REVIEW 2 cited by
Cedille: A large autoregressive French language model
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Cedille: A large autoregressive French language model
read the original abstract
Scaling up the size and training of autoregressive language models has enabled novel ways of solving Natural Language Processing tasks using zero-shot and few-shot learning. While extreme-scale language models such as GPT-3 offer multilingual capabilities, zero-shot learning for languages other than English remain largely unexplored. Here, we introduce Cedille, a large open source auto-regressive language model, specifically trained for the French language. Our results show that Cedille outperforms existing French language models and is competitive with GPT-3 on a range of French zero-shot benchmarks. Furthermore, we provide an in-depth comparison of the toxicity exhibited by these models, showing that Cedille marks an improvement in language model safety thanks to dataset filtering.
Forward citations
Cited by 2 Pith papers
-
Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification
The paper proposes a four-stage self-verification prompting method but explicitly labels its experimental results as fabricated, so it cannot support its claimed gains.
-
Leveraging Vision-Language Large Models for Interpretable Video Action Recognition with Semantic Tokenization
LVLM-VAR transforms video into 'semantic action tokens' and uses a LoRA-tuned vision-language model to classify actions and generate explanations, reporting 94.1% on NTU RGB+D X-Sub.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.