9 citations · 19 across the 9 of their papers we have counts for
10 papers
CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientists
Junlin Yang, Dylan Zhang, Xiangchen Song +7
We introduce CausaLab, a scalable environment for evaluating interactive causal discovery by LLM agents. Unlike prior evaluations, CausaLab evaluates both whether an agent can solv…
Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning
Zhiwen You, Xi Chen, Aniket Vashishtha +5
Clinical diagnosis is a complex reasoning process in which clinicians gather evidence, form hypotheses, and test them against alternative explanations. In medical training, this re…
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…
Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar +3
Large Language Models (LLMs) have been used as experts to infer causal graphs, often by repeatedly applying a pairwise prompt that asks about the causal relationship of each variab…
On Evaluating and Mitigating Gender Biases in Multilingual Settings
Aniket Vashishtha, Kabir Ahuja, Sunayana Sitaram
While understanding and removing gender biases in language models has been a long-standing problem in Natural Language Processing, prior research work has primarily been limited to…
Vaccination Worldwide: Strategies, Distribution and Challenges
Chirag Samal, Kasia Jakimowicz, Krishnendu Dasgupta +26
The Coronavirus 2019 (Covid-19) pandemic caused by the SARS-CoV-2 virus represents an unprecedented crisis for our planet. It is a bane of the über connected world that we live in…
Mining Trends of COVID-19 Vaccine Beliefs on Twitter with Lexical Embeddings
Harshita Chopra, Aniket Vashishtha, Ridam Pal +3
Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompanies…
VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning
Raghav Awasthi, Keerat Kaur Guliani, Saif Ahmad Khan +7
A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy,…