Papers with semantic complexity
Semi-supervised Relation Extraction via Data Augmentation and Consistency-training (2023.eacl-main)
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| Challenge: | Obtaining high-quality human labelled data is an expensive and noisy process. |
| Approach: | They propose to leverage unlabelled data to improve the sample efficiency of the models. |
| Outcome: | The proposed methods can be used to extract the Cause-Effect relation between a given head entity and tail entity based on context in the input sentence. |
TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale (2026.acl-industry)
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| Challenge: | a 5minute downtime for an incident could result in a loss of 40 million dollars and erosion of user trust. |
| Approach: | They propose a multi-stage event unification engine that synergizes efficient indexing techniques with Large Language Models (LLMs) to make informed decisions on event merging. |
| Outcome: | The proposed system outperforms baseline methods in routing accuracy, clustering quality, and Signal-to-Noise Ratio. |
Semantic-Aware Logical Reasoning via a Semiotic Framework (2026.acl-long)
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Yunyao Zhang, Xinglang Zhang, Junxi Sheng, Wenbing Li, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang, Zikai Song
| Challenge: | Existing studies largely overlook the interplay between logical complexity and semantic complexity, limiting their robustness under abstract propositions, ambiguous contexts, and conflicting stances. |
| Approach: | They propose a semiotic-square-guided framework that integrates automated deduction with reflective verification to manage logical complexity across deeper reasoning chains. |
| Outcome: | The proposed framework achieves state-of-the-art performance on RepublicQA with 6.25% average gain, and generalizes well to four mainstream logical reasoning benchmarks with an additional 7.05% improvement. |
CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation (2025.acl-long)
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| Challenge: | Publicly available datasets can be used to evaluate performance of large language models . however, contamination of test data can artificially inflate model performance . |
| Approach: | They propose a Contamination-resilient Evaluation strategy that updates data with real-world knowledge. |
| Outcome: | The proposed evaluation strategy can be used to update datasets with real-world knowledge. |
Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement (2026.findings-acl)
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| Challenge: | Existing KGQA benchmarks and methods are biased toward positive and calculation constraints. Negative constraints are neglected, although they frequently appear in real-world questions. |
| Approach: | They propose a task where each question contains at least one negative constraint and a corresponding dataset, NestKGQA. |
| Outcome: | The proposed framework outperforms baselines on both KGQA and NEST-KGQA benchmarks under few-shot settings. |
AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations (2026.findings-acl)
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| Challenge: | Existing approaches to decomposing model activations into interpretable features fail to account for input complexity. |
| Approach: | They propose a framework that dynamically adjusts sparsity levels based on the semantic complexity of each input. |
| Outcome: | The proposed framework outperforms fixed-sparsity approaches on reconstruction fidelity, explained variance, cosine similarity and interpretability metrics while eliminating the burden of extensive hyperparameter tuning. |
ClimateCause: Complex and Implicit Causal Structures in Climate Reports (2026.findings-acl)
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| Challenge: | Existing datasets for causal discovery from text lack granularity and abstraction for domains characterized by such complex causality. |
| Approach: | They propose a manually expert-annotated dataset of higher-order causal structures from science-for-policy climate reports. |
| Outcome: | The proposed dataset is highly readable and can be used to quantify readability. |
Semantically Comprehensive Token Pruning in LVLMs via Maximizing Concept Coverage (2026.acl-long)
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| Challenge: | Existing visual token pruning methods leverage simple metrics derived from human experience, such as attention or similarity, to rank and select tokens within a highly entangled feature space. |
| Approach: | They propose a novel visual token pruning method that uses a concept-driven paradigm to quantify the Marginal Semantic Gain of each token's contribution to uncovered concepts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in a concept-driven model while maintaining semantic completeness. |