Summary of On Limitations Of Llm As Annotator For Low Resource Languages, by Suramya Jadhav et al.
On Limitations of LLM as Annotator for Low Resource Languages
by Suramya Jadhav, Abhay Shanbhag, Amogh Thakurdesai, Ridhima Sinare, Raviraj Joshi
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 explores the challenges of developing accurate models and datasets for low-resource languages, such as Marathi, due to the lack of sufficient linguistic data and resources. It investigates the potential of Large Language Models (LLMs) like GPT-4o, Gemini 1.0 Pro, Gemma 2, and Llama 3.1 in generating datasets and resources for these underrepresented languages. The study evaluates the performance of both closed-source and open-source LLMs as annotators on classification tasks such as sentiment analysis, news classification, and hate speech detection, comparing them to fine-tuned BERT models. The findings reveal that while LLMs excel in annotation tasks for high-resource languages like English, they still fall short when applied to Marathi, highlighting the limitations of LLMs as annotators for low-resource languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to help languages with not enough data and tools. It wants to see if big language models can be used to make more data and tools for these languages. They tested some of these big models on a low-resource language called Marathi, doing things like sentiment analysis and hate speech detection. The results show that even the best big models aren’t good enough for this kind of work, and they’re not as good as using a special version of BERT. |
Keywords
» Artificial intelligence » Bert » Classification » Gemini » Gpt » Llama
