Latest Articles for Natural Language Processing

New methods improve how we assess computer-generated text.

Enhancing text-to-SQL models by integrating diverse question phrasing.

Introducing BMTPT for improved prompt tuning in language models.

Learn how data-to-text generation makes complex information easier to understand.

A new method safeguards decision privacy in language models while maintaining performance.

This article examines the difficulties and techniques in updating knowledge in language models.

A new approach to enhance NLP model performance on unseen data.

Exploring ways to enhance LLM reasoning without external feedback.

Improving language models for accurate, evidence-based answers.

A new framework to evaluate LLMs' understanding of code tasks.

This paper analyzes the advantages of multi-head attention over single-head attention in machine learning tasks.

SafeCoder improves the safety of code generated by language models.

A new method assesses the effectiveness of model editing in generating longer texts.

A new method enhances how language models gather and use information.

Research on how Transformers improve generalization for longer sequences in addition tasks.

An overview of transformers and their impact on data processing.

Strategies to improve model performance despite uneven data distribution.

A new method to improve language model behavior by reducing bias and toxic output.

SyntaxShap enhances understanding of language model predictions through syntax analysis.

SLEB streamlines LLMs by removing redundant transformer blocks, enhancing speed and efficiency.

An overview of In-Context Learning and its practical applications through the Pelican Soup Framework.

A study on reliability and uncertainty in large language models.

A new approach enhances contrastive learning through varied divergence measures.

This article discusses a new approach to improve text generation models using quantization.

An in-depth look at why transformers struggle with sensitive functions.

This study outlines methods to evaluate dialogue generation through diversity and quality.

Study reveals language models struggle against simple text manipulations.

This research examines how language models respond to accurate versus false information.

TOAD dataset enhances virtual assistant communication for better user interactions.

A new method for adapting LLMs without extensive labeling.

A new framework for assessing AI answer correctness with human-like judgment.

This study examines how language models adapt their predictions using in-context learning.

This article discusses a method to improve LLMs using verbal feedback without overgeneralization.

A look at how transformers excel with unstructured data in regression tasks.

Exploring the future of extractive language processing using generative models.

A new approach improves language models' ability to transform sequences despite limited data.

LinkNER combines NER models and LLMs for better named entity recognition.

This research focuses on enhancing language models by refining their approach to negation.

A framework to enhance LLMs' understanding of abstraction.

A study on mixing domain-specific adapters for improved AI performance.