| Challenge: | Large language models (LLMs) are prone to various forms of safety risks, such as learning and propagating societal biases and even creating harmful or deceptive content through jailbreak attacks. |
| Approach: | They propose a framework for causally tracing multiple components simultaneously that systematically identifies the subsets of components most critical to a desired performance metric. |
| Outcome: | The proposed method outperforms existing methods in identifying components critical to a desired performance metric. |
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Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
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| Approach: | They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality. |
| Outcome: | The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks. |
Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery (2025.findings-emnlp)
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| Challenge: | Current causal discovery methods rely on pairwise or iterative strategies that fail to capture global dependencies, amplify local biases, and reduce overall accuracy. |
| Approach: | They propose a framework for one-step full causal graph discovery using prompt-based discovery and a data-driven method for settings without metadata. |
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Causal Inference with Large Language Model: A Survey (2025.findings-naacl)
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| Challenge: | Existing causal inference frameworks do not match human judgment in several key areas, such as domain knowledge, logical inference, and cultural context. |
| Approach: | They propose to apply large language models to causal inference tasks . they summarize the main causal problems and approaches and compare their results . |
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METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Existing benchmarks evaluate contextual causal reasoning in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy. |
| Approach: | They use a unified context to benchmark large language models' contextual causal reasoning skills. |
| Outcome: | The proposed benchmarks show that LLMs are susceptible to distraction by irrelevant but factually correct information at lower level of causality. |
DoubleLingo: Causal Estimation with Large Language Models (2024.naacl-short)
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| Challenge: | Existing methods for causal estimation are inadequate for noisy text data. |
| Approach: | They propose to use LLM-based nuisance models to estimate causal effects from non-randomized data using assumptions about the underlying data distribution. |
| Outcome: | The proposed method reduces the relative absolute error by 10.4% over existing methods on the best available dataset. |
CodeSCM: Causal Analysis for Multi-Modal Code Generation (2025.naacl-long)
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| Challenge: | Prior work has shown that multimodal prompts can be highly sensitive, where small adjustments might result in drastically different responses from the model. |
| Approach: | They propose a Structural Causal Model (SCM) for analyzing multi-modal code generation using large language models (LLMs). |
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Multimodal Causal Reasoning Benchmark: Challenging Multimodal Large Language Models to Discern Causal Links Across Modalities (2025.findings-acl)
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| Challenge: | Existing MLLMs lack robustness in multimodal causal reasoning compared to their performance in textual settings. |
| Approach: | They propose a novel multimodal chain-of-thought (CoT) reasoning benchmark that leverages siamese images and text pairs to challenge MLLMs. |
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CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models (2025.emnlp-main)
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| Challenge: | Existing fine-tuning paradigms focus on aligning LLMs with task-specific objectives. |
| Approach: | They propose a pipeline that leverages human priors to automatically generate token-level causal signals and introduce the Re-Attention mechanism to guide training. |
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Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective (2024.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models have facilitated the development of Multimodal LLMs. |
| Approach: | They propose a causal framework to interpret unimodal biases in visual question answering problems and a framework to integrate information from different modalities and mitigate biase. |
| Outcome: | The proposed framework analyzes visual question answering (VQA) problems to assess their impact on predictions. |
CausalGraph2LLM: Evaluating LLMs for Causal Queries (2025.findings-naacl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have opened up new avenues for their use beyond standard Natural Language Processing tasks. |
| Approach: | They propose a benchmark to evaluate the capabilities of Large Language Models (LLMs) they use over 700k queries to compare their encoding capabilities. |
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