Papers with Out-of-distribution
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble (2022.findings-emnlp)
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| Challenge: | Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. |
| Approach: | They propose a framework that encourages intermediate features to learn layer-specialized representations and assembles them implicitly into a single representation to absorb rich information in the pre-trained language model. |
| Outcome: | The proposed framework is significantly more effective than previous studies in intent classification and OOD datasets. |
On Prefix-tuning for Lightweight Out-of-distribution Detection (2023.acl-long)
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| Challenge: | Out-of-distribution (OOD) detection is a fundamental task vexing real-world applications . fine-tuning based methods require storing fine- tuned models for each scenario . |
| Approach: | They propose an unsupervised prefix-tuning based OOD detection framework called PTO . they propose to take advantage of optional training data labels and targeted OOD data . |
| Outcome: | The proposed framework performs better than existing methods under a wide range of metrics, detection settings, and OOD types. |
‘No’ Matters: Out-of-Distribution Detection in Multimodality Multi-Turn Interactive Dialogue Download PDF (2025.findings-acl)
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| Challenge: | Out-of-distribution (OOD) detection is essential for multimodal learning systems . a novel scoring framework is proposed to efficiently detect OOD in multi-round long dialogues . |
| Approach: | They propose a scoring framework that integrates visual language models with a score framework that detects OOD in two key scenarios. |
| Outcome: | The proposed framework detects OOD in two key scenarios: mismatches between dialogue and image input pair and previously unseen labels. |
Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networks (2025.emnlp-main)
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| Challenge: | Existing methods for out-of-distribution (OOD) detection ignore textual-structural diversity . text-rich networks (TrNs) represent complex interplay between textual content and relational structures . |
| Approach: | They propose a framework for evaluating out-of-distribution detection in text-rich networks . they propose augmentations, structural shifts, and domain-based divisions to model interplay . |
| Outcome: | Experiments on 11 datasets show the framework is effective in out-of-distribution detection. |
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection (2023.findings-emnlp)
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| Challenge: | Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning. |
| Approach: | They propose to apply world knowledge to enhance OOD detection performance through selective generation from large language models (LLMs) they propose to extract visual objects from each image to fully capitalize on the aforementioned world knowledge. |
| Outcome: | The proposed method outperforms the state-of-the-art on visual OOD detection on in-distribution (ID) samples. |
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection (2023.acl-long)
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| Challenge: | Out-of-distribution (OOD) detection is critical for reliable predictions over text . fine-tuning with pre-trained language models has been a de facto procedure . |
| Approach: | They propose to leverage pre-trained language models for OOD detection without fine-tuning on ID data. |
| Outcome: | The proposed approach outperforms the fine-tuned model under distributional shifts. |
How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)
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| Challenge: | Out-of-distribution (OOD) detection is crucial for ensuring AI safety . large language models (LLMs) are becoming more prevalent due to their scale, pre-training objectives, and paradigms used for inference. |
| Approach: | They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning. |
| Outcome: | The proposed method outperforms other OOD detectors in zero-grad and fine-tuning scenarios. |
Interventional Training for Out-Of-Distribution Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Existing methods for NLU training use only known and single confounders, but in many NLU tasks the confounder can be unknown and multifactorial. |
| Approach: | They propose a method that performs multi-granular intervention with identified multifactorial confounders by using a bottom-up automatic intervention method. |
| Outcome: | The proposed method performs multi-granular intervention with identified multifactorial confounders on three NLU tasks, namely, natural language inference, fact verification and paraphrase identification. |
PROOD: A Simple LLM Out-of-Distribution Guardrail Leveraging Response Semantics (2025.findings-emnlp)
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| Challenge: | Existing OOD methods often struggle with deliberately obfuscated, context-dependent, or superficially benign prompts. |
| Approach: | They propose a framework that jointly analyzes LLM prompts and their outputs to improve semantic understanding. |
| Outcome: | The proposed framework outperforms existing OOD methods on three benchmarks and improves F1 scores by up to 6.3 points. |
VI-OOD: A Unified Framework of Representation Learning for Textual Out-of-distribution Detection (2024.lrec-main)
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| Challenge: | Out-of-distribution (OOD) detection is a crucial part of deep neural networks. |
| Approach: | They propose a variational inference framework which maximizes the likelihood of the joint distribution p(x, y) instead of p[y|x). |
| Outcome: | The proposed framework maximizes the likelihood of the joint distribution p(x, y) instead of p[y|x). |