Papers with cross-dataset generalization
Fake News Detection Strategies under Dataset Bias: Using Large-scale Coarse-grained Labels (2026.eacl-srw)
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| Challenge: | Existing datasets differ substantially in content distributions and annotation policies, complicating fair evaluation and generalization assessment. |
| Approach: | They quantitatively analyze dataset bias across multiple public fake news datasets with different annotation granularities, including article-level and publisher-level labels. |
| Outcome: | The proposed approach improves detection performance under in-dataset and cross-data set evaluation settings. |
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages (2022.acl-long)
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| Challenge: | Existing studies on pre-trained language models assume they encode metaphorical knowledge useful for NLP systems. |
| Approach: | They propose to probing metaphoricity information in PLMs and measure their generalization . they find that contextual representations in PMLs encode metaphorical knowledge . |
| Outcome: | The proposed model can encode metaphorical knowledge across languages and datasets . the model can be used to train and test NLP systems . |
Explaining Generalization of AI-Generated Text Detectors Through Linguistic Analysis (2026.eacl-long)
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| Challenge: | Existing studies have reported generalization gaps in AI-text detectors, but they lack insights into the causes. |
| Approach: | They propose to analyze generalization behavior of AI-text detectors using linguistic analysis to explain performance variance. |
| Outcome: | The proposed model can generalize across unseen prompts, model families, and domains, but it can't generalize under distribution shifts. |
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning (2024.acl-long)
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| Challenge: | Existing methods focus on single-step reasoning, ignoring logical dependencies between steps. |
| Approach: | They propose a method that maximizes a structure-based return to facilitate structured reasoning and explanation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on EntailmentBank and STREET benchmarks. |
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models (2025.acl-long)
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| Challenge: | Existing backdoor models are limited in coverage of attack, system integrity and backdoor alignment . ELBA-Bench provides over 1300 experiments encompassing 12 attack methods, 18 datasets, and 12 LLMs. |
| Approach: | They propose a framework that allows attackers to inject backdoor through parameter efficient fine-tuning or without fine-uning techniques. |
| Outcome: | ELBA-Bench provides over 1300 experiments encompassing 12 attack methods, 18 datasets, and 12 LLMs. |
DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models (2026.acl-long)
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| Challenge: | Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs). |
| Approach: | They propose a framework that explicitly decouples FFN and Attention contributions via noise-induced causal interventions to capture model's internal fragility. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in both uncertainty estimation and calibration while exhibiting superior cross-dataset generalization. |