Controllable Text Generation for Large Language Models: A Survey
Source
- Person key:
ysymyth - Source kind:
paper - Canonical URL: https://arxiv.org/abs/2408.12599
- License:
NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
5f591214301ffecbe35a9faee19742c593c7a8dbdb321fe893202421b969fadb - First seen: 2026-05-16
- Last changed: 2026-05-16
- Identity guard: Do not confuse with yao-shunyu-alfred, the physics-to-AI researcher at alfredyao.github.io.
Classification
- Category: Tool use / computer use / digital automation
- Topic hub: shunyu-yao-public-corpora
- Project taxonomy: shunyu-yao-project-taxonomy
- Paper map: shunyu-yao-paper-map
Summary
In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world applications, LLMs must meet increasingly complex requirements. Beyond avoiding misleading or inappropriate content, LLMs are also expected to cater to specific user needs, such as imitating particular writing styles or generating text with poetic richness. These varied demands have driven the development of Controllable Text Generation (CTG) techniques, which ensure that outputs…
What This Teaches
How to evaluate and build agents that interact with real software, web environments, and user-facing tasks.
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