LLaSO: A Foundational Framework for Reproducible Research in Large
  Language and Speech Model
Authors: name: Yirong Sun, name: Yizhong Geng, name: Peidong Wei, name: Yanjun Chen, name: Jinghan Yang, name: Rongfei Chen, name: Wei Zhang, name: Xiaoyu Shen

The development of Large Speech-Language Models (LSLMs) has been slowed by
fragmented architectures and a lack of transparency, hindering the systematic
comparison and reproducibility of research. Unlike in the vision-language
domain, the LSLM field suffers from the common practice of releasing model
weights without their corresponding training data and configurations. To
address these critical gaps, we introduce LLaSO, the first fully open,
end-to-end framework for large-scale speech-language modeling. LLaSO provides
the community with three essential resources: (1) LLaSO-Align, a 12M-instance
speech-text alignment corpus; (2) LLaSO-Instruct, a 13.5M-instance multi-task
instruction-tuning dataset; and (3) LLaSO-Eval, a reproducible benchmark for
standardized evaluation. To validate our framework, we build and release
LLaSO-Base, a 3.8B-parameter reference model trained exclusively on our public
data. It achieves a normalized score of 0.72, establishing a strong,
reproducible baseline that surpasses comparable models. Our analysis reveals
that while broader training coverage enhances performance, significant
generalization gaps persist on unseen tasks, particularly in pure audio
scenarios. By releasing the complete stack of data, benchmarks, and models,
LLaSO establishes a foundational open standard to unify research efforts and
accelerate community-driven progress in LSLMs. We release the code, dataset,
pretrained models, and results in https://github.com/EIT-NLP/LLaSO.