mlsanityData to 14 Oct 2026

TigerBot: An Open Multilingual Multitask LLM

arXiv 2312.08688 ↗First posted 14 Dec 2023cs.CL cs.AIcited by 15

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Abstract (arXiv)

We release and introduce the TigerBot family of large language models (LLMs), consisting of base and chat models, sized from 7, 13, 70 and 180 billion parameters. We develop our models embarking from Llama-2 and BLOOM, and push the boundary further in data, training algorithm, infrastructure, and application tools. Our models yield meaningful performance gain over SOTA open-source models, e.g., Llama-2, specifically 6% gain in English and 20% gain in Chinese. TigerBot model family also achieves leading performance in major academic and industrial benchmarks and leaderboards. We believe that TigerBot represents just a snapshot of lightning-fast progression in LLM open-source community. Therefore, we are thrilled to give back by publicly releasing our models and reporting our approach behind, with additional emphases on building SOTA LLMs in a democratized way and making LLMs of use in real-world applications.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 24 of its 31 references that are in the map. First posted on arXiv 14 Dec 2023.

Builds on

24 of 31 references.

References · 24

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12 more
  1. 2020GLU Variants Improve Transformercited by 1,804
  2. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
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  4. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  5. 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 632
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Citing papers per year: 2024: 9; 2025: 5; 2026: 1.

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