7 citations · 9 across the 7 of their papers we have counts for
3 papers · 1 filter
Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks
Ashutosh Srivastava, Lokesh Nagalapatti, Gautam Jajoo +3
Recent claims of strong performance by Large Language Models (LLMs) on causal discovery are undermined by a key flaw: many evaluations rely on benchmarks likely included in pretrai…
Executable Counterfactuals: Improving LLMs' Causal Reasoning Through Code
Aniket Vashishtha, Qirun Dai, Hongyuan Mei +3
Counterfactual reasoning, a hallmark of intelligence, consists of three steps: inferring latent variables from observations (abduction), constructing alternatives (interventions),…
Teaching Transformers Causal Reasoning through Axiomatic Training
Aniket Vashishtha, Abhinav Kumar, Atharva Pandey +4
For text-based AI systems to interact in the real world, causal reasoning is an essential skill. Since active interventions are costly, we study to what extent a system can learn c…