In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinions
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Abstract (arXiv)
Large language models (LLMs) have achieved impressive results across various tasks. However, they still struggle with long-context documents. This study evaluates the performance of three leading LLMs: GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro on lengthy, complex, and opinion-varying documents concerning infrastructure projects, under both zero-shot and few-shot scenarios. Our results indicate that GPT-4o excels in zero-shot scenarios for simpler, shorter documents, while Claude 3.5 Sonnet surpasses GPT-4o in handling more complex, sentiment-fluctuating opinions. In few-shot scenarios, Claude 3.5 Sonnet outperforms overall, while GPT-4o shows greater stability as the number of demonstrations increases.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 38 references that are in the map. First posted on arXiv 15 Oct 2024.
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References · 17
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
- 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
- 2023Extending Context Window of Large Language Models via Positional Interpolationcited by 721
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5 more
- 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Taskscited by 37
- 2023Large language models for aspect-based sentiment analysiscited by 17
- 2024Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)cited by 7
- 2020Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language Modelcited by 6
- 2024KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Modelscited by 6
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