6 papers
FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models
Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee +5
Large language models (LLMs) have achieved remarkable success in generative tasks, yet they often fall short in ensuring the factual accuracy of their outputs, thus limiting their…
SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models
Debarun Bhattacharjya, Balaji Ganesan, Junkyu Lee +4
When does a large language model (LLM) know what it does not know? Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM…
Multilinear and Linear Programs for Partially Identifiable Queries in Quasi-Markovian Structural Causal Models
João P. Arroyo, João G. Rodrigues, Daniel Lawand +6
We investigate partially identifiable queries in a class of causal models. We focus on acyclic Structural Causal Models that are quasi-Markovian (that is, each endogenous variable…
The Consistency Hypothesis in Uncertainty Quantification for Large Language Models
Quan Xiao, Debarun Bhattacharjya, Balaji Ganesan +5
Estimating the confidence of large language model (LLM) outputs is essential for real-world applications requiring high user trust. Black-box uncertainty quantification (UQ) method…
Q-function Decomposition with Intervention Semantics with Factored Action Spaces
Junkyu Lee, Tian Gao, Elliot Nelson +3
Many practical reinforcement learning environments have a discrete factored action space that induces a large combinatorial set of actions, thereby posing significant challenges. E…
Rationalization Models for Text-to-SQL
Gaetano Rossiello, Nhan Pham, Michael Glass +2
We introduce a framework for generating Chain-of-Thought (CoT) rationales to enhance text-to-SQL model fine-tuning. These rationales consist of intermediate SQL statements and expl…