9 papers
Diffusion Language Model Inference with Monte Carlo Tree Search
Zheng Huang, Kiran Ramnath, Yueyan Chen +8
Diffusion language models (DLMs) have recently emerged as a compelling alternative to autoregressive generation, offering parallel generation and improved global coherence. During…
Learning to Ideate for Machine Learning Engineering Agents
Yunxiang Zhang, Kang Zhou, Zhichao Xu +5
Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. To address this, we introduce MLE-Ideator, a dua…
SALT: Step-level Advantage Assignment for Long-horizon Agents via Trajectory Graph
Jiazheng Li, Yawei Wang, David Yan +5
Large Language Models (LLMs) have demonstrated remarkable capabilities, enabling language agents to excel at single-turn tasks. However, their application to complex, multi-step, a…
PromptPrism: A Linguistically-Inspired Taxonomy for Prompts
Sullam Jeoung, Yueyan Chen, Yi Zhang +3
Prompts are the interface for eliciting the capabilities of large language models (LLMs). Understanding their structure and components is critical for analyzing LLM behavior and op…
Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models
Sangmin Woo, Kang Zhou, Yun Zhou +4
Large Vision Language Models (LVLMs) often suffer from object hallucination, which undermines their reliability. Surprisingly, we find that simple object-based visual prompting --…
A Systematic Survey of Automatic Prompt Optimization Techniques
Kiran Ramnath, Kang Zhou, Sheng Guan +18
Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. Ho…