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Found 11 papers, 7 papers with code
Threat Scenarios and Best Practices to Detect Neural Fake News
In this work, we discuss different threat scenarios from neural fake news generated by state-of-the-art language models.
QLoRA: Efficient Finetuning of Quantized LLMs
Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99. 3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.
Ranked #2 on
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Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization
In long document controllable summarization, where labeled data is scarce, pretrained models struggle to adapt to the task and effectively respond to user queries.
EvEntS ReaLM: Event Reasoning of Entity States via Language Models
This paper investigates models of event implications.
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
Modern summarization models generate highly fluent but often factually unreliable outputs.
StructSum: Summarization via Structured Representations
To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models.
Definition Frames: Using Definitions for Hybrid Concept Representations
Advances in word representations have shown tremendous improvements in downstream NLP tasks, but lack semantic interpretability.
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
To this end, we propose a framework that translates a pre-trained ML pipeline into a neural network and fine-tunes the ML models within the pipeline jointly using backpropagation.
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology available to every developer.
PAC Learning Guarantees Under Covariate Shift
We consider the Domain Adaptation problem, also known as the covariate shift problem, where the distributions that generate the training and test data differ while retaining the same labeling function.
Conditional Variational Autoencoder for Neural Machine Translation
We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT).
