Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Lanchantin, Jack"'
Autor:
Mekala, Dheeraj, Weston, Jason, Lanchantin, Jack, Raileanu, Roberta, Lomeli, Maria, Shang, Jingbo, Dwivedi-Yu, Jane
Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem. While there has been significant progress on learning to use specific tools via fine-tuning, language models still strug
Externí odkaz:
http://arxiv.org/abs/2402.14158
Autor:
Lanchantin, Jack, Sukhbaatar, Sainbayar, Synnaeve, Gabriel, Sun, Yuxuan, Srinet, Kavya, Szlam, Arthur
Recent progress in using machine learning models for reasoning tasks has been driven by novel model architectures, large-scale pre-training protocols, and dedicated reasoning datasets for fine-tuning. In this work, to further pursue these advances, w
Externí odkaz:
http://arxiv.org/abs/2309.07974
Large language models have been shown to struggle with multi-step reasoning, and do not retain previous reasoning steps for future use. We propose a simple method for solving both of these problems by allowing the model to take Self-Notes. Unlike rec
Externí odkaz:
http://arxiv.org/abs/2305.00833
Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-labe
Externí odkaz:
http://arxiv.org/abs/2011.14027
Publikováno v:
Findings of the Association for Computational Linguistics: EMNLP 2020
State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack. We distill ideas from past work into a unified framework: a successful natural language adversarial example is a perturbation that fools the mo
Externí odkaz:
http://arxiv.org/abs/2004.14174
Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural Networks to effic
Externí odkaz:
http://arxiv.org/abs/1904.08049
Autor:
Lanchantin, Jack, Gao, Ji
Statistical language models are powerful tools which have been used for many tasks within natural language processing. Recently, they have been used for other sequential data such as source code.(Ray et al., 2015) showed that it is possible train an
Externí odkaz:
http://arxiv.org/abs/1803.08793
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios. In this paper, we present a novel algorithm, DeepWo
Externí odkaz:
http://arxiv.org/abs/1801.04354
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging
Externí odkaz:
http://arxiv.org/abs/1710.11238
The past decade has seen a revolution in genomic technologies that enable a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements
Externí odkaz:
http://arxiv.org/abs/1708.00339