Summary of Tagengo: a Multilingual Chat Dataset, by Peter Devine
Tagengo: A Multilingual Chat Dataset
by Peter Devine
First submitted to arxiv on: 21 May 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 significant advancement in open-source large language models (LLMs). The authors create a vast dataset of over 70,000 prompt-response pairs in 74 languages, comprising human-generated prompts and synthetic responses. Using this dataset, they train a state-of-the-art English LLM to engage in multilingual conversations. Evaluating their model on MT-Bench chat benchmarks in six languages reveals that it outperforms previous open-source LLMs across each language. Furthermore, the study demonstrates that training on more multilingual data benefits performance in a target language (Japanese) compared to solely using data from that language. These findings emphasize the importance of large-scale, high-quality multilingual datasets for developing accessible LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers understand many languages better. The team created a huge collection of conversations in 74 languages, with questions and answers written by humans and computers. They used this data to train a computer program that can have conversations in multiple languages. When they tested the program on different languages, it did better than other similar programs. They also found that training the program on many languages helps it talk more naturally in one language (Japanese). Overall, this study shows how important it is to teach computers about many languages at once. |
Keywords
» Artificial intelligence » Prompt
