Summary of Youtube Comments Decoded: Leveraging Llms For Low Resource Language Classification, by Aniket Deroy et al.
YouTube Comments Decoded: Leveraging LLMs for Low Resource Language Classification
by Aniket Deroy, Subhankar Maity
First submitted to arxiv on: 6 Nov 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 presents a shared task on sarcasm detection and sentiment analysis in code-mixed texts, specifically in Tamil-English and Malayalam-English languages. The task aims to identify sarcasm and sentiment polarity within social media comments and posts. To address the challenges posed by class imbalance and code-mixing, the authors experiment with state-of-the-art large language models like GPT-3.5 Turbo via prompting to classify comments into sarcastic or non-sarcastic categories. The results show a macro-F1 score of 0.61 for Tamil language and 0.50 for Malayalam language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about trying to understand when people are being sarcastic online, especially when they’re mixing languages like Tamil and English. This is hard because sarcasm can be tricky to spot, and adding different languages makes it even harder. The authors created a special dataset with lots of examples of code-mixed texts (where people mix languages in the same sentence) and asked AI models to figure out if the sentences were meant to be sarcastic or not. They used a really good AI model called GPT-3.5 Turbo to help them do this, and it seemed to work pretty well! |
Keywords
» Artificial intelligence » F1 score » Gpt » Prompting
