Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) perform well on Out-of-Distribution tasks, but performance degrades as distribution shift becomes more severe. |
| Approach: | They propose a demonstration search framework that incorporates an OOD proxy to approximate the inaccessible target domain and guide the retrieval process. |
| Outcome: | The proposed framework enhances robustness in out-of-distribution tasks by incorporating an OOD proxy to approximate the inaccessible target domain and guide the retrieval process. |
Similar Papers
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. |
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. |
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. |
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. |
Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods that rely on limited demos and out-of-demonstration (OOD) queries fail when faced with out- of-demotion queries. |
| Approach: | They propose a query-aware prompting method that elicits the inherent generalizability of large language models by query-based demo generation. |
| Outcome: | The proposed method outperforms state-of-the-art methods in the OOD setting and two public math benchmarks. |
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. |
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. |
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. |
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. |
From Fake to Real: Mitigating Out-of-Distribution Bias in In-Context Learning via Feedback Supervision from Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | In-Context Learning (ICL) is one of the most common methods for complex Natural Language Understanding tasks. |
| Approach: | They propose a method that uses model confidence and perturbation perplexity to enhance the quality of pseudo-labels. |
| Outcome: | The proposed method reduces OOD biases by avoiding direct use of source data. |