Simulating Crisis Cognition: A Computational Framework for Hypothesis Generation in Crisis Communication (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable fidelity in simulating social dynamics, yet using them to inform high-stakes crisis policy requires rigorous causal evaluation. |
| Approach: | They propose a framework that functions as an in-silico hypothesis generator to evaluate communication strategies by coupling real-world telemetry with 1,813 agents. |
| Outcome: | The proposed framework provides a rigorous testbed for evaluating strategies before human-subject trials. |
Similar Papers
PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events (2025.emnlp-main)
Copied to clipboard
Mengzhu Liu, Zhengqiu Zhu, Chuan Ai, Chen Gao, Xinghong Li, Lingnan He, Kaisheng Lai, Yingfeng Chen, Xin Lu, Yong Li, Quanjun Yin
| Challenge: | Social media's rich information content and spatiotemporal granularity provide unique opportunities for emotion prediction and management. |
| Approach: | They propose a Psychology-driven generative Agent framework for explainable panic prediction based on emotion arousal theory. |
| Outcome: | The proposed framework improves panic emotion prediction performance by 13% to 21% compared to baseline models. |
Language Models as Causal Effect Generators (2025.emnlp-main)
Copied to clipboard
| 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. |
| Approach: | They propose a sequence-driven structural causal model that uses language models to parameterize a structural causal system based on a user-specified DAG. |
| Outcome: | The proposed method outperforms state-of-the-art methods and can underpin auditing of language models for (un)desirable causal effects, such as misinformation or discrimination. |
LLMs Don’t Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations (2025.emnlp-main)
Copied to clipboard
Harry Mayne, Ryan Othniel Kearns, Yushi Yang, Andrew M. Bean, Eoin D. Delaney, Chris Russell, Adam Mahdi
| Challenge: | Existing studies on language models' ability to explain their decisions in natural language have focused on self-generated counterfactual explanations (SCEs). |
| Approach: | They evaluate whether LLMs can generate valid counterfactuals and minimal ones . authors suggest that SCEs are, at best, an ineffective explainability tool . |
| Outcome: | The proposed language models can explain their decisions in natural language, the study finds . the models can produce valid counterfactual explanations, but make small edits that fail to change predictions. |
Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their autoregressive generation paradigm makes it computationally prohibitive to explore diverse reasoning paths. |
| Approach: | They propose a framework that combines diffusion-based generation with autoregressive evaluation to efficiently generate diverse intermediate reasoning thoughts and employ LLMs as evaluators to assess and select candidates based on their plausibility and correctness. |
| Outcome: | The proposed framework improves inference efficiency while maintaining competitive or superior reasoning accuracy. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
Copied to clipboard
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
| Challenge: | Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability. |
| 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-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to Emotional Support Conversation (ESC) are mechanistically opaque and lacks a causal mechanism between dialogue features and effective empathic strategies. |
| Approach: | They propose a framework that uses Doubly Robust learning to model causal effects of utterance features on strategy selection. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in empathy and helpfulness and provides a theoretically grounded, interpretable solution to the mechanistic interpretability dilemma in affective computing. |
Perspective Taking through Generating Responses to Conflict Situations (2024.findings-acl)
Copied to clipboard
| Challenge: | Language models struggle to understand and explain the beliefs of others, despite improving performance on a wide variety of tasks. |
| Approach: | They propose to modify the social-chem-101 corpus to allow for perspective-taking, the process of conceptualizing the point of view of another person. |
| Outcome: | The proposed models outperform the recent models conditioned on self-disclosures with high similarity to the conflict situation. |
CodeSCM: Causal Analysis for Multi-Modal Code Generation (2025.naacl-long)
Copied to clipboard
| 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. |
On Emergent Social World Models — Evidence for Functional Integration of Theory of Mind and Pragmatic Reasoning in Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LMs) possess astonishing abilities and prove useful for a plethora of downstream tasks, but controversy persists regarding how to conceptualize their capacities. |
| Approach: | They analyze LMs’ performance across seven subcategories of ToM abilities using a large localizer dataset than used in prior work. |
| Outcome: | The proposed models recruit shared computational mechanisms for general Theory of Mind (ToM) and language-specific pragmatic reasoning on a substantially larger localizer dataset than used in prior work. |
CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing classification-based methods capture noise and spurious correlations while overlooking the underlying causal mechanisms. |
| Approach: | They propose a hallucination detection framework based on structural causal models that captures static and passive signals from internal states and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise. |
| Outcome: | Experiments on four datasets and three widely used LLMs show that the proposed framework improves AUROC and interpretability. |