Summary of Can Perplexity Predict Fine-tuning Performance? An Investigation Of Tokenization Effects on Sequential Language Models For Nepali, by Nishant Luitel et al.
Can Perplexity Predict Fine-Tuning Performance? An Investigation of Tokenization Effects on Sequential Language Models for Nepali
by Nishant Luitel, Nirajan Bekoju, Anand Kumar Sah, Subarna Shakya
First submitted to arxiv on: 28 Apr 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 This paper explores the impact of subwording mechanisms on the understanding capacity of language models. Unlike previous studies that only examined a few languages, this research uses six different tokenization schemes to train small language models in Nepali and evaluates their performance on various downstream tasks. The results show that SentencePiece outperforms byte-level BPE algorithms like GPT and RoBERTa for finetuning performances in Nepali. Additionally, the study pretrains and fine-tunes sequential transformer-based language models instead of Bert-based models. This research contributes to filling the gap in understanding how subwording affects language model performance across different languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how language models can handle words they haven’t seen before during testing. They used special techniques to train small language models for Nepali, a language spoken in Nepal, and tested them on various tasks. The results show that one technique is better than others at improving the performance of these language models. This research helps us understand how language models work and can be applied to other languages. |
Keywords
» Artificial intelligence » Bert » Gpt » Language model » Tokenization » Transformer
