MEGA XStoryCloze
XStoryCloze as part of the MEGA benchmark suite. A cross-lingual story completion task that consists of professionally translated versions of the English StoryCloze dataset to 10 non-English languages. Requires models to predict the correct ending for a given four-sentence story, evaluating commonsense reasoning and narrative understanding.
Phi-3.5-MoE-instruct from Microsoft currently leads the MEGA XStoryCloze leaderboard with a score of 0.828 across 2 evaluated AI models.
What MEGA XStoryCloze measures
MEGA XStoryCloze is a text benchmark that evaluates large language models on language and reasoning tasks. LLM Stats tracks 2 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.8.
Compare leaders on the best AI for language and best AI for reasoning leaderboards.
Publication
- Paper
- MEGA: Multilingual Evaluation of Generative AI
- Authors
- Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, and 8 others
- Published
- arXiv
- 2303.12528
Abstract
Generative AI models have shown impressive performance on many Natural Language Processing tasks such as language understanding, reasoning, and language generation. An important question being asked by the AI community today is about the capabilities and limits of these models, and it is clear that evaluating generative AI is very challenging. Most studies on generative LLMs have been restricted to English and it is unclear how capable these models are at understanding and generating text in other languages. We present the first comprehensive benchmarking of generative LLMs - MEGA, which evaluates models on standard NLP benchmarks, covering 16 NLP datasets across 70 typologically diverse languages. We compare the performance of generative LLMs including Chat-GPT and GPT-4 to State of the Art (SOTA) non-autoregressive models on these tasks to determine how well generative models perform compared to the previous generation of LLMs. We present a thorough analysis of the performance of models across languages and tasks and discuss challenges in improving the performance of generative LLMs on low-resource languages. We create a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field.
Phi-3.5-MoE-instruct leads with 82.8%, followed by Phi-3.5-mini-instruct at 73.5%.
Progress Over Time
Interactive timeline showing model performance evolution on MEGA XStoryCloze
MEGA XStoryCloze Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Microsoft | 60B | — | — | ||
| 2 | Microsoft | 4B | — | — |
FAQ
Common questions about MEGA XStoryCloze.
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