65 citations · 68 across the 11 of their papers we have counts for
11 papers
FoodSense: A Multisensory Food Dataset and Benchmark for Predicting Taste, Smell, Texture, and Sound from Images
Sabab Ishraq, Aarushi Aarushi, Juncai Jiang +1
Humans routinely infer taste, smell, texture, and even sound from food images a phenomenon well studied in cognitive science. However, prior vision language research on food has fo…
TranslateGemma Technical Report
Mara Finkelstein, Isaac Caswell, Tobias Domhan +18
We present TranslateGemma, a suite of open machine translation models based on the Gemma 3 foundation models. To enhance the inherent multilingual capabilities of Gemma 3 for the t…
Mind the Gap... or Not? How Translation Errors and Evaluation Details Skew Multilingual Results
Jan-Thorsten Peter, David Vilar, Tobias Domhan +2
Most current large language models (LLMs) support a wide variety of languages in addition to English, including high-resource languages (e.g. German, Chinese, French), as well as l…
Feeding Two Birds or Favoring One? Adequacy-Fluency Tradeoffs in Evaluation and Meta-Evaluation of Machine Translation
Behzad Shayegh, Jan-Thorsten Peter, David Vilar +4
We investigate the tradeoff between adequacy and fluency in machine translation. We show the severity of this tradeoff at the evaluation level and analyze where popular metrics fal…
Assay2Mol: large language model-based drug design using BioAssay context
Yifan Deng, Spencer S. Ericksen, Anthony Gitter
Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, molecule screening assays evaluate candidate molecules' functional res…
Gemma 3 Technical Report
Gemma Team, Aishwarya Kamath, Johan Ferret +209
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…
You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation
Gergely Flamich, David Vilar, Jan-Thorsten Peter +1
The goal of translation, be it by human or by machine, is, given some text in a source language, to produce text in a target language that simultaneously 1) preserves the meaning o…
Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data
Mara Finkelstein, David Vilar, Markus Freitag
Recent research in neural machine translation (NMT) has shown that training on high-quality machine-generated data can outperform training on human-generated data. This work accomp…
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
Firas Trabelsi, David Vilar, Mara Finkelstein +1
Minimum Bayes Risk (MBR) decoding is a powerful decoding strategy widely used for text generation tasks, but its quadratic computational complexity limits its practical application…
There's no Data Like Better Data: Using QE Metrics for MT Data Filtering
Jan-Thorsten Peter, David Vilar, Daniel Deutsch +3
Quality Estimation (QE), the evaluation of machine translation output without the need of explicit references, has seen big improvements in the last years with the use of neural me…
Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model
Christian Tomani, David Vilar, Markus Freitag +5
Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy for neural machine translation (NMT) models. The underlying assumption is that model probability corre…