activity
20222026
most citedMMTEB: Massive Multilingual Text Embedding Benchmark

15 citations · 54 across the 17 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CL2025★ 2 cited

Olmo 3

Team Olmo, :, Allyson Ettinger +66

We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function…

cs.CL2025

FilBench: Can LLMs Understand and Generate Filipino?

Lester James V. Miranda, Elyanah Aco, Conner Manuel +2

Despite the impressive performance of LLMs on English-based tasks, little is known about their capabilities in specific languages such as Filipino. In this work, we address this ga…

cs.CL2025

The UD-NewsCrawl Treebank: Reflections and Challenges from a Large-scale Tagalog Syntactic Annotation Project

Angelina A. Aquino, Lester James V. Miranda, Elsie Marie T. Or

This paper presents UD-NewsCrawl, the largest Tagalog treebank to date, containing 15.6k trees manually annotated according to the Universal Dependencies framework. We detail our t…

cs.CL2025★ 1 cited

R3: Robust Rubric-Agnostic Reward Models

David Anugraha, Zilu Tang, Lester James V. Miranda +5

Reward models are essential for aligning language model outputs with human preferences, yet existing approaches often lack both controllability and interpretability. These models a…

cs.CL2025★ 15 cited

MMTEB: Massive Multilingual Text Embedding Benchmark

Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83

Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…

cs.CL2025★ 2 cited

2 OLMo 2 Furious

Team OLMo, Pete Walsh, Luca Soldaini +40

We present OLMo 2, the next generation of our fully open language models. OLMo 2 includes a family of dense autoregressive language models at 7B, 13B and 32B scales with fully rele…