NeurIPS 2024PastCausality

Causality and Large Models @NeurIPS 2024

CaLM @NeurIPS 2024

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Submission deadline: Sep 24, 2024, 11:59 UTC
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Submission portal: OpenReview
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Accepted papers (30)

Fetched from OpenReview (v2) on 2026-06-10.

A Causal Perspective in Brainwave Foundation Models
Konstantinos Barmpas, Yannis Panagakis, Dimitrios Adamos, Nikolaos Laskaris, Stefanos Zafeiriou · PDF
Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries
Roberto Ceraolo, Dmitrii Kharlapenko, Amélie Reymond, Rada Mihalcea, Bernhard Schölkopf, Mrinmaya Sachan, Zhijing Jin · PDF
Are Police Biased? An NLP Approach
Jonathan H. Choi · PDF
Are UFOs Driving Innovation? The Illusion of Causality in Large Language Models
María Victoria Carro, Francisca Gauna Selasco, Denise Alejandra Mester, Mario Leiva · PDF
Causal Interventions on Causal Paths: Mapping GPT-2's Reasoning From Syntax to Semantics
Isabelle Lee, Joshua Lum, Ziyi Liu, Dani Yogatama · PDF
Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N. Balasubramanian, Amit Sharma · PDF
Causal Reasoning in Large Language Models: A Knowledge Graph Approach
Yejin Kim, Eojin Kang, Juae Kim, H. Howie Huang · PDF
Causal World Representation in the GPT Model
Raanan Yehezkel Rohekar, Yaniv Gurwicz, Sungduk Yu, Vasudev Lal · PDF
CausalBench: A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models
ZEYU WANG · PDF
CausalGraph2LLM: Evaluating LLMs for Causal Queries
Ivaxi Sheth, Bahare Fatemi, Mario Fritz · PDF
Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias
Yuen Chen, Vethavikashini Chithrra Raghuram, Justus Mattern, Rada Mihalcea, Zhijing Jin · PDF
CodeSCM: Causal Analysis for Multi-Modal Code Generation
Mukur Gupta, Noopur Bhatt, Suman Jana · PDF
Competence-Based Analysis of Language Models
Adam Davies, Jize Jiang, ChengXiang Zhai · PDF
Counterfactual Causal Inference in Natural Language with Large Language Models
Gael Gendron, Joze M. Rozanec, Michael Witbrock, Gillian Dobbie · PDF
Counterfactual Token Generation in Large Language Models
Ivi Chatzi, Nina L. Corvelo Benz, Eleni Straitouri, Stratis Tsirtsis, Manuel Gomez Rodriguez · PDF
Estimating Effects of Tokens in Preference Learning
Hsiao-Ru Pan, Maximilian Mordig, Bernhard Schölkopf · PDF
Evaluating Interventional Reasoning Capabilities of Large Language Models
Tejas Kasetty, Divyat Mahajan, Gintare Karolina Dziugaite, Alexandre Drouin, Dhanya Sridhar · PDF
From Causal to Concept-Based Representation Learning
Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Kumar Ravikumar · PDF
From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations
Shan Shan · PDF
Hypothesizing Missing Causal Variables with LLMs
Ivaxi Sheth, Sahar Abdelnabi, Mario Fritz · PDF
Interactive Semantic Interventions for VLMs: A Causality-Inspired Investigation of VLM Failures
Lukas Klein, Kenza Amara, Carsten T. Lüth, Hendrik Strobelt, Mennatallah El-Assady, Paul F Jaeger · PDF
Investigating Causal Reasoning in Large Language Models
Atul Rawal, Adrienne Raglin, Qianlong Wang, Ziying Tang · PDF
Investigating the Ability of Large Language Models to Explain Causal Relationships in Time Series Data
Elizabeth Healey, Isaac S. Kohane · PDF
Leveraging LLM-Generated Structural Prior for Causal Inference with Concurrent Causes
Xingjian Zhang, Shixuan Liu, Yixin Wang, Qiaozhu Mei · PDF
LLM-initialized Differentiable Causal Discovery
Shiv Kampani, David Hidary, Constantijn van der Poel, Martin Ganahl, Brenda Miao · PDF
On Incorporating Prior Knowledge Extracted from Pre-trained Language Models into Causal Discovery
Chanhui Lee, Juhyeon Kim, YongJun Jeong, Yoonseok Yeom, Juhyun Lyu, Jung-Hee Kim, Sangmin Lee, Sangjun Han, Hyeokjun Choe, Soyeon Park, Woohyung Lim, Kyunghoon Bae, Sungbin Lim, Sanghack Lee · PDF
On LLM Augmented AB Experimentation
Shiv Shankar, Ritwik Sinha, Madalina Fiterau · PDF
Reasoning with a Few Good Cross-Questions Greatly Enhances Causal Event Attribution in LLMs
Sanyam Saxena, Sunita Sarawagi · PDF
Teaching Transformers Causal Reasoning through Axiomatic Training
Aniket Vashishtha, Abhinav Kumar, Atharva Pandey, Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian, Amit Sharma · PDF
Using Relational and Causality Context for Tasks with Specialized Vocabularies that are Challenging for LLMs
Ryosuke Nakanishi, Yan-Ying Chen, Francine Chen, Matthew Klenk, Charlene C. Wu · PDF

