NeurIPS 2024 Past Causality
Causality and Large Models @NeurIPS 2024
CaLM @NeurIPS 2024
- Submission deadline
- Sep 24, 2024, 11:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (30)
Fetched from OpenReview (v2) on 2026-06-10.
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A Causal Perspective in Brainwave Foundation Models
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Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries
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Are Police Biased? An NLP Approach
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Are UFOs Driving Innovation? The Illusion of Causality in Large Language Models
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Causal Interventions on Causal Paths: Mapping GPT-2's Reasoning From Syntax to Semantics
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Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
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Causal Reasoning in Large Language Models: A Knowledge Graph Approach
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Causal World Representation in the GPT Model
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CausalBench: A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models
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CausalGraph2LLM: Evaluating LLMs for Causal Queries
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Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias
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CodeSCM: Causal Analysis for Multi-Modal Code Generation
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Competence-Based Analysis of Language Models
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Counterfactual Causal Inference in Natural Language with Large Language Models
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Counterfactual Token Generation in Large Language Models
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Estimating Effects of Tokens in Preference Learning
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Evaluating Interventional Reasoning Capabilities of Large Language Models
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From Causal to Concept-Based Representation Learning
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From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations
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Hypothesizing Missing Causal Variables with LLMs
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Interactive Semantic Interventions for VLMs: A Causality-Inspired Investigation of VLM Failures
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Investigating Causal Reasoning in Large Language Models
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Investigating the Ability of Large Language Models to Explain Causal Relationships in Time Series Data
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Leveraging LLM-Generated Structural Prior for Causal Inference with Concurrent Causes
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LLM-initialized Differentiable Causal Discovery
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On Incorporating Prior Knowledge Extracted from Pre-trained Language Models into Causal Discovery
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On LLM Augmented AB Experimentation
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Reasoning with a Few Good Cross-Questions Greatly Enhances Causal Event Attribution in LLMs
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Teaching Transformers Causal Reasoning through Axiomatic Training
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Using Relational and Causality Context for Tasks with Specialized Vocabularies that are Challenging for LLMs