3 citations · 3 across the 6 of their papers we have counts for
6 papers
First-Order In-Context Shapley: Scalable Data Auditing for LLM Alignment and Evaluation
Yunting Song, Matthew Watson, Moustafa Farid Alzantot +2
Post-training alignment of Large Language Models is increasingly bottlenecked by data quality: preference and instruction-tuning corpora accumulate label noise, safety inversions,…
Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning
Shenao Zhang, Yaqing Wang, Yinxiao Liu +5
Large Language Models (LLMs) trained via Reinforcement Learning (RL) have exhibited strong reasoning capabilities and emergent reflective behaviors, such as rethinking and error co…
Revisiting Funnel Transformers for Modern LLM Architectures with Comprehensive Ablations in Training and Inference Configurations
DongHyun Choi, Lucas Spangher, Chris Hidey +2
Transformer-based Large Language Models, which suffer from high computational costs, advance so quickly that techniques proposed to streamline earlier iterations are not guaranteed…
Factored Agents: Decoupling In-Context Learning and Memorization for Robust Tool Use
Nicholas Roth, Christopher Hidey, Lucas Spangher +6
In this paper, we propose a novel factored agent architecture designed to overcome the limitations of traditional single-agent systems in agentic AI. Our approach decomposes the ag…
Project MPG: towards a generalized performance benchmark for LLM capabilities
Lucas Spangher, Tianle Li, William F. Arnold +6
There exists an extremely wide array of LLM benchmarking tasks, whereas oftentimes a single number is the most actionable for decision-making, especially by non-experts. No such ag…
Improving Multi-Agent Debate with Sparse Communication Topology
Yunxuan Li, Yibing Du, Jiageng Zhang +4
Multi-agent debate has proven effective in improving large language models quality for reasoning and factuality tasks. While various role-playing strategies in multi-agent debates…