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20202026
most citedWinner Takes It All: Training Performant RL Populations for Combinatorial Optimization

6 citations · 21 across the 21 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

Shieldstral

Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273

We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…

cs.CL2026

LKValues: Aligning Large Language Models with Sri Lankan Societal Values

Nethmi Muthugala, Supryadi, Surangika Ranathunga +7

Value alignment of Large Language Models (LLMs) has been shown to be culturally biased toward Western norms. This results in the mishandling of local values in multilingual societi…

cs.CL2026★ 1 cited

Ministral 3

Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian +116

We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes:…

cs.CL2025

Command A: An Enterprise-Ready Large Language Model

Team Cohere, :, Aakanksha +227

In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…

cs.CL2025★ 1 cited

Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study

Menglong Cui, Pengzhi Gao, Wei Liu +2

Large language models (LLMs) have shown continuously improving multilingual capabilities, and even small-scale open-source models have demonstrated rapid performance enhancement. I…

cs.CL2024

Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier

John Dang, Shivalika Singh, Daniel D'souza +42

We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly perf…

cs.CL2023★ 5 cited

Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs

Andries Smit, Paul Duckworth, Nathan Grinsztajn +2

Recent advancements in large language models (LLMs) underscore their potential for responding to inquiries in various domains. However, ensuring that generative agents provide accu…