398 citations · 2.8k across the 98 of their papers we have counts for
115 papers
Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval
Jonghyun Song, Cheyon Jin, Wenlong Zhao +2
A common retrieve-and-rerank paradigm involves retrieving relevant candidates from a broad set using a fast bi-encoder (BE), followed by applying expensive but accurate cross-encod…
Large Language Model Augmented Narrative Driven Recommendations
Sheshera Mysore, Andrew McCallum, Hamed Zamani
Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for exa…
Answering Compositional Queries with Set-Theoretic Embeddings
Shib Dasgupta, Andrew McCallum, Steffen Rendle +1
The need to compactly and robustly represent item-attribute relations arises in many important tasks, such as faceted browsing and recommendation systems. A popular machine learnin…
Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond
Haw-Shiuan Chang, Zonghai Yao, Alolika Gon +2
Is the output softmax layer, which is adopted by most language models (LMs), always the best way to compute the next word probability? Given so many attention layers in a modern tr…
Machine Reading Comprehension using Case-based Reasoning
Dung Thai, Dhruv Agarwal, Mudit Chaudhary +6
We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our meth…
Editable User Profiles for Controllable Text Recommendation
Sheshera Mysore, Mahmood Jasim, Andrew McCallum +1
Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanism…