Papers with causality
Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition (C18-1)
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| Challenge: | Event relation recognition is a challenging language processing task because the query events are selected from different paragraphs in a document or even different documents, so there is lack of explicit clue. |
| Approach: | They propose to use image processing to acquire similar event instances and use image matching to approximate calculation between events. |
| Outcome: | The proposed model performs comparable to CNN while slightly better than LSTM on the ACE-R2 corpus. |
Evaluating Instructively Generated Statement by Large Language Models for Directional Event Causality Identification (2025.findings-acl)
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| Challenge: | Existing studies focus on identifying existence of causality between two event mentions, but the direction of causalities is crucial for understanding the causal relation. |
| Approach: | They propose to instruct a GLM to generate causality statements and identify directional event causality by evaluating the generated statements. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods even with fewer training data. |
The Geometry of Numerical Reasoning: Language Models Compare Numeric Properties in Linear Subspaces (2025.naacl-short)
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| Challenge: | Existing studies have focused on simple factual recall, but we have not explored how this is used in more complex queries. |
| Approach: | They propose to identify low-dimensional subspaces which encode numerical attributes associated with entities in comparison prompts. |
| Outcome: | The proposed model can answer numeric comparison questions using a low-dimensional subspace of theembedding space. |
Debiasing Event Understanding for Visual Commonsense Tasks (2022.findings-acl)
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| Challenge: | a recent study shows that object-based event understanding is purely likelihood-based, leading to incorrect event prediction. |
| Approach: | They propose to mitigate object-based event understanding by optimizing aggregation with association-based prediction. |
| Outcome: | The proposed approach improves visual commonsense reasoning tasks by combining do-calculus with association-based prediction. |
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond (2022.tacl-1)
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Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
| Challenge: | causality has not had the same importance in natural language processing, says aaron e. smith . he says research on causality in NLP remains scattered across domains without unified definitions . |
| Approach: | They propose to consolidate research on causality in NLP across academic areas . they explore potential uses of causal inference to improve robustness, fairness, interpretability . |
| Outcome: | The proposed method is a unified overview of causal inference for the NLP community. |
Unit Testing for Concepts in Neural Networks (2022.tacl-1)
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| Challenge: | Existing theories of language and cognition hold that these representations are structured in a compositional way and that the meanings of composite concepts (''gray car'') are inherited predictably from the meaning of the parts. |
| Approach: | They propose to test models for determining whether a system’s behavior is consistent with several key aspects of Fodor’s criteria. |
| Outcome: | The proposed models succeed on tests of groundedness, modularity, and reusability of concepts, but important questions about causality remain open. |
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang (2022.acl-long)
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| Challenge: | a recent study suggests that language evolution is a diachronic process, but no causal analysis is performed to verify these claims. |
| Approach: | They analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang terms. |
| Outcome: | The proposed model shows that slang has smaller semantic change but larger frequency shifts over time. |
Event Causality Identification with Synthetic Control (2024.emnlp-main)
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| Challenge: | Existing approaches to event causality identification have primarily utilized linguistic patterns and multi-hop relational inference, risking false causality . |
| Approach: | They propose to use the Rubin Causal Model to identify event causality by generating a twin from existing corpora. |
| Outcome: | The proposed method can identify causal relations more robustly than previous methods, including GPT-4, which is demonstrated on a causality benchmark, COPES-hard. |
Unveiling Decision-Making in LLMs for Text Classification : Extraction of influential and interpretable concepts with Sparse Autoencoders (2026.findings-eacl)
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| Challenge: | Concept-based explanations for large language models are not well understood in text classification. |
| Approach: | They propose a model with a specialized classifier head and activation rate sparsity loss for sentence classification . they compare it to existing models with HI-Concept and ConceptShap . |
| Outcome: | The proposed model improves both the causality and interpretability of the extracted features. |
Causal Intervention for Abstractive Related Work Generation (2023.findings-emnlp)
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| Challenge: | Existing models ignore the inherent causality during related work generation, leading to spurious correlations which downgrade the models’ generation quality and generalizability. |
| Approach: | They propose a Causal Intervention Module for Related Work Generation (CaM) that captures causal relationships in related work generation and implements causal interventions to mitigate the negative impact of spurious correlations. |
| Outcome: | The proposed framework improves the quality and coherence of generated related work by capturing causalities in the generation process. |
Joint Reasoning for Temporal and Causal Relations (P18-1)
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| Challenge: | a cause must occur earlier than its effect, temporal and causal relations are closely related . a joint inference framework is developed for studying temporal, causal relations . |
| Approach: | They propose a joint inference framework for temporal and causal relations . they use constraints inherent in time and causality to enforce constraints . |
| Outcome: | The proposed framework improves extraction of temporal and causal relations from text. |
Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning (2023.acl-long)
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| Challenge: | Existing QA models rely on shortcuts to provide the true answer, referred to as disconnected reasoning problem. |
