Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition
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| Format: | Preprint |
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2025
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| _version_ | 1866912567114334208 |
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| author | Shirke, Mayur Shembade, Amey Thorat, Pavan Wagh, Madhushri Joshi, Raviraj |
| author_facet | Shirke, Mayur Shembade, Amey Thorat, Pavan Wagh, Madhushri Joshi, Raviraj |
| contents | Named Entity Recognition (NER) in code-mixed text, particularly Hindi-English (Hinglish), presents unique challenges due to informal structure, transliteration, and frequent language switching. This study conducts a comparative evaluation of code-mixed fine-tuned models and non-code-mixed multilingual models, along with zero-shot generative large language models (LLMs). Specifically, we evaluate HingBERT, HingMBERT, and HingRoBERTa (trained on code-mixed data), and BERT Base Cased, IndicBERT, RoBERTa and MuRIL (trained on non-code-mixed multilingual data). We also assess the performance of Google Gemini in a zero-shot setting using a modified version of the dataset with NER tags removed. All models are tested on a benchmark Hinglish NER dataset using Precision, Recall, and F1-score. Results show that code-mixed models, particularly HingRoBERTa and HingBERT-based fine-tuned models, outperform others - including closed-source LLMs like Google Gemini - due to domain-specific pretraining. Non-code-mixed models perform reasonably but show limited adaptability. Notably, Google Gemini exhibits competitive zero-shot performance, underlining the generalization strength of modern LLMs. This study provides key insights into the effectiveness of specialized versus generalized models for code-mixed NER tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_02514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition Shirke, Mayur Shembade, Amey Thorat, Pavan Wagh, Madhushri Joshi, Raviraj Computation and Language Machine Learning Named Entity Recognition (NER) in code-mixed text, particularly Hindi-English (Hinglish), presents unique challenges due to informal structure, transliteration, and frequent language switching. This study conducts a comparative evaluation of code-mixed fine-tuned models and non-code-mixed multilingual models, along with zero-shot generative large language models (LLMs). Specifically, we evaluate HingBERT, HingMBERT, and HingRoBERTa (trained on code-mixed data), and BERT Base Cased, IndicBERT, RoBERTa and MuRIL (trained on non-code-mixed multilingual data). We also assess the performance of Google Gemini in a zero-shot setting using a modified version of the dataset with NER tags removed. All models are tested on a benchmark Hinglish NER dataset using Precision, Recall, and F1-score. Results show that code-mixed models, particularly HingRoBERTa and HingBERT-based fine-tuned models, outperform others - including closed-source LLMs like Google Gemini - due to domain-specific pretraining. Non-code-mixed models perform reasonably but show limited adaptability. Notably, Google Gemini exhibits competitive zero-shot performance, underlining the generalization strength of modern LLMs. This study provides key insights into the effectiveness of specialized versus generalized models for code-mixed NER tasks. |
| title | Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2509.02514 |