Beyond In-Context Learning: Aligning Long-form Generation of Large
  Language Models via Task-Inherent Attribute Guidelines
Authors: name: Do Xuan Long, name: Duong Ngoc Yen, name: Do Xuan Trong, name: Luu Anh Tuan, name: Kenji Kawaguchi, name: Shafiq Joty, name: Min-Yen Kan, name: Nancy F. Chen

In-context learning (ICL) is an important yet not fully understood ability of
pre-trained large language models (LLMs). It can greatly enhance task
performance using a few examples, termed demonstrations, without fine-tuning.
Although effective in question answering, ICL often underperforms in long-form
generation tasks such as summarization. Under appropriately realistic
assumptions, we empirically and theoretically show that ICL demonstrations
alone are insufficient to teach LLMs the task language and format distributions
for generation. We argue for explicit exposure to the task distributions and
hypothesize that defining them by prompting enhances model performance. To this
end, we present LongGuide, which efficiently generates two parallel streams of
guidelines capturing task language and format properties: (i) Metric Guidelines
(MGs) that instruct models to optimize self-evaluated metrics; and (ii) Output
Constraint Guidelines (OCGs) that constrain generation at both token and
sentence levels. LongGuide automatically selects the best combination of
guidelines, improving both strong open- and closed-source LLMs by over 5% in
both zero- and few-shot settings. We show that LongGuide is generalizable,
learnable by weak models to enhance strong ones, and integrates synergistically
with automatic prompt optimizers.