| Challenge: | High-dimensional dense embeddings extracted by large language models pose memory requirements and high computation time. |
| Approach: | They propose a method that maps high-dimensional dense embeddings to lower-dimensional sparse representations while preserving crucial anomaly characteristics. |
| Outcome: | The proposed method achieves better detection performance than 11 SOTA anomaly detection algorithms while maintaining computational efficiency and low memory cost. |
Similar Papers
LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead. |
| Approach: | They propose a learning-free method that transforms an LLM embedding into a binary embeddable using Isolation Kernel (IKE). |
| Outcome: | The proposed method performs 16.7 faster retrieval and 16 lower memory usage than the original LLM embeddings while maintaining comparable accuracy. |
Enhancing Two Steps Textual Anomaly Detection through Anisotropy Mitigation (2026.acl-long)
Copied to clipboard
| Challenge: | Recent approaches to anomaly detection focus on embeddings from pre-trained models . however, the geometric properties of pre-training embedders can hinder detection algorithms . |
| Approach: | They propose to apply anomaly detection algorithms to embeddings from pre-trained models to improve accuracy. |
| Outcome: | The proposed approach improves similarity-trained models by adapting embeddings to assumptions made by classical detection algorithms. |
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. |
AD-NLP: A Benchmark for Anomaly Detection in Natural Language Processing (2023.emnlp-main)
Copied to clipboard
| Challenge: | Methods for Anomaly Detection in text have shown strong empirical results on ad-hoc anomaly setups that are usually made by downsampling some classes of a labeled dataset. |
| Approach: | They propose a unified benchmark for detecting various types of anomalies . they evaluate two strong shallow baselines and two current state-of-the-art neural approaches . |
| Outcome: | The proposed benchmarks provide insights into the knowledge the neural models are learning when performing the task. |
Improving Text Embeddings with Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for obtaining text embeddings require complex training pipelines . authors leverage proprietary LLMs to generate diverse synthetic data for text embeds based on 93 languages . |
| Approach: | They propose a method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. |
| Outcome: | The proposed method achieves strong performance on competitive text embedding benchmarks without using any labeled data. |
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Backdoor attacks manipulate model predictions by inserting malicious "poison" instances that contain a specific pattern or "trigger." |
| Approach: | They propose an attack that inserts style-based triggers into training and test data by using a poison selection technique to improve the effectiveness of both LLMBkd and existing backdoor attacks. |
| Outcome: | The proposed attack achieves high success rates across a wide range of styles with little effort and no model training. |
LDIR: Low-Dimensional Dense and Interpretable Text Embeddings with Relative Representations (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing text embeddings with high dimensions are difficult to trace and interpret. |
| Approach: | They propose low-dimensional and interpretable text embeddings with relative representations that encode semantic meanings in a vector space where similar texts are close together in the representation space. |
| Outcome: | The proposed embeddings outperform existing models on multiple tasks with fewer dimensions and are lowdimensional and dense while maintaining interpretability. |
Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection (2026.acl-long)
Copied to clipboard
| Challenge: | a rapid proliferation of large language models (LLMs) generate text that increasingly resembles human writing . this makes it difficult to capture subtle cues that distinguish AI-generated content from human-written content . |
| Approach: | They propose a framework that disentangles AI-detection semantics from generator-aware artifacts by latent encoding and perturbation-based regularization. |
| Outcome: | The proposed framework disentangles AI-detection semantics from generator-aware artifacts on 20 representative LLMs across 7 categories. |
Unsupervised Anomaly Detection in Multi-Topic Short-Text Corpora (2023.eacl-main)
Copied to clipboard
| Challenge: | Unsupervised anomaly detection is a challenging task when the majority class is heterogeneous. |
| Approach: | They propose to use word embeddings to represent each sample by a dense vector and use a Mixture Model approach to detect which samples deviate the most from the underlying distributions of the corpus. |
| Outcome: | The proposed method is more efficient than state-of-the-art methods on real datasets. |
Can LLMs Find a Needle in a Haystack? A Look at Anomaly Detection Language Modeling (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Anomaly detection (AD) is a problem in machine learning, but it is not always competitive on certain datasets. |
| Approach: | They propose a new approach to Anomaly detection based on large pre-trained language models in three modalities. |
| Outcome: | The proposed model beats baselines on anomaly detection when presented as imbalanced classification problem regardless of the concentration of anomalous samples. |