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Found 21 papers, 5 papers with code
Fewer Truncations Improve Language Modeling
In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens.
MyCrunchGPT: A chatGPT assisted framework for scientific machine learning
To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a webapp based guided user interface, that includes options for a comprehensive summary report.
A Static Evaluation of Code Completion by Large Language Models
Large language models trained on code have shown great potential to increase productivity of software developers.
Real-Time Prediction of Gas Flow Dynamics in Diesel Engines using a Deep Neural Operator Framework
As an alternative to physics based models, we develop an operator-based regression model (DeepONet) to learn the relevant output states for a mean-value gas flow engine model using the engine operating conditions as input variables.
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Such large models incur significant resource usage (in terms of memory, latency, and dollars) as well as carbon footprint.
ReCode: Robustness Evaluation of Code Generation Models
Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.
Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models
Natural language often contains ambiguities that can lead to misinterpretation and miscommunication.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
An Analysis of the Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation
We present a systematic analysis of the impact of decoding algorithms on LM fairness, and analyze the trade-off between fairness, diversity and quality.
On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations
Multiple metrics have been introduced to measure fairness in various natural language processing tasks.
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal
Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings.
Industry Scale Semi-Supervised Learning for Natural Language Understanding
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks.
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification
We make use of a conditional generator for data augmentation that is trained directly using the meta-learning objective and simultaneously with prototypical networks, hence ensuring that data augmentation is customized to the task.
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation
To systematically study and benchmark social biases in open-ended language generation, we introduce the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset that consists of 23, 679 English text generation prompts for bias benchmarking across five domains: profession, gender, race, religion, and political ideology.
Data Augmentation using Pre-trained Transformer Models
Language model based pre-trained models such as BERT have provided significant gains across different NLP tasks.
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification
In particular, we show that (a) upsampling in latent space is a competitive baseline for feature space augmentation (b) adding the difference between two examples to a new example is a simple yet effective data augmentation method.
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity
In addition, methods optimizing diversity can reduce training data in many cases to 50% with little impact on performance.
Natural Language Understanding
Task-Oriented Dialogue Systems
Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments.
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
To address this problem, we present an open source Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks from a broad range of viewing angles (-32. 5 degrees to 54. 0 degrees).
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.
