In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinions

• URL: http://arxiv.org/abs/2410.11265v1
• Date: Tue, 15 Oct 2024 04:42:21 GMT
• Title: In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinions
• Authors: Alireza Shamshiri, Kyeong Rok Ryu, June Young Park,
• Abstract summary: This study evaluates the performance of three leading large language models: GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro. 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.
• Score: 2.974480694911691
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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