Search Results for author: Khyathi Raghavi Chandu

Found 19 papers, 2 papers with code

How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources

1 code implementation NeurIPS 2023 Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, Hannaneh Hajishirzi

Our evaluations show that the best model in any given evaluation reaches on average 87% of ChatGPT performance, and 73% of GPT-4 performance, suggesting that further investment in building better base models and instruction-tuning data is required to close the gap.

Instruction Following

Continual Dialogue State Tracking via Example-Guided Question Answering

no code implementations23 May 2023 Hyundong Cho, Andrea Madotto, Zhaojiang Lin, Khyathi Raghavi Chandu, Satwik Kottur, Jing Xu, Jonathan May, Chinnadhurai Sankar

Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services.

Continual Learning Dialogue State Tracking +3

Curriculum Script Distillation for Multilingual Visual Question Answering

no code implementations17 Jan 2023 Khyathi Raghavi Chandu, Alborz Geramifard

Pre-trained models with dual and cross encoders have shown remarkable success in propelling the landscape of several tasks in vision and language in Visual Question Answering (VQA).

Question Answering Visual Question Answering

Multilingual Multimodality: A Taxonomical Survey of Datasets, Techniques, Challenges and Opportunities

no code implementations30 Oct 2022 Khyathi Raghavi Chandu, Alborz Geramifard

To this end, we review the languages studied, gold or silver data with parallel annotations, and understand how these modalities and languages interact in modeling.

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching

no code implementations Findings (EMNLP) 2021 Parul Chopra, Sai Krishna Rallabandi, Alan W Black, Khyathi Raghavi Chandu

Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing.


CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing

1 code implementation NAACL (CALCS) 2021 Sai Muralidhar Jayanthi, Kavya Nerella, Khyathi Raghavi Chandu, Alan W Black

The NLP community has witnessed steep progress in a variety of tasks across the realms of monolingual and multilingual language processing recently.

Grounding 'Grounding' in NLP

no code implementations4 Jun 2021 Khyathi Raghavi Chandu, Yonatan Bisk, Alan W Black

And finally, (3) How to advance our current definition to bridge the gap with Cognitive Science?

Reading Between the Lines: Exploring Infilling in Visual Narratives

no code implementations EMNLP 2020 Khyathi Raghavi Chandu, Ruo-Ping Dong, Alan Black

In this paper, we tackle this problem by using \textit{infilling} techniques involving prediction of missing steps in a narrative while generating textual descriptions from a sequence of images.

Visual Storytelling

Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey

no code implementations14 Oct 2020 Khyathi Raghavi Chandu, Alan W Black

Neural text generation metamorphosed into several critical natural language applications ranging from text completion to free form narrative generation.

Image Captioning Machine Translation +2

Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models

no code implementations COLING 2022 Khyathi Raghavi Chandu, Piyush Sharma, Soravit Changpinyo, Ashish Thapliyal, Radu Soricut

Training large-scale image captioning (IC) models demands access to a rich and diverse set of training examples, gathered from the wild, often from noisy alt-text data.

Denoising Image Captioning

Style Variation as a Vantage Point for Code-Switching

no code implementations1 May 2020 Khyathi Raghavi Chandu, Alan W. black

We believe this viewpoint of CS as style variations opens new perspectives for modeling various tasks in CS text.

Language Modelling speech-recognition +3

Induction and Reference of Entities in a Visual Story

no code implementations15 Sep 2019 Ruo-Ping Dong, Khyathi Raghavi Chandu, Alan W. black

We also conduct human evaluation from which it is concluded that the visual stories generated by our model are preferred 82% of the times.

Visual Storytelling

"My Way of Telling a Story": Persona based Grounded Story Generation

no code implementations14 Jun 2019 Shrimai Prabhumoye, Khyathi Raghavi Chandu, Ruslan Salakhutdinov, Alan W. black

To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand.

Visual Storytelling

A Survey of Code-switched Speech and Language Processing

no code implementations25 Mar 2019 Sunayana Sitaram, Khyathi Raghavi Chandu, Sai Krishna Rallabandi, Alan W. black

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world.

Textually Enriched Neural Module Networks for Visual Question Answering

no code implementations23 Sep 2018 Khyathi Raghavi Chandu, Mary Arpita Pyreddy, Matthieu Felix, Narendra Nath Joshi

Problems at the intersection of language and vision, like visual question answering, have recently been gaining a lot of attention in the field of multi-modal machine learning as computer vision research moves beyond traditional recognition tasks.

Image Captioning Question Answering +2

Comparative Analysis of Neural QA models on SQuAD

no code implementations WS 2018 Soumya Wadhwa, Khyathi Raghavi Chandu, Eric Nyberg

The task of Question Answering has gained prominence in the past few decades for testing the ability of machines to understand natural language.

Information Retrieval Question Answering +2

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