3 papers
cs.LG2026
Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models
Julian Skirzynski, Harry Cheon, Shreyas Kadekodi +2
Concept bottleneck models predict outcomes from high-level concepts detected in inputs. Although concepts provide a simple way to reap benefits from interpretability, very few data…
cs.CL2025
Quantifying Cognitive Bias Induction in LLM-Generated Content
Abeer Alessa, Param Somane, Akshaya Lakshminarasimhan +3
Large language models (LLMs) are integrated into applications like shopping reviews, summarization, or medical diagnosis support, where their use affects human decisions. We invest…
cs.LG2020
Automatic Discovery of Interpretable Planning Strategies
Julian Skirzyński, Frederic Becker, Falk Lieder
When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-…