StyleAdaptedLM: Enhancing Instruction Following Models with Efficient Stylistic Transfer
This addresses the challenge of stylistic personalization for enterprise communication, offering an efficient method but is incremental as it builds on existing LoRA techniques.
The paper tackled the problem of adapting LLMs to specific stylistic characteristics like brand voice without compromising instruction adherence, and introduced StyleAdaptedLM, a framework using LoRA adapters trained on unstructured corpora and merged with instruction-following models, achieving improved stylistic consistency and preserved task performance as confirmed by human evaluations.
Adapting LLMs to specific stylistic characteristics, like brand voice or authorial tones, is crucial for enterprise communication but challenging to achieve from corpora which lacks instruction-response formatting without compromising instruction adherence. We introduce StyleAdaptedLM, a framework that efficiently transfers stylistic traits to instruction-following models using Low-Rank Adaptation (LoRA). LoRA adapters are first trained on a base model with diverse unstructured stylistic corpora, then merged with a separate instruction-following model. This enables robust stylistic customization without paired data or sacrificing task performance. Experiments across multiple datasets and models demonstrate improved stylistic consistency while preserving instruction adherence, with human evaluations confirming brand-specific convention uptake. StyleAdaptedLM offers an efficient path for stylistic personalization in LLMs.