A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models (2025)

Abstract: Multi-Aspect Controllable Text Generation (MCTG) introduces fine-grained multiple constraints in natural language generation, i.e. control attributes in topics, sentiments, and detoxification.MCTG demonstrates application prospects for trustworthy generation of Large Language Models (LLMs) but is limited by generalization issues.Existing work exploits additional structures and strategies for solutions, requiring LLMs’ modifications.To activate LLMs’ MCTG ability, we propose a lightweight MCTG pipeline based on data augmentation and instruction tuning.We analyze aspect bias and correlations in traditional datasets and address these concerns with augmented control attributes and sentences.Augmented datasets are feasible for instruction tuning.We conduct experiments for various LLMs backbone and parameter sizes, demonstrating general effectiveness on MCTG performance.

## introduction multi-aspect controlled text generation @xcite is an emerging natural language generation task. mctg alleviates multiple constraints (e.g. detoxification requirements) in language generation and contributes to a secure, faithful, and trustworthy generation. existing methods @xcite @xcite mainly focus on additional structures or decoding procedures, limiting extrapolation to llms. due to enormous parameters and complex inference processes, refactoring llms with existing methods is unavailable in terms of cost and performance. instruction tuning (it) on target datasets is a general solution for various llm tasks, e.g. role playing @xcite , mathe-matical @xcite . however, mctg suffers from the absence of high-quality it datasets. existing work @xcite relies on combinations of single-aspect datasets for supervised learning, which fails to achieve the ideal performance due to issues like aspects bias and correlations @xcite . from the perspective of datasets, we propose a lightweight mctg solution for llms. we analyze concerns in existing mctg datasets and address them with an llm-based data augmentation pipeline. first, we delve into control attributes and sentences in existing datasets and analyze potential concerns for aspect bias and correlations. then, we construct a data augmentation pipeline to produce augmented datasets. we provide mechanisms to ensure the effectiveness and quality of augmentation. the data format is conveniently consistent with it datasets. consequently, data augmentation is beneficial to common llms without specific structures. we validate the effectiveness on various scales and the backbone of our llms. the result shows that the augmented dataset contributes to the performance of mctg, especially in aspect de-biasing and overall accuracy among 3 aspects. ## methodology as shown in fig. 1 , we first analyze 3 representative concerns in existing mctg datasets, then propose an llm-based data augmentation pipeline correspondingly, and finally transform augmented datasets for instruction tuning of llms. ## discussion aspect correlations to demonstrate aspect correlations learned by llms, we record predicted attribute distribution and their mutual information (mi) @xcite . we calculate the mi of all three aspects and each two of them, results are shown in ## conclusion in this work, we construct a lightweight mctg solution for llms. we analyze concerns in original mctg datasets and provide an llm-based data augmentation pipeline for better mctg instructiontuning, including generating cross labels, finegrained label descriptions and rewriting heterogeneous sentences for target aspects. in experiments, training llm with augmented data exhibits enhanced and balanced performances among aspects.