most citedCharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

3 citations · 5 across the 4 of their papers we have counts for

collaborators

6 papers

astro-ph.IM20251 cited

AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy

Jinghang Shi, Xiaoyu Tang, Yang Huang +4

Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…

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.LG20251 cited

TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology

Alexis Chevalier, Soumya Ghosh, Urvi Awasthi +15

Understanding the biological mechanisms of disease is crucial for medicine, and in particular, for drug discovery. AI-powered analysis of genome-scale biological data holds great p…

cs.IR2024

LitSearch: A Retrieval Benchmark for Scientific Literature Search

Anirudh Ajith, Mengzhou Xia, Alexis Chevalier +3

Literature search questions, such as "Where can I find research on the evaluation of consistency in generated summaries?" pose significant challenges for modern search engines and…

cs.CL20243 cited

CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

Zirui Wang, Mengzhou Xia, Luxi He +10

Chart understanding plays a pivotal role when applying Multimodal Large Language Models (MLLMs) to real-world tasks such as analyzing scientific papers or financial reports. Howeve…

cs.CL2024

Language Models as Science Tutors

Alexis Chevalier, Jiayi Geng, Alexander Wettig +19

NLP has recently made exciting progress toward training language models (LMs) with strong scientific problem-solving skills. However, model development has not focused on real-life…