| Approach: | They propose a causal-effect approach that exploits true multi-hop reasoning instead of shortcuts. |
| Outcome: | The proposed method achieves 5.8% higher points of its Supps score on hotpotQA through true multihop reasoning. |
Weakly Supervised Multilingual Causality Extraction from Wikipedia (D19-1)
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| Challenge: | Existing methods for extracting causality knowledge from Wikipedia are lacking in this area. |
| Approach: | They propose a method for extracting causality knowledge from Wikipedia . they exploit the multilinguality of Wikipedia and the ability to translate to multiple languages . |
| Outcome: | The proposed method achieves precision and recall above 98% and 64%, respectively. |
CLEAR: Can Language Models Really Understand Causal Graphs? (2024.findings-emnlp)
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| Challenge: | Existing language models lack a conceptual framework for understanding causal graphs, but there is still potential for improvement. |
| Approach: | They develop a framework to define causal graph understanding by assessing language models’ behaviors through four practical criteria derived from diverse disciplines. |
| Outcome: | The proposed framework defines three complexity levels and encompasses 20 causal graph-based tasks across 20 different levels. |
Understanding Fine-grained Distortions in Reports of Scientific Findings (2024.findings-acl)
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| Challenge: | a fine-grained understanding of how scientific findings are reported is crucial, says a new study . a recent study found that tweets distort scientific findings more often than news reports . |
| Approach: | They propose to annotate 1,600 scientific findings from academic papers paired with corresponding tweets . they also establish baselines for automatically detecting these characteristics . |
| Outcome: | The proposed method outperforms few-shot prompting in detecting distortions in unpaired data. |
Uncertainty in Causality: A New Frontier (2025.acl-long)
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| Challenge: | Existing literature on uncertainty in causality is lacking a comprehensive review of this area. |
| Approach: | They propose a trichotomy categorizing causal uncertainty into aleatoric, epistemic, ontological and ontological categories . they propose key traits for an optimal causal LLM to handle uncertainty . |
| Outcome: | The proposed method categorizes causal uncertainty into aleatoric, epistemic, and ontological uncertainty. |
Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering (2021.acl-long)
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| Challenge: | Recent advances in natural language processing and computer vision have made significant progress in artificial intelligence (AI). |
| Approach: | They propose Motion-Appearance Synergistic Networks which embed cross-modal features grounded on motion and appearance information and selectively utilize them depending on the question’s intentions. |
| Outcome: | The proposed network achieves state-of-the-art on the TGIF-QA and MSVD-QA datasets and qualitatively analyzes the results. |
LLMs Are Prone to Fallacies in Causal Inference (2024.emnlp-main)
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| Challenge: | Recent work shows that causal facts can be extracted from LLMs through prompting . but it is unclear if this success is limited to explicitly-mentioned causal facts in pretraining data . |
| Approach: | They fine tune LLMs on synthetic data and test whether they can infer causal relations . they find that LLM can correctly deduce absence of causal relations from temporal and spatial relations if order is randomized . |
| Outcome: | The proposed model outperforms existing methods on causal inference tasks. |
CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks (2025.findings-emnlp)
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| Challenge: | Existing work on multi-agent collaborative tasks in Minecraft is limited due to inefficiency and limited fault tolerance. |
| Approach: | They propose a framework that incorporates causality to manage dependencies among subtasks. |
| Outcome: | The proposed framework achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft. |
CausalDialogue: Modeling Utterance-level Causality in Conversations (2023.findings-acl)
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| Challenge: | Despite widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans . despite their widespread adoption in society, chatbots have yet not shown natural chat capability . |
| Approach: | They propose a causality-enhanced method to enhance the impact of causality at the utterance level in training neural conversation models. |
| Outcome: | The proposed method improves diversity and agility of loss functions and still needs improvement . the proposed method is based on a CausalDialogue dataset . |
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 . |
| Outcome: | The proposed framework provides a unified causal view of internal consistency and external alignment in LLM generation dynamics. |
Causal Graph based Event Reasoning using Semantic Relation Experts (2025.acl-long)
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| Challenge: | Recent advances in event reasoning have limited ability to accurately identify causal connections between events. |
| Approach: | They propose a collaborative approach to generate correct graphs and graphs to assist reasoning . they propose 'a causal chain of events' task that requires a causal link between events . |
| Outcome: | The proposed approach achieves competitive results with state-of-the-art models on forecasting and next event prediction tasks. |
Injecting Context via Situation Working Memory for Logical Reasoning with LLMs (2026.acl-long)
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| Challenge: | Recent advances in large language models have improved logical reasoning by injecting formal logic or explicit structured representations. |
| Approach: | a cognitively inspired method is proposed to help LLMs construct a mental representation of events . SituW builds a situation representation by decomposing text along these five dimensions . it also guides LLM inference with this evolving state . |
| Outcome: | a cognitively inspired method improves accuracy and predictability in large language models . SituW builds a mental representation by decomposing text along these dimensions . |
Planning Beyond Text: Graph-based Reasoning for Complex Narrative Generation (2026.findings-acl)
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| Challenge: | Existing methods for long-form complex narrative generation struggle to maintain global narrative coherence and logical consistency. |
| Approach: | They propose a framework that performs narrative planning on structural graph representations instead of direct sequential text representations. |
| Outcome: | The proposed model outperforms representative baselines across diverse scenarios. |