Harm de Vries

Leandro Von Werra

The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech … (see more)report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at https://hf.co/bigcode.

2023-01-09

ArXiv (preprint)

The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents

Xing Han Lu

2023-01-01

EACL (published)

The Power of Prompt Tuning for Low-Resource Semantic Parsing

Nathan Schucher

Prompt tuning has recently emerged as an effective method for adapting pre-trained language models to a number of language understanding and… (see more) generation tasks. In this paper, we investigate prompt tuning for semantic parsing—the task of mapping natural language utterances onto formal meaning representations. On the low-resource splits of Overnight and TOPv2, we find that a prompt tuned T5-xl significantly outperforms its fine-tuned counterpart, as well as strong GPT-3 and BART baselines. We also conduct ablation studies across different model scales and target representations, finding that, with increasing model scale, prompt tuned T5 models improve at generating target representations that are far from the pre-training distribution.

2022-05-01

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (published)

TopiOCQA: Open-domain Conversational Question Answering with Topic Switching

Vaibhav Adlakha

Shehzaad Dhuliawala

Kaheer Suleman

2022-04-13

Transactions of the Association for Computational Linguistics (published)

The Power of Prompt Tuning for Low-Resource Semantic Parsing

Nathan Schucher

Prompt tuning has recently emerged as an effective method for adapting pre-trained language models to a number of language understanding and… (see more) generation tasks. In this paper, we investigate prompt tuning for semantic parsing—the task of mapping natural language utterances onto formal meaning representations. On the low-resource splits of Overnight and TOPv2, we find that a prompt tuned T5-xl significantly outperforms its fine-tuned counterpart, as well as strong GPT-3 and BART baselines. We also conduct ablation studies across different model scales and target representations, finding that, with increasing model scale, prompt tuned T5 models improve at generating target representations that are far from the pre-training distribution.

2021-10-16

ArXiv (preprint)

Generative Compositional Augmentations for Scene Graph Prediction

Boris Knyazev

Cătălina Cangea

Graham W. Taylor

Eugene Belilovsky

Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of v… (see more)ision and language. We consider a challenging problem of compositional generalization that emerges in this task due to a long tail data distribution. Current scene graph generation models are trained on a tiny fraction of the distribution corresponding to the most frequent compositions, e.g. . However, test images might contain zero- and few-shot compositions of objects and relationships, e.g. . Despite each of the object categories and the predicate (e.g. ‘on’) being frequent in the training data, the models often fail to properly understand such unseen or rare compositions. To improve generalization, it is natural to attempt increasing the diversity of the training distribution. However, in the graph domain this is non-trivial. To that end, we propose a method to synthesize rare yet plausible scene graphs by perturbing real ones. We then propose and empirically study a model based on conditional generative adversarial networks (GANs) that allows us to generate visual features of perturbed scene graphs and learn from them in a joint fashion. When evaluated on the Visual Genome dataset, our approach yields marginal, but consistent improvements in zero- and few-shot metrics. We analyze the limitations of our approach indicating promising directions for future research.

2021-10-10

2021 IEEE/CVF International Conference on Computer Vision (ICCV) (published)

Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation

Boris Knyazev

Cătălina Cangea

Graham W. Taylor

Eugene Belilovsky

2020-01-01

Proceedings of the British Machine Vision Conference 2020 (published)

CLOSURE: Assessing Systematic Generalization of CLEVR Models

Dzmitry Bahdanau

Timothy O'Donnell

Shikhar Murty

Philippe Beaudoin

Yoshua Bengio

2019-12-12

ArXiv (preprint)

Systematic Generalization: What Is Required and Can It Be Learned?

Dzmitry Bahdanau*

Shikhar Murty*

2019-01-01

ICLR.cc/2019/Conference (poster)

openreview.net

Visual Reasoning with Multi-hop Feature Modulation

Florian Strub

Mathieu Seurin

Ethan Perez

Jérémie Mary

P. Preux

Olivier Pietquin

2018-10-06

Computer Vision – ECCV 2018 (published)

Feature-wise transformations

2018-07-09

Distill (published)

FiLM: Visual Reasoning with a General Conditioning Layer

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence ne… (see more)ural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.

2018-04-29

Proceedings of the AAAI Conference on Artificial Intelligence (published)