Optimizing Prompts for Minimal-Edit GEC
Researchers have developed an innovative approach to improve the performance of Large Language Models (LLMs) in correcting grammatical errors with minimal editing. Their method introduces taxonomy-based instructions and batching techniques, which significantly reduce overcorrections and enhance accuracy compared to fine-tuned models. The new prompt achieves a $F{0.5}$ score of 78.32 on the…
1 source primary source
Key points
- Introduced taxonomy-based instructions for minimal-edit constraints
- Batching multiple sentences improves edit rate across diverse LLM families
- Achieved $F{0.5}=78.32$ on BEA-2019 test set, new SOTA
Read the original at arXiv cs.CL · by Kateryna Karpo, Artem Chernodub primary source Open source ↗
Topics · follow one to build your own front page
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us.
More in Generative AI & Models
All →- AllSpark releases Iris-mini and Iris-pro, top open-weight search agents · 1 src
- OpenAI Unveils GPT‑6 Astra: Record‑Breaking 3D Rendering, Loop‑Transformer Architecture · 79 src
- Language Models Can't Detect Their Own Training Data · 1 src
- Intern-S2-397B: Hugging Face's New Multimodal Foundation Model · 1 src
- ContractEval: Improves Procedural Instruction Conformance · 1 src
Comments
via GitHub Discussions