Hwan Kim. Chang

Continual alignment requires LLMs to adapt to new requirements without forgetting previously acquired behaviors. Natural-language instructions are flexible and composable but offer only indirect control, whereas post-training provides stronger adaptation at the cost of repeated parameter updates. We introduce Ready2Blend, which combines the flexibility of natural language with learned alignment.

Published on Aug 14 · Submitted by HWANCHANG on Aug 20 Abstract A framework that separates thematic seeds from genre-form controls generates diverse, high-quality creative writing data across 13 genres and improves LLM creative writing performance. High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the structural and functional conventions of diverse creative formats.