Summary of Is It Good Data For Multilingual Instruction Tuning or Just Bad Multilingual Evaluation For Large Language Models?, by Pinzhen Chen et al.
Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models?
by Pinzhen Chen, Simon Yu, Zhicheng Guo, Barry Haddow
First submitted to arxiv on: 18 Jun 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 This research paper explores the limitations of current practices in designing and evaluating multilingual large language models, which claim to cater to speakers of varied languages. The study hypothesizes that fine-tuning and evaluation methods may not accurately align with this objective due to a reliance on translation, which cannot account for language-specific knowledge but can introduce defects. The paper investigates the impact of instruction data on model output and whether translated test sets capture nuances. Results show notable differences between native and translated instruction data, particularly when model performance is high, while other types of test sets do not reveal such disparities. Regularization is found to be beneficial in bridging this gap for structured tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we train and test language models that can understand many languages. Right now, people often use translations to help these models learn, but this might not work well because it’s hard to translate tricky language-specific knowledge. The researchers wanted to see if the way we train the models affects their performance, and if translated tests are good enough to check how well they’re doing. They found that using native language data makes a big difference, especially when the model is very good at understanding text. They also showed that adding some “noise” to the training process can help the model be more consistent. |
Keywords
» Artificial intelligence » Fine tuning » Regularization » Translation
