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Found 20 papers, 12 papers with code
META: Metadata-Empowered Weak Supervision for Text Classification
Based on seed words, we rank and filter motif instances to distill highly label-indicative ones as {``}seed motifs{''}, which provide additional weak supervision.
DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering
The application of natural language processing models to PDF documents is pivotal for various business applications yet the challenge of training models for this purpose persists in businesses due to specific hurdles.
TOOLVERIFIER: Generalization to New Tools via Self-Verification
Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem.
MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization
RL-based techniques can be used to search for prompts that when fed into a target language model maximize a set of user-specified reward functions.
Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models
In this paper, we introduce a novel training data selection based on the learning percentage of the samples.
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase
DAIL leverages the intuition that large language models are more familiar with the content generated by themselves.
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank
To address the annotation bottleneck, we introduce SELFOOD, a self-supervised OOD detection method that requires only in-distribution samples as supervision.
A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches
Etremely Weakly Supervised Text Classification (XWS-TC) refers to text classification based on minimal high-level human guidance, such as a few label-indicative seed words or classification instructions.
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models
For each utterance, we prompt the LLM with questions corresponding to its top-level intent and a set of slots and use the LLM generations to construct the target meaning representation.
Progressive Sentiment Analysis for Code-Switched Text Data
Instead of training on the entire code-switched corpus at once, we create buckets based on the fraction of words in the resource-rich language and progressively train from resource-rich language dominated samples to low-resource language dominated samples.
Leveraging QA Datasets to Improve Generative Data Augmentation
The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation.
LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification
Weakly supervised text classification methods typically train a deep neural classifier based on pseudo-labels.
BFClass: A Backdoor-free Text Classification Framework
To recognize the poisoned subset, we examine the training samples with these identified triggers as the most suspicious token, and check if removing the trigger will change the poisoned model's prediction.
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data
Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases.
News Meets Microblog: Hashtag Annotation via Retriever-Generator
Hashtag annotation for microblog posts has been recently formulated as a sequence generation problem to handle emerging hashtags that are unseen in the training set.
X-Class: Text Classification with Extremely Weak Supervision
Finally, we pick the most confident documents from each cluster to train a text classifier.
Contextualized Weak Supervision for Text Classification
Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers.
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss
We extend the consistency of hierarchical classification algorithm over asymmetric tree distance loss.
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.
User Bias Removal in Review Score Prediction
Review score prediction of text reviews has recently gained a lot of attention in recommendation systems.
