Summary of In-context Learning For Long-context Sentiment Analysis on Infrastructure Project Opinions, by Alireza Shamshiri et al.
In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinions
by Alireza Shamshiri, Kyeong Rok Ryu, June Young Park
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the performance of three large language models (LLMs) – GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro – on complex documents about infrastructure projects under zero-shot and few-shot scenarios. The results show that GPT-4o excels in simpler documents, while Claude 3.5 Sonnet handles more complex opinions better. In few-shot scenarios, Claude 3.5 Sonnet outperforms overall, with GPT-4o showing greater stability as the number of demonstrations increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at three big language models (GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro) that are really good at understanding documents about building projects. The study checks how well these models do when they don’t have any training and when they only get a little bit of practice. The results show that one model (GPT-4o) is better with simple documents, while another model (Claude 3.5 Sonnet) does better with more complicated opinions. |
Keywords
» Artificial intelligence » Claude » Few shot » Gemini » Gpt » Zero shot
