Search Results for author: Abhinav Sethy

Found 13 papers, 1 papers with code

Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models

no code implementations18 Feb 2024 Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long, Sudipta Kar, Abhinav Sethy, Joo-Kyung Kim, Dongyeop Kang

Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks.

Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models

no code implementations30 Oct 2023 Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, Abhinav Sethy

To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs.

Language Modelling Retrieval

Learning to Retrieve Engaging Follow-Up Queries

1 code implementation21 Feb 2023 Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Omar Zia Khan, Zeynab Raeesy, Abhinav Sethy

The retrieval system is trained on a dataset which contains ~14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates.

Retrieval valid

Label Dependent Deep Variational Paraphrase Generation

no code implementations27 Nov 2019 Siamak Shakeri, Abhinav Sethy

Generating paraphrases that are lexically similar but semantically different is a challenging task.

Machine Reading Comprehension Paraphrase Generation +1

Semi-Supervised Learning for Text Classification by Layer Partitioning

no code implementations26 Nov 2019 Alexander Hanbo Li, Abhinav Sethy

In this way, $F$ serves as a feature extractor that maps the input to high-level representation and adds systematical noise using dropout.

General Classification Sentence +2

Knowledge Distillation in Document Retrieval

no code implementations11 Nov 2019 Siamak Shakeri, Abhinav Sethy, Cheng Cheng

In this paper we show that knowledge distillation can be used to encourage a model that generates claim independent document encodings to mimic the behavior of a more complex model which generates claim dependent encodings.

Knowledge Distillation Retrieval

Knowledge Enhanced Attention for Robust Natural Language Inference

no code implementations31 Aug 2019 Alexander Hanbo Li, Abhinav Sethy

Neural network models have been very successful at achieving high accuracy on natural language inference (NLI) tasks.

Natural Language Inference

Differentiable Greedy Networks

no code implementations30 Oct 2018 Thomas Powers, Rasool Fakoor, Siamak Shakeri, Abhinav Sethy, Amanjit Kainth, Abdel-rahman Mohamed, Ruhi Sarikaya

Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization.

Claim Verification Combinatorial Optimization +1

Language Modeling with Highway LSTM

no code implementations19 Sep 2017 Gakuto Kurata, Bhuvana Ramabhadran, George Saon, Abhinav Sethy

Language models (LMs) based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

End-to-End ASR-free Keyword Search from Speech

no code implementations13 Jan 2017 Kartik Audhkhasi, Andrew Rosenberg, Abhinav Sethy, Bhuvana Ramabhadran, Brian Kingsbury

The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Diverse Embedding Neural Network Language Models

no code implementations22 Dec 2014 Kartik Audhkhasi, Abhinav Sethy, Bhuvana Ramabhadran

We propose Diverse Embedding Neural Network (DENN), a novel architecture for language models (LMs).

Language Modelling

Generalized Ambiguity Decomposition for Understanding Ensemble Diversity

no code implementations28 Dec 2013 Kartik Audhkhasi, Abhinav Sethy, Bhuvana Ramabhadran, Shrikanth. S. Narayanan

We present extensions of this decomposition to common regression and classification loss functions, and report a simulation-based analysis of the diversity term and the accuracy of the decomposition.

General Classification regression

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