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

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

astro-ph.IM2025★ 1 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…

eess.AS2024

Analyzing and Mitigating Inconsistency in Discrete Audio Tokens for Neural Codec Language Models

Wenrui Liu, Zhifang Guo, Jin Xu +4

Building upon advancements in Large Language Models (LLMs), the field of audio processing has seen increased interest in training audio generation tasks with discrete audio token s…