Search Results for author:
Found 15 papers, 8 papers with code
bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark
We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark.
LEVER: Learning to Verify Language-to-Code Generation with Execution
The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation.
Ranked #1 on
Code Generation
on MBPP
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
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.
Ranked #15 on
Natural Language Inference
on RTE
Training Trajectories of Language Models Across Scales
Why do larger language models demonstrate more desirable behaviors?
Complementary Explanations for Effective In-Context Learning
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
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.
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
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
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.
Efficient Language Modeling with Sparse all-MLP
All-MLP architectures have attracted increasing interest as an alternative to attention-based models.
Ranked #1 on
Zero-Shot Learning
on HellaSwag
Efficient Large Scale Language Modeling with Mixtures of Experts
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.
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
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.
Self-training Improves Pre-training for Natural Language Understanding
Unsupervised pre-training has led to much recent progress in natural language understanding.
Conversational Semantic Parsing
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.
Preserving Integrity in Online Social Networks
Online social networks provide a platform for sharing information and free expression.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
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.
Ranked #3 on
Open-Domain Question Answering
on ELI5
