CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
cs.LG
Submitted: 2024-04-09
Updated: 2026-09-07
Comments: Accepted for publication in IEEE Transactions on Artificial Intelligence
Project page: https://www.bnlearn.com
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
- Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
- Large Language Models and Causal Inference in Collaboration: A Survey
- Enhancing Causal Reasoning in Large Language Models: A Causal Attribution Model for Precision Fine-Tuning
- Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
- Causal Discovery with Language Models as Imperfect Experts
- Can large language models build causal graphs?
- Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs
- Understanding Causality with Large Language Models: Feasibility and Opportunities
- Can Large Language Models Infer Causation from Correlation?
- Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis
- Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models
- Efficient Causal Graph Discovery Using Large Language Models
- Zero-shot Causal Graph Extrapolation from Text via LLMs
- LLaMA: Open and Efficient Foundation Language Models
- OPT: Open Pre-trained Transformer Language Models
- The Falcon Series of Open Language Models
- Mitigating Prior Errors in Causal Structure Learning: A Resilient Approach via Bayesian Networks
- Pre-trained Summarization Distillation
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