| 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). |
| Outcome: | The proposed model is based on the principles of Causal Mediation Analysis and quantifies the causal effects of different prompt modalities on the model. |
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| Challenge: | Using sequence-driven structural causal models (SD-SCMs) we characterize how SD-SCAMs enables sampling from observational, interventional, and counterfactual distributions according to the desired causal structure. |
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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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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. |
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Bridging Internal Consistency and External Alignment: A Causal and Dynamic Interpretability Framework for LLM Generation (2026.acl-long)
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| Challenge: | Existing interpretability methods focus on internal and external aspects of the model . existing explanations often focus on surface correlations or static dependencies . |
| Approach: | They propose a causal and dynamic interpretability framework for Large Language Models . they characterize backdoor-adjusted causal effects of generated prefix and prompt . |
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CausalEval: Towards Better Causal Reasoning in Language Models (2025.naacl-long)
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Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan
| Challenge: | Large language models (LLMs) have been used for a variety of tasks, including problem-solving, decision-making, and understanding of the world. |
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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. |
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Multi-component Causal Tracing in Large Language Models (2026.acl-long)
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| 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. |
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Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference (2026.findings-eacl)
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| Challenge: | Prior studies have examined the impact of structured output on LLMs’ generation quality, often presenting one-way findings. |
| Approach: | They propose to derive five potential causal structures characterizing the influence of structured output on LLMs’ generation using one assumed and two guaranteed constraints. |
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The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code (2023.findings-acl)
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| Challenge: | entailment a) |
| Approach: | entailment : We want to explore whether Code-LLMs with code prompts are better . encoding a code prompt is better than text-only LLMs, they say . |
| Outcome: | entailment : Our results show that Code-LLMs with code prompts are better compared to text-only LLMs. |
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. |