most citedGemma 3 Technical Report

65 citations · 67 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

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…

cs.CL2025

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…

cs.LG2025★ 1 cited

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…

cs.CL2025★ 65 cited

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…

cs.CL2025★ 1 cited

ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer

Omer Goldman, Uri Shaham, Dan Malkin +11

To achieve equitable performance across languages, large language models (LLMs) must be able to abstract knowledge beyond the language in which it was learnt. However, the current…