LLM中池化机制的对比分析：情感分析视角
Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis Perspective
AI总结：
本文从情感分析视角对比了Mean、Max和Weighted Sum等池化机制在BERT和GPT架构上的性能，发现不同机制各有优劣，强调需根据具体应用需求选择池化方法以优化下游任务。
摘要：
大型语言模型（LLMs）通过在各种任务中提供最先进的性能，彻底改变了自然语言处理（NLP）。其中，像BERT和GPT这样基于Transformer的模型依赖池化层将词元级嵌入聚合为句子级表示。诸如Mean、Max和Weighted Sum等常见池化机制在此聚合过程中发挥关键作用。尽管被广泛使用，这些策略在不同LLM架构上的比较性能仍被探讨不足。为填补这一空白，本文在句子级情感分析的背景下，研究了这些池化机制对两个突出的LLM家族——BERT和GPT的影响。综合实验表明，根据任务的具体要求，每种池化机制都展现出独特的优势和劣势。我们的发现强调了选择适合特定应用需求的池化方法的重要性，促使人们重新评估关于池化操作的常见假设。通过提供可操作的见解，本研究有助于优化基于LLM的下游任务模型。
英文摘要：
Large Language Models (LLMs) have revolutionized natural language processing (NLP) by delivering state-of-the-art performance across a variety of tasks. Among these, Transformer-based models like BERT and GPT rely on pooling layers to aggregate token-level embeddings into sentence-level representations. Common pooling mechanisms such as Mean, Max, and Weighted Sum play a pivotal role in this aggregation process. Despite their widespread use, the comparative performance of these strategies on different LLM architectures remains underexplored. To address this gap, this paper investigates the effects of these pooling mechanisms on two prominent LLM families -- BERT and GPT, in the context of sentence-level sentiment analysis. Comprehensive experiments reveal that each pooling mechanism exhibits unique strengths and weaknesses depending on the task's specific requirements. Our findings underline the importance of selecting pooling methods tailored to the demands of particular applications, prompting a re-evaluation of common assumptions regarding pooling operations. By offering actionable insights, this study contributes to the optimization of LLM-based models for downstream tasks.
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