Sign up to save your podcasts
Or
Share The Devil is in the Errors: Leveraging LLMs for Fine-grained Machine Translation Evaluation
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
The Devil is in the Errors: Leveraging LLMs for Fine-grained Machine Translation Evaluation
The paper proposes AutoMQM, a technique that uses large language models to identify and categorize errors in machine translations, improving performance and providing interpretability compared to score-based evaluation methods.
https://arxiv.org/abs//2308.07286
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
View all episodes
By Igor Melnyk
5
33 ratings
The paper proposes AutoMQM, a technique that uses large language models to identify and categorize errors in machine translations, improving performance and providing interpretability compared to score-based evaluation methods.
https://arxiv.org/abs//2308.07286
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
More shows like Arxiv Papers
View all
Exchanges
958 Listeners
Odd Lots
1,977 Listeners
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
438 Listeners
The Daily
112,858 Listeners
All-In with Chamath, Jason, Sacks & Friedberg
10,073 Listeners
Hard Fork
5,535 Listeners
UnHerd with Freddie Sayers
214 Listeners
Unsupervised Learning with Jacob Effron
51 Listeners
Latent Space: The AI Engineer Podcast
98 Listeners
BG2Pod with Brad Gerstner and Bill Gurley
473 Listeners