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Found 9 papers, 2 papers with code
LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models
Large language models (LLMs) have revolutionized various domains, yet their utility comes with significant challenges related to outdated or problematic knowledge embedded during pretraining.
Translation-Enhanced Multilingual Text-to-Image Generation
Research on text-to-image generation (TTI) still predominantly focuses on the English language due to the lack of annotated image-caption data in other languages; in the long run, this might widen inequitable access to TTI technology.
Cross-lingual Text-to-Image Generation
Crosslingual Text-to-Image Generation
+6
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data
Currently popular video-text data mining approach via automatic speech recognition (ASR) used in HowTo100M provides low-quality captions that often do not refer to the video content.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.
Ranked #14 on
Natural Language Inference
on CommitmentBank
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding
For complex parsing tasks, the state-of-the-art method is based on autoregressive sequence to sequence models to generate the parse directly.
Implicit Language Model in LSTM for OCR
Neural networks have become the technique of choice for OCR, but many aspects of how and why they deliver superior performance are still unknown.
Pose-Aware Face Recognition in the Wild
We propose a method to push the frontiers of unconstrained face recognition in the wild, focusing on the problem of extreme pose variations.
Learning Document Image Binarization from Data
In this paper we present a fully trainable binarization solution for degraded document images.
