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TeleChat Technical Report

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arxiv 2401.03804 v2 pith:7X7X2LT5 submitted 2024-01-08 cs.CL cs.AI

Zhongjiang He , Zihan Wang , Xinzhang Liu , Shixuan Liu , Yitong Yao , Yuyao Huang , Xuelong Li , Yongxiang Li
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Zhonghao Che Zhaoxi Zhang Yan Wang Xin Wang Luwen Pu Huinan Xu Ruiyu Fang Yu Zhao Jie Zhang Xiaomeng Huang Zhilong Lu Jiaxin Peng Wenjun Zheng Shiquan Wang Bingkai Yang Xuewei he Zhuoru Jiang Qiyi Xie Yanhan Zhang Zhongqiu Li Lingling Shi Weiwei Fu Yin Zhang Zilu Huang Sishi Xiong Yuxiang Zhang Chao Wang Shuangyong Song
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classification cs.CLcs.AI
keywords telechatmodelsbillionlanguagecodecollectionfine-tunedhuman
verification ladder T0 review T1 audit T2 compute T3 formal
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In this technical report, we present TeleChat, a collection of large language models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It includes pretrained language models as well as fine-tuned chat models that is aligned with human preferences. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, including trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves comparable performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat's 7B and 12B variant, along with code and a portion of our pretraining data, to the public community.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-Objective Exploration and Preference Optimization via Mutual Information

    cs.CL 2026-07 unverdicted novelty 6.0 of 10

    MI-EPO maximizes joint conditional mutual information among responses, feedback, and preference vectors, using probabilistic routing to improve alignment and controllability in multi-objective LLM optimization.

  2. TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering

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    TableZoomer compresses tables into schemas, zooms to query-relevant regions, and executes generated Python to answer questions, lifting Qwen3-8B accuracy by 19.34 points on DataBench and 25 points on TableBench Fact Checking.

  3. AI Flow: Perspectives, Scenarios, and Approaches

    cs.AI 2025-06 conditional novelty 5.0 of 10

    AI Flow proposes to combine device-edge-cloud deployment, feature-aligned model families, and multi-model collaboration to make large AI models cheaper, faster, and more widely accessible.

  4. Technical Report of TeleChat2, TeleChat2.5 and T1

    cs.CL 2025-07 conditional novelty 4.0 of 10

    The released T1-115B open-weight model outperforms OpenAI's o1-mini and GPT-4o on MATH500, AlignBench, and IFEval, despite using a standard dense transformer architecture.

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