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Found 16 papers, 9 papers with code
Self-Evaluation Improves Selective Generation in Large Language Models
Safe deployment of large language models (LLMs) may benefit from a reliable method for assessing their generated content to determine when to abstain or to selectively generate.
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Specifically, we introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types, including questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked.
Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost.
Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals
This paper presents an efficient Multi-scale Transformer-based approach for the task of Emotion recognition from Physiological data, which has gained widespread attention in the research community due to the vast amount of information that can be extracted from these signals using modern sensors and machine learning techniques.
Vision Transformer for Action Units Detection
Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System.
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022).
ViDeBERTa: A powerful pre-trained language model for Vietnamese
This paper presents ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, with three versions - ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, which are pre-trained on a large-scale corpus of high-quality and diverse Vietnamese texts using DeBERTa architecture.
Dialect-robust Evaluation of Generated Text
Evaluation metrics that are not robust to dialect variation make it impossible to tell how well systems perform for many groups of users, and can even penalize systems for producing text in lower-resource dialects.
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case study.
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.
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer
Finally, we propose an efficient retrieval approach that interprets task prompts as task embeddings to identify similar tasks and predict the most transferable source tasks for a novel target task.
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning
Despite their recent successes in tackling many NLP tasks, large-scale pre-trained language models do not perform as well in few-shot settings where only a handful of training examples are available.
Ranked #1 on
Few-Shot NLI
on SNLI (8 training examples per class)
Exploring and Predicting Transferability across NLP Tasks
We also develop task embeddings that can be used to predict the most transferable source tasks for a given target task, and we validate their effectiveness in experiments controlled for source and target data size.
Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification
While paragraph embedding models are remarkably effective for downstream classification tasks, what they learn and encode into a single vector remains opaque.
Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment
Supervised distributional methods are applied successfully in lexical entailment, but recent work questioned whether these methods actually learn a relation between two words.
Sentence Simplification with Memory-Augmented Neural Networks
Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications.
Ranked #1 on
Text Simplification
on PWKP / WikiSmall
