5 papers
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
Javier González, Risa Ueno, Cliff Wong +12
The rapid digitization of real-world data presents an unprecedented opportunity to optimize healthcare delivery and accelerate biomedical discovery. However, these data are often f…
RE-IMAGINE: Symbolic Benchmark Synthesis for Reasoning Evaluation
Xinnuo Xu, Rachel Lawrence, Kshitij Dubey +7
Recent Large Language Models (LLMs) have reported high accuracy on reasoning benchmarks. However, it is still unclear whether the observed results arise from true reasoning or from…
Compositional Causal Reasoning Evaluation in Language Models
Jacqueline R. M. A. Maasch, Alihan Hüyük, Xinnuo Xu +2
Causal reasoning and compositional reasoning are two core aspirations in AI. Measuring the extent of these behaviors requires principled evaluation methods. We explore a unified pe…
Reasoning Elicitation in Language Models via Counterfactual Feedback
Alihan Hüyük, Xinnuo Xu, Jacqueline Maasch +2
Despite the increasing effectiveness of language models, their reasoning capabilities remain underdeveloped. In particular, causal reasoning through counterfactual question answeri…
Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models
Javier González, Aditya V. Nori
Recent advances in AI have been significantly driven by the capabilities of large language models (LLMs) to solve complex problems in ways that resemble human thinking. However, th…