Summary of Machine Translation Hallucination Detection For Low and High Resource Languages Using Large Language Models, by Kenza Benkirane et al.
Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Models
by Kenza Benkirane, Laura Gongas, Shahar Pelles, Naomi Fuchs, Joshua Darmon, Pontus Stenetorp, David Ifeoluwa Adelani, Eduardo Sánchez
First submitted to arxiv on: 23 Jul 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 presents a study on detecting hallucinations in Machine Translation (MT) systems, focusing on Large Language Models (LLMs) and semantic similarity within massively multilingual embeddings. The authors evaluate sentence-level hallucination detection approaches using LLMs across 16 language directions, covering High-Resource Languages (HRLs) and Low-Resource Languages (LRLs). They find that the choice of model is crucial for performance, with Llama3-70B outperforming the previous state-of-the-art on HRLs by up to 0.16 MCC and Claude Sonnet outperforming other LLMs on average by 0.03 MCC on LRLs. The study demonstrates that LLMs can achieve comparable or even better performance than previously proposed models, but their advantage is less pronounced for LRLs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to detect mistakes in machine translation systems. It focuses on using big language models and comparing them with other approaches. The researchers tested these methods across 16 different languages, including some that are hard to work with because they have limited data. They found that the choice of model is important for getting good results, especially when working with languages that have a lot of data. However, even the best models don’t perform as well when working with languages that have very little data. |
Keywords
» Artificial intelligence » Claude » Hallucination » Translation
