32 citations · 70 across the 9 of their papers we have counts for
12 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
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…
Entity Image and Mixed-Modal Image Retrieval Datasets
Cristian-Ioan Blaga, Paul Suganthan, Sahil Dua +6
Despite advances in multimodal learning, challenging benchmarks for mixed-modal image retrieval that combines visual and textual information are lacking. This paper introduces a no…
Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks
Paul Suganthan, Fedor Moiseev, Le Yan +7
Decoder-based transformers, while revolutionizing language modeling and scaling to immense sizes, have not completely overtaken encoder-heavy architectures in natural language proc…
SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives
Fedor Moiseev, Gustavo Hernandez Abrego, Peter Dornbach +3
Dual encoders have been used for retrieval tasks and representation learning with good results. A standard way to train dual encoders is using a contrastive loss with in-batch nega…
SKILL: Structured Knowledge Infusion for Large Language Models
Fedor Moiseev, Zhe Dong, Enrique Alfonseca +1
Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, it is largely unexplored whether they can better inter…
Exploring Dual Encoder Architectures for Question Answering
Zhe Dong, Jianmo Ni, Daniel M. Bikel +4
Dual encoders have been used for question-answering (QA) and information retrieval (IR) tasks with good results. Previous research focuses on two major types of dual encoders, Siam…
Prosody Modifications for Question-Answering in Voice-Only Settings
Aleksandr Chuklin, Aliaksei Severyn, Johanne Trippas +3
Many popular form factors of digital assistants---such as Amazon Echo, Apple Homepod, or Google Home---enable the user to hold a conversation with these systems based only on the s…
Eval all, trust a few, do wrong to none: Comparing sentence generation models
Ondřej Cífka, Aliaksei Severyn, Enrique Alfonseca +1
In this paper, we study recent neural generative models for text generation related to variational autoencoders. Previous works have employed various techniques to control the prio…
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca +1
Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related q…
Fast k-best Sentence Compression
Katja Filippova, Enrique Alfonseca
A popular approach to sentence compression is to formulate the task as a constrained optimization problem and solve it with integer linear programming (ILP) tools. Unfortunately, d…
Evaluation of the DiversiNews diversified news service
Daniele Pighin, Enrique Alfonseca, Felix Leif Keppmann +1
In this report we present the outcome of an extensive evaluation of the DiversiNews platform [8, 10] for diversified browsing of news, developed in the scope of the RENDER project.…