4 papers
Evaluating Causal Reasoning in LLMs: A Case Study in the Challenges of Procedural Generation
Justin Clarke, Isha Karn, Patrick Murray +2
Large language models (LLMs) appear to represent and reason about a large range of phenomena. In particular, recent studies have examined whether LLMs can accurately reason about c…
Challenges in Understanding Modality Conflict in Vision-Language Models
Trang Nguyen, Jackson Michaels, Madalina Fiterau +1
This paper highlights the challenge of decomposing conflict detection from conflict resolution in Vision-Language Models (VLMs) and presents potential approaches, including using a…
Improving Generative Methods for Causal Evaluation via Simulation-Based Inference
Pracheta Amaranath, Vinitra Muralikrishnan, Amit Sharma +1
Generating synthetic datasets that accurately reflect real-world observational data is critical for evaluating causal estimators, but it remains a challenging task. Existing genera…
Compositional Models for Estimating Causal Effects
Purva Pruthi, David Jensen
Many real-world systems can be usefully represented as sets of interacting components. Examples include computational systems, such as query processors and compilers, natural syste…