b'Rahul Gupta'

research

∙ 08/08/2023

FLIRT: Feedback Loop In-context Red Teaming

Warning: this paper contains content that may be inappropriate or offens...

0 Ninareh Mehrabi, et al. ∙

research

∙ 05/19/2023

Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning

Large Language Models (LLMs) are known to memorize significant portions ...

0 Mustafa Safa Ozdayi, et al. ∙

research

∙ 05/17/2023

"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation

Transgender and non-binary (TGNB) individuals disproportionately experie...

1 Anaelia Ovalle, et al. ∙

research

∙ 02/23/2023

MUTANT: A Multi-sentential Code-mixed Hinglish Dataset

The multi-sentential long sequence textual data unfolds several interest...

6 Rahul Gupta, et al. ∙

research

∙ 11/17/2022

Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models

Natural language often contains ambiguities that can lead to misinterpre...

5 Ninareh Mehrabi, et al. ∙

research

∙ 10/31/2022

Design Considerations For Hypothesis Rejection Modules In Spoken Language Understanding Systems

Spoken Language Understanding (SLU) systems typically consist of a set o...

0 Aman Alok, et al. ∙

research

∙ 10/07/2022

An Analysis of the Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation

Several prior works have shown that language models (LMs) can generate t...

8 Jwala Dhamala, et al. ∙

research

∙ 08/02/2022

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

In this work, we demonstrate that multilingual large-scale sequence-to-s...

1 Saleh Soltan, et al. ∙

research

∙ 05/26/2022

Differentially Private Decoding in Large Language Models

Recent large-scale natural language processing (NLP) systems use a pre-t...

8 Jimit Majmudar, et al. ∙

research

∙ 05/06/2022

Federated Learning with Noisy User Feedback

Machine Learning (ML) systems are getting increasingly popular, and driv...

39 Rahul Sharma, et al. ∙

research

∙ 05/03/2022

Training Mixed-Domain Translation Models via Federated Learning

Training mixed-domain translation models is a complex task that demands ...

6 Peyman Passban, et al. ∙

research

∙ 03/25/2022

On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations

Multiple metrics have been introduced to measure fairness in various nat...

0 Yang Trista Cao, et al. ∙

research

∙ 03/25/2022

Canary Extraction in Natural Language Understanding Models

Natural Language Understanding (NLU) models can be trained on sensitive ...

5 Rahil Parikh, et al. ∙

research

∙ 03/16/2022

Measuring Fairness of Text Classifiers via Prediction Sensitivity

With the rapid growth in language processing applications, fairness has ...

10 Satyapriya Krishna, et al. ∙

research

∙ 02/08/2022

Learnings from Federated Learning in the Real world

Federated Learning (FL) applied to real world data may suffer from sever...

12 Christophe Dupuy, et al. ∙

research

∙ 10/27/2021

Towards Realistic Single-Task Continuous Learning Research for NER

There is an increasing interest in continuous learning (CL), as data pri...

10 Justin Payan, et al. ∙

research

∙ 07/14/2021

An Efficient DP-SGD Mechanism for Large Scale NLP Models

Recent advances in deep learning have drastically improved performance o...

0 Christophe Dupuy, et al. ∙

research

∙ 06/21/2021

Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification

Existing bias mitigation methods to reduce disparities in model outcomes...

13 Yada Pruksachatkun, et al. ∙

research

∙ 01/29/2021

ADePT: Auto-encoder based Differentially Private Text Transformation

Privacy is an important concern when building statistical models on data...

7 Satyapriya Krishna, et al. ∙

research

∙ 01/28/2021

ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification

Practical sequence classification tasks in natural language processing o...

4 Varun Kumar, et al. ∙

research

∙ 01/27/2021

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

Recent advances in deep learning techniques have enabled machines to gen...

5 Jwala Dhamala, et al. ∙

research

∙ 10/08/2020

Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks

Neural Architecture Search (NAS) methods, which automatically learn enti...

