随着大语言模型在人工智能领域的持续突破，各科技巨头和研究机构不断推出性能卓越的大型模型。这些模型在不同领域和任务中展现出了惊人的语言智能和学习能力。然而，如何最大化利用这些模型的互补优势，以实现更高效、更准确的处理任务，成为了当前研究的焦点。
近期，哈尔滨工业大学社会计算与信息检索研究中心和鹏城实验室的研究人员提出了一种创新性框架——DeePEn（无需训练的异构大模型集成学习框架）。DeePEn的出现为大模型的集成学习提供了新的视角和方法，旨在挖掘和利用不同模型间的互补性，以提升整体性能。
不同于传统方法依赖于额外的训练模块来筛选、融合多个模型生成的回复，DeePEn在解码过程中采取了一种更为直接和高效的方式。它在模型输出的概率分布层面进行融合，通过联合决定每一步的输出token，从而在不进行额外训练的情况下，实现了模型间的高效集成。
这一创新框架的提出，标志着大模型集成学习领域的重要进展。它不仅为当前大模型的利用提供了新的思路，而且为学术界和产业界在大模型应用、多语言处理和复杂任务解决方面提供了强大的工具。DeePEn的出现，不仅有助于推动学术交流与传播，也预示着在AI研究的前沿，集成学习技术将发挥越来越重要的作用。
如果您有关于大模型集成学习、多语言大模型的研究成果或创新想法，欢迎投稿至AIxiv专栏，与全球的学术界和产业界共享您的研究成果。通过分享和交流，共同促进人工智能领域的创新发展。投稿邮箱：liyazhou@jiqizhixin.com；zhaoyunfeng@jiqizhixin.com。
### 结语
随着AI技术的不断进步，大模型集成学习框架如DeePEn的诞生，不仅为人工智能领域注入了新的活力，也为解决复杂问题提供了更为高效、灵活的解决方案。这一创新不仅有助于提升现有模型的性能，还为未来人工智能技术的应用和研究开辟了广阔的道路。让我们期待更多类似的创新成果，共同推动人工智能技术的前沿发展。
英语如下：
### The Cutting Edge of AI: Integrating Heterogeneous Large Models, DeePEn Innovation Framework Paces New Breakthroughs in Language Technology
As large language models continue to push the boundaries in the field of artificial intelligence, tech giants and research institutions are consistently unveiling sophisticated large-scale models that demonstrate exceptional linguistic intelligence and learning capabilities across various domains and tasks. However, the challenge lies in how to harness the complementary strengths of these models to achieve more efficient and accurate task processing. This is currently a focal point in ongoing research.
Recently, researchers from the Social Computing and Information Retrieval Center at Harbin Institute of Technology and the Peng Cheng Laboratory have introduced an innovative framework named DeePEn (Deep Ensemble for Heterogeneous Large Model Integration Learning). This framework offers a new perspective and methodology for the integration of large models, aiming to leverage the complementarity among different models to enhance overall performance.
Contrary to traditional approaches that rely on additional training modules to sift and fuse responses generated by multiple models, DeePEn takes a more direct and efficient approach during the decoding process. It integrates the models’ output probability distributions, making joint decisions on each step’s output token, thus achieving the integration of models without the need for extra training.
The introduction of this innovative framework marks a significant advancement in the domain of large model integration learning. Not only does it provide new insights into the utilization of current large models, but it also offers a powerful tool for academia and industry in the application of large models, multilingual processing, and tackling complex tasks. The emergence of DeePEn not only facilitates academic exchanges and dissemination but also signals the growing importance of integration learning techniques at the forefront of AI research.
If you have research outcomes or innovative ideas regarding large model integration learning or multilingual large models, we welcome your contributions to the AIxiv专栏. Share your findings with the global academic and industrial communities to foster innovation in the field of artificial intelligence. Please submit your articles to liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com.
### Conclusion
As AI technology advances, the advent of frameworks like DeePEn not only infuses new vitality into the AI domain but also provides more efficient and flexible solutions to complex problems. This innovation not only helps to enhance the performance of existing models but also paves the way for future applications and research in AI technology. Let us look forward to more such innovative achievements and work together to drive the development of AI technology to its forefront.
【来源】https://www.jiqizhixin.com/articles/2024-07-19
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