Search Results for author: Ves Stoyanov

Found 15 papers, 8 papers with code

bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark

2 code implementations4 Jun 2023 Momchil Hardalov, Pepa Atanasova, Todor Mihaylov, Galia Angelova, Kiril Simov, Petya Osenova, Ves Stoyanov, Ivan Koychev, Preslav Nakov, Dragomir Radev

We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark.

Fact Checking named-entity-recognition +5

LEVER: Learning to Verify Language-to-Code Generation with Execution

1 code implementation16 Feb 2023 Ansong Ni, Srini Iyer, Dragomir Radev, Ves Stoyanov, Wen-tau Yih, Sida I. Wang, Xi Victoria Lin

The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation.

Arithmetic Reasoning Code Generation +2

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

no code implementations22 Dec 2022 Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, Xian Li, Brian O'Horo, Gabriel Pereyra, Jeff Wang, Christopher Dewan, Asli Celikyilmaz, Luke Zettlemoyer, Ves Stoyanov

To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.

Language Modelling Meta-Learning +2

Complementary Explanations for Effective In-Context Learning

1 code implementation25 Nov 2022 Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, Ves Stoyanov, Greg Durrett, Ramakanth Pasunuru

Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts, but there has been limited understanding of exactly how these explanations function or why they are effective.

Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models

1 code implementation30 May 2022 Mengzhou Xia, Mikel Artetxe, Jingfei Du, Danqi Chen, Ves Stoyanov

In this work, we adapt prompt-based few-shot learning to ELECTRA and show that it outperforms masked language models in a wide range of tasks.

Few-Shot Learning Text Infilling

ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection

no code implementations25 May 2022 Badr AlKhamissi, Faisal Ladhak, Srini Iyer, Ves Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab

Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next.

Cultural Vocal Bursts Intensity Prediction Few-Shot Learning +1

On the Role of Bidirectionality in Language Model Pre-Training

no code implementations24 May 2022 Mikel Artetxe, Jingfei Du, Naman Goyal, Luke Zettlemoyer, Ves Stoyanov

Prior work on language model pre-training has explored different architectures and learning objectives, but differences in data, hyperparameters and evaluation make a principled comparison difficult.

Language Modelling Text Infilling

Efficient Large Scale Language Modeling with Mixtures of Experts

no code implementations20 Dec 2021 Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giri Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, Ves Stoyanov

This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language modeling, zero- and few-shot priming, and full-shot fine-tuning.

Language Modelling

Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning

1 code implementation ICLR 2021 Beliz Gunel, Jingfei Du, Alexis Conneau, Ves Stoyanov

Our proposed fine-tuning objective leads to models that are more robust to different levels of noise in the fine-tuning training data, and can generalize better to related tasks with limited labeled data.

Contrastive Learning Data Augmentation +4

Conversational Semantic Parsing

no code implementations EMNLP 2020 Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Mike Haeger, Haoran Li, Yashar Mehdad, Ves Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta

In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session.

dialog state tracking Semantic Parsing

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

41 code implementations ACL 2020 Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdel-rahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer

We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.

Abstractive Text Summarization Denoising +5

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