Can Large Language Models Understand Context?

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本文建立了一个上下文理解基准,通过四个任务和九个数据集测试大型语言模型(LLM)理解上下文的能力。实验显示,预训练的LLM在处理微妙的上下文特征时表现不佳,且3-bit后训练量化会降低其上下文理解性能。此基准为LLM评估提供了新的视角,补充了现有评估方法。

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本文是LLM系列文章,针对《Can Large Language Models Understand Context?》的翻译。

摘要

理解上下文是理解人类语言的关键,大型语言模型(LLM)越来越多地被视为在令人印象深刻的程度上展示了这一能力。然而,尽管LLM的评估涵盖了自然语言处理领域内的各个领域,但对探究其理解上下文特征的语言能力的关注有限。本文通过调整现有数据集以适应生成模型的评估,引入了一个上下文理解基准。该基准测试由四个不同的任务和九个数据集组成,所有这些都具有旨在评估模型理解上下文能力的提示。首先,我们评估了LLM在上下文内学习预训练场景下的性能。实验结果表明,与最先进的微调模型相比,预训练的密集模型难以理解更细微的上下文特征。其次,随着LLM压缩在研究和现实世界的应用中具有越来越重要的意义,我们评估了在上下文学习环境下量化模型的上下文理解。我们发现,在我们的基准测试中,3-bit后训练量化会导致不同程度的性能下降。我们对这些场景进行了广泛的分析,以证实我们的实验结果。

1 引言

2 相关工作

3 任务选择与设计

4 实验

5 用例研究:查询重写

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### LLaMA Model Improvements in AI and Machine Learning The advancements seen with models such as ChatGPT indicate significant progressions within artificial intelligence (AI) and machine learning (ML), particularly concerning large language models (LLMs). While specific details about LLaMA (Large Language Model Meta AI) may not be directly provided by the given references, insights into similar models can offer valuable context. #### Enhanced Architectural Design Improvements to LLaMA involve refining its architectural design. This includes optimizing transformer layers for better performance while reducing computational overheads. Such enhancements allow LLaMA to process larger datasets efficiently without compromising on speed or accuracy[^2]. #### Improved Training Techniques Advancements also come from improved training techniques that enable faster convergence during optimization processes. These methods ensure stable gradients throughout backpropagation cycles, leading to higher quality outputs when generating text or performing other natural language processing tasks. #### Expanded Context Window Another notable improvement is expanding the context window size beyond previous limitations. A wider context allows LLaMA to understand longer sequences of input data effectively, thereby improving coherence over extended conversations or document analysis scenarios where broader contextual understanding plays a crucial role. #### Multimodal Capabilities Integration Integrating multimodal capabilities represents another frontier explored under recent developments. By combining textual information with visual elements like images or videos, these enhanced versions aim at delivering richer interactions between humans and machines across diverse applications ranging from education platforms to virtual assistants. ```python # Example Python code demonstrating how one might interact with an advanced version of LLaMA API. import llama_api response = llama_api.generate_text(prompt="Explain quantum computing.", max_tokens=100) print(response['text']) ```
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