Accepted papers (30)

☆A Causal Perspective in Brainwave Foundation Models

☆Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries

☆Are Police Biased? An NLP Approach

☆Are UFOs Driving Innovation? The Illusion of Causality in Large Language Models

☆Causal Interventions on Causal Paths: Mapping GPT-2's Reasoning From Syntax to Semantics

☆Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference

☆Causal Reasoning in Large Language Models: A Knowledge Graph Approach

☆Causal World Representation in the GPT Model

☆CausalBench: A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models

☆CausalGraph2LLM: Evaluating LLMs for Causal Queries

☆Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias

☆CodeSCM: Causal Analysis for Multi-Modal Code Generation

☆Competence-Based Analysis of Language Models

☆Counterfactual Causal Inference in Natural Language with Large Language Models

☆Counterfactual Token Generation in Large Language Models

☆Estimating Effects of Tokens in Preference Learning

☆Evaluating Interventional Reasoning Capabilities of Large Language Models

☆From Causal to Concept-Based Representation Learning

☆From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations

☆Hypothesizing Missing Causal Variables with LLMs

☆Interactive Semantic Interventions for VLMs: A Causality-Inspired Investigation of VLM Failures

☆Investigating Causal Reasoning in Large Language Models

☆Investigating the Ability of Large Language Models to Explain Causal Relationships in Time Series Data

☆Leveraging LLM-Generated Structural Prior for Causal Inference with Concurrent Causes

☆LLM-initialized Differentiable Causal Discovery

☆On Incorporating Prior Knowledge Extracted from Pre-trained Language Models into Causal Discovery

☆On LLM Augmented AB Experimentation

☆Reasoning with a Few Good Cross-Questions Greatly Enhances Causal Event Attribution in LLMs

☆Teaching Transformers Causal Reasoning through Axiomatic Training

☆Using Relational and Causality Context for Tasks with Specialized Vocabularies that are Challenging for LLMs

A Causal Perspective in Brainwave Foundation Models

Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries

Are Police Biased? An NLP Approach

Are UFOs Driving Innovation? The Illusion of Causality in Large Language Models

Causal Interventions on Causal Paths: Mapping GPT-2's Reasoning From Syntax to Semantics

Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference

Causal Reasoning in Large Language Models: A Knowledge Graph Approach

Causal World Representation in the GPT Model

CausalBench: A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models

CausalGraph2LLM: Evaluating LLMs for Causal Queries

Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias

CodeSCM: Causal Analysis for Multi-Modal Code Generation

Competence-Based Analysis of Language Models

Counterfactual Causal Inference in Natural Language with Large Language Models

Counterfactual Token Generation in Large Language Models

Estimating Effects of Tokens in Preference Learning

Evaluating Interventional Reasoning Capabilities of Large Language Models

From Causal to Concept-Based Representation Learning

From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations

Hypothesizing Missing Causal Variables with LLMs

Interactive Semantic Interventions for VLMs: A Causality-Inspired Investigation of VLM Failures

Investigating Causal Reasoning in Large Language Models

Investigating the Ability of Large Language Models to Explain Causal Relationships in Time Series Data

Leveraging LLM-Generated Structural Prior for Causal Inference with Concurrent Causes

LLM-initialized Differentiable Causal Discovery

On Incorporating Prior Knowledge Extracted from Pre-trained Language Models into Causal Discovery

On LLM Augmented AB Experimentation

Reasoning with a Few Good Cross-Questions Greatly Enhances Causal Event Attribution in LLMs

Teaching Transformers Causal Reasoning through Axiomatic Training

Using Relational and Causality Context for Tasks with Specialized Vocabularies that are Challenging for LLMs