Summary of 1-800-shared-tasks @ Nlu Of Devanagari Script Languages: Detection Of Language, Hate Speech, and Targets Using Llms, by Jebish Purbey et al.
1-800-SHARED-TASKS @ NLU of Devanagari Script Languages: Detection of Language, Hate Speech, and Targets using LLMs
by Jebish Purbey, Siddartha Pullakhandam, Kanwal Mehreen, Muhammad Arham, Drishti Sharma, Ashay Srivastava, Ram Mohan Rao Kadiyala
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents a system description of our entry for the CHiPSAL 2025 shared task, focusing on language detection, hate speech identification, and target detection in Devanagari script languages. We experimented with large language models like MuRIL, IndicBERT, Gemma-2, and their ensembles to address challenges in multilingual processing and class imbalance. Our approach achieved competitive results across all tasks: F1 of 0.9980, 0.7652, and 0.6804 for Sub-tasks A, B, and C respectively. This work provides insights into the effectiveness of transformer models in domain-specific and linguistic challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a computer program that can detect languages, spot hate speech, and find specific targets in Indian languages like Hindi and others. It uses special AI models to make it better at understanding these languages. The results are really good, showing the program can do well on different tasks. This work shows how these AI models can be used for certain types of challenges. |
Keywords
» Artificial intelligence » Transformer
