6 papers
From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization
Ying Chang, Jiahang Xu, Xuan Feng +3
The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to diagnose agent failures and i…
Prompt Orchestration Markup Language
Yuge Zhang, Nan Chen, Jiahang Xu +1
Large Language Models (LLMs) require sophisticated prompting, yet current practices face challenges in structure, data integration, format sensitivity, and tooling. Existing method…
Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization
Yuanye Liu, Jiahang Xu, Li Lyna Zhang +6
Large Language Models (LLMs) have shown significant capability across various tasks, with their real-world effectiveness often driven by prompt design. While recent research has fo…
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Marah Abdin, Jyoti Aneja, Hany Awadalla +126
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal test…
Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
Zhenting Qi, Mingyuan Ma, Jiahang Xu +3
This paper introduces rStar, a self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superio…
VisEval: A Benchmark for Data Visualization in the Era of Large Language Models
Nan Chen, Yuge Zhang, Jiahang Xu +2
Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implemen…