OpenAI Enhances Language Models with Fill-in-the-Middle Training: A Path to Advanced Infilling Capabilities

Transformer-based language models, like BERT and T5, are adept at various tasks but struggle with infilling—generating text within a specific location while considering both preceding and succeeding contexts. Though encoder-decoder models can handle suffixes, their training data typically includes shorter infill regions than practical ones. However, causal decoder-based models, such as GPT-3 and its successors, […]

The post OpenAI Enhances Language Models with Fill-in-the-Middle Training: A Path to Advanced Infilling Capabilities appeared first on MarkTechPost.

adept advanced ai paper summary ai shorts applications artificial intelligence bert capabilities causal data decoder editors pick encoder encoder-decoder however language language model language models large language model location openai path practical staff struggle tasks tech news technology text training training data transformer