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arXiv 2606.24331cs.CLcs.ET
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基于Transformer的语言模型在垂直领域中的应用:架构、应用与批判性评估

Transformer-Based Language Models Across Domain Verticals: Architectures, Applications and Critical Assessment

SCOPE, VIT-AP University(VIT-AP大学SCOPE学院);SCORE, VIT(VIT大学SCORE学院)

Guruprakash J, Krithika L. B

AI总结:

本文系统梳理Transformer架构变体(编码器、解码器、长上下文等)及后2023年进展(指令微调、RLHF、MoE等),评估其在医疗、金融等领域的部署,并批判性分析模型选择、参数-能耗权衡及“SOTA”含义。

摘要:

基于Transformer的语言模型已成为自然语言处理的默认基础,新模型的快速发布使得从业者难以区分持久性想法与增量公告的噪音。本综述在两个层面展开。在机制层面,我们将主要的Transformer家族组织成一个工作分类,涵盖编码器-仅、解码器-仅、编码器-解码器、长上下文、基于排列和生成器-判别器变体。然后,我们将讨论扩展到2023年后改变实践格局的发展:指令微调、来自人类反馈的强化学习、直接偏好优化、混合专家扩展、检索增强以及来自OpenAI、Anthropic、Google、Meta、Mistral和DeepSeek的当前旗舰模型系列。在应用层面,我们调查了在医疗、金融、法律、教育、客户服务、创意写作和科学工作领域的部署。基于此,我们将每个领域与使Transformer成为合适工具的具体能力联系起来。本文的贡献是基于该综述的批判性评估。我们在对部署决策重要的四个轴上比较架构,量化参数数量与能源成本之间的权衡。我们还讨论了对齐方法、数据来源和基准饱和如何改变称模型为“最先进”的含义。最后一节列出了我们认为值得更多关注的研究问题。

英文摘要:

Transformer-based language models have become the default substrate for natural language processing and the pace of new releases has made it hard for practitioners to separate durable ideas from the noise of incremental announcements. This review works at two levels. At the level of mechanism, we organise the main transformer families into a working taxonomy, covering encoder-only, decoder-only, encoder-decoder, long-context, permutation-based, and generator-discriminator variants. We then extend the discussion to post-2023 developments that changed the picture in practice: instruction tuning, reinforcement learning from human feedback, direct preference optimisation, mixture-of-experts scaling, retrieval augmentation and the current flagship model families from OpenAI, Anthropic, Google, Meta, Mistral and DeepSeek. At the level of use, we survey deployments across healthcare, finance, legal, education, customer service, creative writing and scientific work. Based on this we link each to the specific capabilities that make a transformer the appropriate tool. The contribution of this paper is a critical assessment that is based on the survey. We compare architectures on four axes that matter to deployment decisions, we quantify the trade-off between parameter count and energy cost. We also discuss how alignment methods, data provenance and benchmark saturation change what it means to call a model "state of the art". The final section lists the research questions that we think deserve more attention.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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