Search Results for author:
Found 10 papers, 3 papers with code
Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task
Converting a knowledge graph or sub-graph to natural text is useful when answering questions based on a knowledge base.
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases.
Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency
In this paper, we show that a simple self-supervised pre-trained audio model can achieve comparable inference efficiency to more complicated pre-trained models with speech transformer encoders.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task
In this work, we propose a single encoder-decoder neural network that can handle long documents and conversations, trained simultaneously for both segmentation and segment labeling using only standard supervision.
Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering
A popular approach to QA is extractive reading comprehension (RC) which finds an answer span in a text passage.
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data
In this paper, we present approaches that have helped us deploy data-efficient neural solutions for NLG in conversational systems to production.
Towards Deep and Representation Learning for Talent Search at LinkedIn
In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter.
Robust Estimation of Neural Signals in Calcium Imaging
We use our proposed robust loss in a matrix factorization framework to extract the neurons and their temporal activity in calcium imaging datasets.
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
Recurrent neural networks have been very successful at predicting sequences of words in tasks such as language modeling.
Ranked #34 on
Language Modelling
on Penn Treebank (Word Level)
