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cs.CL2025
Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs
Juncheng Xie, Hung-yi Lee
Controlling the length of text produced by large language models (LLMs) remains challenging: models frequently overshoot or undershoot explicit length instructions because they can…
cs.CL2024
Non-instructional Fine-tuning: Enabling Instruction-Following Capabilities in Pre-trained Language Models without Instruction-Following Data
Juncheng Xie, Shensian Syu, Hung-yi Lee
Instruction fine-tuning is crucial for today's large language models (LLMs) to learn to follow instructions and align with human preferences. Conventionally, supervised data, inclu…