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20232025
most citedBaichuan 2: Open Large-scale Language Models

127 citations · 132 across the 13 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CV2024

Beyond Sight: Towards Cognitive Alignment in LVLM via Enriched Visual Knowledge

Yaqi Zhao, Yuanyang Yin, Lin Li +7

Does seeing always mean knowing? Large Vision-Language Models (LVLMs) integrate separately pre-trained vision and language components, often using CLIP-ViT as vision backbone. Howe…

cs.LG2024

Baichuan Alignment Technical Report

Mingan Lin, Fan Yang, Yanjun Shen +21

We introduce Baichuan Alignment, a detailed analysis of the alignment techniques employed in the Baichuan series of models. This represents the industry's first comprehensive accou…

cs.AI2024

Baichuan-Omni Technical Report

Yadong Li, Haoze Sun, Mingan Lin +23

The salient multimodal capabilities and interactive experience of GPT-4o highlight its critical role in practical applications, yet it lacks a high-performing open-source counterpa…

cs.CL2024

BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline

Guosheng Dong, Da Pan, Yiding Sun +17

The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several ins…

cs.CL2024★ 2 cited

PAS: Data-Efficient Plug-and-Play Prompt Augmentation System

Miao Zheng, Hao Liang, Fan Yang +16

In recent years, the rise of Large Language Models (LLMs) has spurred a growing demand for plug-and-play AI systems. Among the various AI techniques, prompt engineering stands out…

cs.CV2024

MathScape: Benchmarking Multimodal Large Language Models in Real-World Mathematical Contexts

Hao Liang, Linzhuang Sun, Minxuan Zhou +7

With the rapid progress of Multimodal LLMs, evaluating their mathematical reasoning capabilities has become an increasingly important research direction. In particular, visual-text…

cs.CL2024

CFBench: A Comprehensive Constraints-Following Benchmark for LLMs

Tao Zhang, Chenglin Zhu, Yanjun Shen +10

The adeptness of Large Language Models (LLMs) in comprehending and following natural language instructions is critical for their deployment in sophisticated real-world applications…