22 citations · 53 across the 18 of their papers we have counts for
18 papers
Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
Madhuri Shanbhogue, Zhe Li, Shanfeng Zhang +86
We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage t…
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…
Mining Generalizable Activation Functions
Alex Vitvitskyi, Michael Boratko, Matej Grcic +3
The choice of activation function is an active area of research, with different proposals aimed at improving optimization, while maintaining expressivity. Additionally, the activat…
EmbeddingGemma: Powerful and Lightweight Text Representations
Henrique Schechter Vera, Sahil Dua, Biao Zhang +86
We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledg…
On the Theoretical Limitations of Embedding-Based Retrieval
Orion Weller, Michael Boratko, Iftekhar Naim +1
Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning, instruction-following, coding, an…
Gemini Embedding: Generalizable Embeddings from Gemini
Jinhyuk Lee, Feiyang Chen, Sahil Dua +44
In this report, we introduce Gemini Embedding, a state-of-the-art embedding model leveraging the power of Gemini, Google's most capable large language model. Capitalizing on Gemini…
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
Shib Dasgupta, Michael Boratko, Andrew McCallum
Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix facto…
A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks
Nicholas Monath, Will Grathwohl, Michael Boratko +3
In dense retrieval, deep encoders provide embeddings for both inputs and targets, and the softmax function is used to parameterize a distribution over a large number of candidate t…
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
Jinhyuk Lee, Anthony Chen, Zhuyun Dai +16
Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. Leveragi…
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures
Qi Cheng, Michael Boratko, Pranay Kumar Yelugam +4
Large language models have demonstrated impressive performance on commonsense tasks; however, these tasks are often posed as multiple-choice questions, allowing models to exploit s…
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren +17
We present Gecko, a compact and versatile text embedding model. Gecko achieves strong retrieval performance by leveraging a key idea: distilling knowledge from large language model…
Box Embeddings: An open-source library for representation learning using geometric structures
Tejas Chheda, Purujit Goyal, Trang Tran +4
A major factor contributing to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with geometric structures (eg. d…
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings
Shib Sankar Dasgupta, Michael Boratko, Siddhartha Mishra +4
Learning representations of words in a continuous space is perhaps the most fundamental task in NLP, however words interact in ways much richer than vector dot product similarity c…
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning
Xuelu Chen, Michael Boratko, Muhao Chen +3
Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for ea…
Modeling Fine-Grained Entity Types with Box Embeddings
Yasumasa Onoe, Michael Boratko, Andrew McCallum +1
Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types' compl…
Improving Local Identifiability in Probabilistic Box Embeddings
Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang +3
Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings, where objects are repr…
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Michael Boratko, Xiang Lorraine Li, Rajarshi Das +3
Given questions regarding some prototypical situation such as Name something that people usually do before they leave the house for work? a human can easily answer them via acquire…
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
Michael Boratko, Harshit Padigela, Divyendra Mikkilineni +10
The recent work of Clark et al. introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set…