Barrett Martin Lattimer
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View article: Sparse Rewards Can Self-Train Dialogue Agents
Sparse Rewards Can Self-Train Dialogue Agents Open
Recent advancements in state-of-the-art (SOTA) Large Language Model (LLM) agents, especially in multi-turn dialogue tasks, have been primarily driven by supervised fine-tuning and high-quality human feedback. However, as base LLM models co…
Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses Open
Detecting hallucinations in large language model (LLM) outputs is pivotal, yet traditional fine-tuning for this classification task is impeded by the expensive and quickly outdated annotation process, especially across numerous vertical do…
Fast and Accurate Factual Inconsistency Detection Over Long Documents Open
Generative AI models exhibit remarkable potential; however, hallucinations across various tasks present a significant challenge, particularly for longer inputs that current approaches struggle to address effectively. We introduce SCALE (So…
Human Inspired Progressive Alignment and Comparative Learning for Grounded Word Acquisition Open
Human language acquisition is an efficient, supervised, and continual process. In this work, we took inspiration from how human babies acquire their first language, and developed a computational process for word acquisition through compara…