2 citations · 4 across the 5 of their papers we have counts for
5 papers
CAPO: Constraint-Aware Prompt Optimization for LLM Agents
Victor Ye Dong, Reid Pryzant, Yi Liu +1
Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks. Such deployments impose distinct operational requireme…
Aligning the Query Space: Greedy Information Projection for Language Model Data Selection
Victor Ye Dong, Kuan-Yun Lee, Jiamei Shuai +3
Data selection for language models is often framed as balancing example quality and diversity. We argue that both are consequences of a more fundamental principle: selected example…
Semi-Offline Reinforcement Learning for Optimized Text Generation
Changyu Chen, Xiting Wang, Yiqiao Jin +5
In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore the environment at significant time co…
FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training
Junyi Chai, Reid Pryzant, Victor Ye Dong +3
Controllable text generation systems often leverage control codes to direct various properties of the output like style and length. Inspired by recent work on causal inference for…
DeepGen: Diverse Search Ad Generation and Real-Time Customization
Konstantin Golobokov, Junyi Chai, Victor Ye Dong +5
We present DeepGen, a system deployed at web scale for automatically creating sponsored search advertisements (ads) for BingAds customers. We leverage state-of-the-art natural lang…