6 citations · 14 across the 9 of their papers we have counts for
6 papers · 1 filter
Composing Policy Gradients and Prompt Optimization for Language Model Programs
Noah Ziems, Dilara Soylu, Lakshya A Agrawal +10
Group Relative Policy Optimization (GRPO) has proven to be an effective tool for post-training language models (LMs). However, AI systems are increasingly expressed as modular prog…
GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
Lakshya A Agrawal, Shangyin Tan, Dilara Soylu +14
Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often requir…
Optimizing Decomposition for Optimal Claim Verification
Yining Lu, Noah Ziems, Hy Dang +1
Current research on the \textit{Decompose-Then-Verify} paradigm for evaluating the factuality of long-form text typically treats decomposition and verification in isolation, overlo…
TOWER: Tree Organized Weighting for Evaluating Complex Instructions
Noah Ziems, Zhihan Zhang, Meng Jiang
Evaluating the ability of large language models (LLMs) to follow complex human-written instructions is essential for their deployment in real-world applications. While benchmarks l…
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection
Noah Ziems, Gang Liu, John Flanagan +1
Network intrusion detection (NID) systems which leverage machine learning have been shown to have strong performance in practice when used to detect malicious network traffic. Deci…
Large Language Models are Built-in Autoregressive Search Engines
Noah Ziems, Wenhao Yu, Zhihan Zhang +1
Document retrieval is a key stage of standard Web search engines. Existing dual-encoder dense retrievers obtain representations for questions and documents independently, allowing…