Search Results for author: Artidoro Pagnoni

Found 11 papers, 7 papers with code

QLoRA: Efficient Finetuning of Quantized LLMs

9 code implementations23 May 2023 Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer

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.

Chatbot Instruction Following +3

Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization

1 code implementation20 Dec 2022 Artidoro Pagnoni, Alexander R. Fabbri, Wojciech Kryściński, Chien-Sheng Wu

In long document controllable summarization, where labeled data is scarce, pretrained models struggle to adapt to the task and effectively respond to user queries.

Question Generation Question-Generation

StructSum: Summarization via Structured Representations

1 code implementation EACL 2021 Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov

To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models.

Abstractive Text Summarization Document Summarization

Definition Frames: Using Definitions for Hybrid Concept Representations

1 code implementation COLING 2020 Evangelia Spiliopoulou, Artidoro Pagnoni, Eduard Hovy

Advances in word representations have shown tremendous improvements in downstream NLP tasks, but lack semantic interpretability.

Relation Extraction Word Embeddings +1

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach

1 code implementation10 Jun 2019 Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi

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.

BIG-bench Machine Learning Translation

PAC Learning Guarantees Under Covariate Shift

no code implementations16 Dec 2018 Artidoro Pagnoni, Stefan Gramatovici, Samuel Liu

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.

Domain Adaptation PAC learning +1

Conditional Variational Autoencoder for Neural Machine Translation

no code implementations11 Dec 2018 Artidoro Pagnoni, Kevin Liu, Shangyan Li

We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT).

Conditional Text Generation Machine Translation +2

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