Summary of How Much Data Is Enough Data? Fine-tuning Large Language Models For In-house Translation: Performance Evaluation Across Multiple Dataset Sizes, by Inacio Vieira et al.
How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes
by Inacio Vieira, Will Allred, Séamus Lankford, Sheila Castilho, Andy Way
First submitted to arxiv on: 5 Sep 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 The paper explores the effectiveness of fine-tuning Large Language Models (LLMs) using translation memories (TMs) to enhance accuracy and efficiency in machine translation. Specifically, it investigates the impact of fine-tuning the Llama 3 model using TMs from a software sector organization on five translation directions across languages with varying resource levels. The study analyzes the influence of different-sized training datasets (1k to 207k segments) on translation quality and evaluates performance based on automatic metrics such as BLEU, chrF++, TER, and COMET. Results show that larger datasets lead to improved translation performance, with a notable increase in BLEU and COMET scores using the largest training set. However, smaller training sets (1k-2k examples) lead to a performance deterioration compared to the baseline model. The study highlights the potential of integrating TMs with LLMs for creating bespoke translation models tailored to specific organizational needs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make machine translation better by using big language models and special memory books that help with translations. They took a big language model called Llama 3 and taught it some new skills by giving it lots of examples from a company in the software industry. They tried this on five different languages and found out that the more examples they gave the model, the better it got at translating. However, if they only gave it a few examples, the model didn’t do as well. This is important because companies want to be able to translate things quickly and accurately, so using these big language models with special memory books could help them do that. |
Keywords
» Artificial intelligence » Bleu » Fine tuning » Language model » Llama » Translation
