PROOD: A Simple LLM Out-of-Distribution Guardrail Leveraging Response Semantics (2025.findings-emnlp)
Copied to clipboard
| 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. |
Similar Papers
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection (2023.findings-emnlp)
Copied to clipboard
| 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. |
How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)
Copied to clipboard
| 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. |
On Prefix-tuning for Lightweight Out-of-distribution Detection (2023.acl-long)
Copied to clipboard
| 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. |
Navigating the Unknown: Intent Classification and Out-of-Distribution Detection Using Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Out-of-Distribution (OOD) detection requires great generalization capability . |
| Approach: | They propose a method that is cost-efficient, high-performing, highly robust and versatile enough to be used with smaller LLMs without sacrificing performance. |
| Outcome: | The proposed method is cost-efficient, high-performing, robust, and versatile enough to be used with smaller LLMs without sacrificing performance. |
Out-of-Distribution Detection via LLM-Guided Outlier Generation for Text-attributed Graph (2025.findings-acl)
Copied to clipboard
| Challenge: | Text-Attributed Graphs (TAGs) are widely used in the real world. |
| Approach: | They propose to use Large Language Models to generate OOD-nodes with high quality . they also use LLMs to integrate existing nodes with LLM-generated edges . |
| Outcome: | The proposed method performs well on samples outside the In-Distribution (ID) data, but it is difficult to obtain high-quality OOD samples in the real world. |
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods for detecting out-of-distribution inputs are underexplored . detecting semantic and non-semantic shifts is difficult for pre-tuned pre-trainers . |
| Approach: | They propose a general OOD score that integrates confidence scores from task-agnostic and task-specific representations to improve detecting semantic and non-semantic shifts. |
| Outcome: | The proposed method improves on two cross-task benchmarks with semantic and non-semantic shifts. |
Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated their effectiveness in natural language processing but also in broader applications due to their advanced comprehension and generative capabilities. |
| Approach: | They propose a taxonomy to categorize existing approaches into two classes based on the role played by LLMs. |
| Outcome: | The proposed taxonomy categorizes existing approaches into two classes based on the role played by LLMs. |
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble (2022.findings-emnlp)
Copied to clipboard
| 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. |
Polysemantic Dropout: Conformal OOD Detection for Specialized LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | specialized large language models (LLMs) are capable of generating coherent, contextually relevant and often human-level language, but they remain vulnerable to errors when confronted with data that falls outside the scope of their domain. |
| Approach: | They propose an inference-time out-of-domain (OOD) detection algorithm that leverages the Inductive Conformal Anomaly Detection framework and a new non-conformity measure based on the model’s dropout tolerance. |
| Outcome: | The proposed method improves detection while maintaining theoretical false alarm bounds from ICAD while maintaining false alarms bounds. |
RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data (2023.emnlp-main)
Copied to clipboard
| Challenge: | Out-of-distribution (OOD) detection is a widely covered topic in classification tasks, but most methods rely on hidden features output by the encoder. |
| Approach: | They propose to leverage soft-probabilities in a black-box framework to detect OOD . they propose to use a more operational evaluation setting to enable OOD detection . |
| Outcome: | The proposed framework can access soft-predictions but not the internal states of the model. |