Summary of Chatgpt Vs Gemini Vs Llama on Multilingual Sentiment Analysis, by Alessio Buscemi and Daniele Proverbio
ChatGPT vs Gemini vs LLaMA on Multilingual Sentiment Analysis
by Alessio Buscemi, Daniele Proverbio
First submitted to arxiv on: 25 Jan 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 proposed study focuses on evaluating the performance of Large Language Model (LLM)-based models like ChatGPT, Gemini, or LLaMA2 in automated sentiment analysis. The researchers construct nuanced and ambiguous scenarios in 10 languages, predict their associated sentiment using popular LLMs, and validate the results against post-hoc human responses. The study highlights significant biases and inconsistent performance across models and evaluated human languages, emphasizing the need for a standardized methodology for evaluating automated sentiment analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated sentiment analysis is used to understand people’s emotions from text messages. But when the text is ambiguous or ironic, it can be difficult to determine the correct sentiment. Researchers tested different language models like ChatGPT and Gemini to see how well they could predict sentiments in various languages. They found that while these models did a good job with some texts, there were significant biases and inconsistencies across models and languages. This study aims to provide a better way to evaluate automated sentiment analysis and improve the performance of algorithms. |
Keywords
» Artificial intelligence » Gemini » Large language model
