Models (LLMs) has motivated a shift toward generative approaches to retrieval and ranking, aiming to supersede classical Dual Encoders (DEs) and Cross…
Scalable In-context Ranking with Generative Models
Nilesh Gupta, Chong You, Srinadh Bhojanapalli +3
In-context Ranking (ICR) is an emerging paradigm for Information Retrieval (IR), which leverages contextual understanding of LLMs by directly incorporating the task description, ca…
Hierarchical Retrieval: The Geometry and a Pretrain-Finetune Recipe
Chong You, Rajesh Jayaram, Ananda Theertha Suresh +3
Dual encoder (DE) models, where a pair of matching query and document are embedded into similar vector representations, are widely used in information retrieval due to their simpli…
Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models
Haotian Ye, Himanshu Jain, Chong You +4
In real-world applications of large language models, outputs are often required to be confined: selecting items from predefined product or document sets, generating phrases that co…
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations
Xu Han, Felix Yu, Joao Sedoc +1
Our goal is a mechanism for efficiently assigning scalar ratings to each of a large set of elements. For example, "what percent positive or negative is this product review?" When s…
Efficient Document Ranking with Learnable Late Interactions
Ziwei Ji, Himanshu Jain, Andreas Veit +6
Cross-Encoder (CE) and Dual-Encoder (DE) models are two fundamental approaches for query-document relevance in information retrieval. To predict relevance, CE models use joint quer…