Search Results for author: Vaibhav Kumar

Found 27 papers, 10 papers with code

An Empirical study to understand the Compositional Prowess of Neural Dialog Models

1 code implementation insights (ACL) 2022 Vinayshekhar Kumar, Vaibhav Kumar, Mukul Bhutani, Alexander Rudnicky

In this work, we examine the problems associated with neural dialog models under the common theme of compositionality.

Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models

no code implementations3 Mar 2024 Arijit Ghosh Chowdhury, Md Mofijul Islam, Faysal Hossain Shezan, Vaibhav Kumar, Vinija Jain, Aman Chadha

Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text.

Data Poisoning

Towards Explainable LiDAR Point Cloud Semantic Segmentation via Gradient Based Target Localization

1 code implementation19 Feb 2024 Abhishek Kuriyal, Vaibhav Kumar

Semantic Segmentation (SS) of LiDAR point clouds is essential for many applications, such as urban planning and autonomous driving.

Autonomous Driving Semantic Segmentation

A Survey on Integrated Sensing and Communication with Intelligent Metasurfaces: Trends, Challenges, and Opportunities

no code implementations28 Jan 2024 Ahmed Magbool, Vaibhav Kumar, Qingqing Wu, Marco Di Renzo, Mark F. Flanagan

Due to the potential of metasurfaces to enhance both communication and sensing performance, numerous papers have explored the performance gains of using metasurfaces to improve ISAC.

Autonomous Vehicles

X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects

no code implementations15 Nov 2023 Minqian Liu, Ying Shen, Zhiyang Xu, Yixin Cao, Eunah Cho, Vaibhav Kumar, Reza Ghanadan, Lifu Huang

Natural Language Generation (NLG) typically involves evaluating the generated text in various aspects (e. g., consistency and naturalness) to obtain a comprehensive assessment.

Dialogue Generation Language Modelling +2

Robust Beamforming Design for Fairness-Aware Energy Efficiency Maximization in RIS-Assisted mmWave Communications

no code implementations3 Jul 2023 Ahmed Magbool, Vaibhav Kumar, Mark F. Flanagan

In addition, adjusting the fairness design parameter can yield a favorable trade-off between energy efficiency and user fairness compared to methods that exclusively focus on optimizing one of these metrics.


Controlled Text Generation with Hidden Representation Transformations

1 code implementation30 May 2023 Vaibhav Kumar, Hana Koorehdavoudi, Masud Moshtaghi, Amita Misra, Ankit Chadha, Emilio Ferrara

We propose CHRT (Control Hidden Representation Transformation) - a controlled language generation framework that steers large language models to generate text pertaining to certain attributes (such as toxicity).

Attribute Contrastive Learning +2

SCA-Based Beamforming Optimization for IRS-Enabled Secure Integrated Sensing and Communication

no code implementations5 May 2023 Vaibhav Kumar, Marwa Chafii, A. Lee Swindlehurst, Le-Nam Tran, Mark F. Flanagan

Integrated sensing and communication (ISAC) is expected to be offered as a fundamental service in the upcoming sixth-generation (6G) communications standard.

A Low-Complexity Solution to Sum Rate Maximization for IRS-assisted SWIPT-MIMO Broadcasting

no code implementations28 Feb 2023 Vaibhav Kumar, Anastasios Papazafeiropoulos, Muhammad Fainan Hanif, Le-Nam Tran, Mark F. Flanagan

At the same time, the complexity of the proposed scheme grows linearly with the number of IRS elements while that of the benchmark scheme is proportional to the cube of the number of IRS elements.

On Energy Efficiency and Fairness Maximization in RIS-Assisted MU-MISO mmWave Communications

no code implementations15 Nov 2022 Ahmed Magbool, Vaibhav Kumar, Mark F. Flanagan

In the first stage, we maximize the energy efficiency, and in the second stage we maximize the fairness subject to a minimum energy efficiency constraint.


On the Energy-Efficiency Maximization for IRS-Assisted MIMOME Wiretap Channels

no code implementations1 Sep 2022 Anshu Mukherjee, Vaibhav Kumar, Derrick Wing Kwan Ng, Le-Nam Tran

Security and energy efficiency have become crucial features in the modern-era wireless communication.