0 Ansel MacLaughlin, et al. ∙

research

∙ 07/20/2020

Phase Transition Behavior in Knowledge Compilation

The study of phase transition behaviour in SAT has led to deeper underst...

9 Rahul Gupta, et al. ∙

research

∙ 05/22/2020

Automatic Discovery of Novel Intents Domains from Text Utterances

One of the primary tasks in Natural Language Understanding (NLU) is to r...

0 Nikhita Vedula, et al. ∙

research

∙ 05/16/2020

Towards classification parity across cohorts

Recently, there has been a lot of interest in ensuring algorithmic fairn...

17 Aarsh Patel, et al. ∙

research

∙ 05/06/2020

Joint Multi-Dimensional Model for Global and Time-Series Annotations

Crowdsourcing is a popular approach to collect annotations for unlabeled...

3 Anil Ramakrishna, et al. ∙

research

∙ 12/03/2019

Fast Intent Classification for Spoken Language Understanding

Spoken Language Understanding (SLU) systems consist of several machine l...

0 Akshit Tyagi, et al. ∙

research

∙ 10/31/2019

Modeling Feature Representations for Affective Speech using Generative Adversarial Networks

Emotion recognition is a classic field of research with a typical setup ...

18 Saurabh Sahu, et al. ∙

research

∙ 06/20/2019

One-vs-All Models for Asynchronous Training: An Empirical Analysis

Any given classification problem can be modeled using multi-class or One...

0 Rahul Gupta, et al. ∙

research

∙ 05/28/2019

Deep Learning for Bug-Localization in Student Programs

Providing feedback is an integral part of teaching. Most open online cou...

0 Rahul Gupta, et al. ∙

research

∙ 03/26/2019

On evaluating CNN representations for low resource medical image classification

Convolutional Neural Networks (CNNs) have revolutionized performances in...

0 Taruna Agrawal, et al. ∙

research

∙ 02/18/2019

Data augmentation for low resource sentiment analysis using generative adversarial networks

Sentiment analysis is a task that may suffer from a lack of data in cert...

0 Rahul Gupta, et al. ∙

research

∙ 01/02/2019

Verity: Blockchains to Detect Insider Attacks in DBMS

Integrity and security of the data in database systems are typically mai...

0 Shubham S. Srivastava, et al. ∙

research

∙ 10/31/2018

A task in a suit and a tie: paraphrase generation with semantic augmentation

Paraphrasing is rooted in semantics. We show the effectiveness of transf...

0 Su Wang, et al. ∙

research

∙ 09/25/2018

A Re-ranker Scheme for Integrating Large Scale NLU models

Large scale Natural Language Understanding (NLU) systems are typically t...

0 Chengwei Su, et al. ∙

research

∙ 06/18/2018

On Enhancing Speech Emotion Recognition using Generative Adversarial Networks

Generative Adversarial Networks (GANs) have gained a lot of attention fr...

0 Saurabh Sahu, et al. ∙

research

∙ 06/07/2018

Semi-supervised and Transfer learning approaches for low resource sentiment classification

Sentiment classification involves quantifying the affective reaction of ...

0 Rahul Gupta, et al. ∙

research

∙ 06/06/2018

Adversarial Auto-encoders for Speech Based Emotion Recognition

Recently, generative adversarial networks and adversarial autoencoders h...

0 Saurabh Sahu, et al. ∙

research

∙ 01/31/2018

Deep Reinforcement Learning for Programming Language Correction

Novice programmers often struggle with the formal syntax of programming ...

0 Rahul Gupta, et al. ∙

research

∙ 10/19/2017

SLING: A framework for frame semantic parsing

We describe SLING, a framework for parsing natural language into semanti...

0 Michael Ringgaard, et al. ∙

research

∙ 05/01/2010

Joint Structured Models for Extraction from Overlapping Sources

We consider the problem of jointly training structured models for extrac...

0 Rahul Gupta, et al. ∙

research

∙ 07/03/2009

Generalized Collective Inference with Symmetric Clique Potentials

Collective graphical models exploit inter-instance associative dependenc...

0 Rahul Gupta, et al. ∙

Rahul Gupta

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