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Found 12 papers, 11 papers with code
On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning
Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem, where a meta-learning model is trained with a certain capability to quickly adapt to newly specified few-shot tasks with potentially unseen intent categories.
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective.
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs' perception of the key information to simultaneously address the three challenges.
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
Large language models (LLMs) have been applied in various applications due to their astonishing capabilities.
LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection
Moreover, we are the first who pose the problem of hyperparameter selection in unsupervised anomaly detection, and propose a solution of synthesizing anomaly data for a pseudo validation set to address this problem.
CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language.
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text
Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples.
Decomposed Meta-Learning for Few-Shot Named Entity Recognition
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples.
Ranked #5 on
Few-shot NER
on Few-NERD (INTRA)
AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER
Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages.
UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data
Prior works in cross-lingual named entity recognition (NER) with no/little labeled data fall into two primary categories: model transfer based and data transfer based methods.
Ranked #1 on
Cross-Lingual NER
on NoDaLiDa Norwegian Bokmål
Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language
However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language.
Ranked #1 on
Cross-Lingual NER
on CoNLL German
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).
Ranked #1 on
Cross-Lingual NER
on MSRA