A Novel SCA-Based Method for Beamforming Optimization in IRS/RIS-Assisted MU-MISO Downlink

1 code implementation25 Aug 2022 Vaibhav Kumar, Rui Zhang, Marco Di Renzo, Le-Nam Tran

In this letter, we consider the fundamental problem of jointly designing the transmit beamformers and the phase-shifts of the intelligent reflecting surface (IRS) / reconfigurable intelligent surface (RIS) to minimize the transmit power, subject to quality-of-service constraints at individual users in an IRS-assisted multiuser multiple-input single-output downlink communication system.

JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning

1 code implementation SemEval (NAACL) 2022 Yash Jakhotiya, Vaibhav Kumar, Ashwin Pathak, Raj Shah

In this paper, we train multiple Large Language Models in both the settings and achieve an F1 score (macro) of 0. 73 for the zero shot setting and an F1 score (macro) of 0. 85 for the one shot setting.

Binary Classification Classification +3

Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings

no code implementations28 Sep 2021 Tenzin Singhay Bhotia, Vaibhav Kumar, Tanmoy Chakraborty

Euclidean word embedding models such as GloVe and Word2Vec have been shown to reflect human-like gender biases.

Word Embeddings

Making Information Seeking Easier: An Improved Pipeline for Conversational Search

no code implementations Findings of the Association for Computational Linguistics 2020 Vaibhav Kumar, Jamie Callan

Given an input question, it uses a BERT-based classifier (trained with weak supervision) to de-contextualize the input by selecting relevant terms from the dialog history.

Conversational Search Passage Ranking +2

Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings

1 code implementation EMNLP (NLPOSS) 2020 Tenzin Singhay Bhotia, Vaibhav Kumar

Non-contextual word embedding models have been shown to inherit human-like stereotypical biases of gender, race and religion from the training corpora.

Word Embeddings

Ranking Clarification Questions via Natural Language Inference

no code implementations18 Aug 2020 Vaibhav Kumar, Vikas Raunak, Jamie Callan

Given a natural language query, teaching machines to ask clarifying questions is of immense utility in practical natural language processing systems.

Natural Language Inference Reading Comprehension

ClarQ: A large-scale and diverse dataset for Clarification Question Generation

1 code implementation ACL 2020 Vaibhav Kumar, Alan W. black

In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange.

Question Answering Question Generation +1

Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings

1 code implementation2 Jun 2020 Tenzin Singhay Bhotia, Vaibhav Kumar, Tanmoy Chakraborty

We also propose a new bias evaluation metric - Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections.

coreference-resolution Word Embeddings

On Compositionality in Neural Machine Translation

no code implementations4 Nov 2019 Vikas Raunak, Vaibhav Kumar, Florian Metze

We investigate two specific manifestations of compositionality in Neural Machine Translation (NMT) : (1) Productivity - the ability of the model to extend its predictions beyond the observed length in training data and (2) Systematicity - the ability of the model to systematically recombine known parts and rules.

Machine Translation NMT +1

On Dimensional Linguistic Properties of the Word Embedding Space

2 code implementations WS 2020 Vikas Raunak, Vaibhav Kumar, Vivek Gupta, Florian Metze

Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties.

Machine Translation Sentence +3

De-Mixing Sentiment from Code-Mixed Text

no code implementations ACL 2019 Yash Kumar Lal, Vaibhav Kumar, Mrinal Dhar, Manish Shrivastava, Philipp Koehn

The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words.

Sentence Sentiment Analysis +1

Writer Independent Offline Signature Recognition Using Ensemble Learning

1 code implementation19 Jan 2019 Sourya Dipta Das, Himanshu Ladia, Vaibhav Kumar, Shivansh Mishra

This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases.

Ensemble Learning

SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection

no code implementations2 Aug 2018 Vaibhav Kumar, Mrinal Dhar, Dhruv Khattar, Yash Kumar Lal, Abhimanshu Mishra, Manish Shrivastava, Vasudeva Varma

We generate sub-word level embeddings of the title using Convolutional Neural Networks and use them to train a bidirectional LSTM architecture.

Clickbait Detection Document Embedding +1

Enabling Code-Mixed Translation: Parallel Corpus Creation and MT Augmentation Approach

no code implementations COLING 2018 Mrinal Dhar, Vaibhav Kumar, Manish Shrivastava

With the help of the created parallel corpus, we analyzed the structure of English-Hindi code-mixed data and present a technique to augment run-of-the-mill machine translation (MT) approaches that can help achieve superior translations without the need for specially designed translation systems.

Machine Translation Sentence +1

Cannot find the paper you are looking for? You can Submit a new open access paper.