activity
20192026
most citedBi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text

3 citations · 3 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CL2026

Instella-MoE Technical Report

Jiang Liu, Sudhanshu Ranjan, Prakamya Mishra +10

In this work, we introduce Instella-MoE, a fully open Mixture-of-Experts (MoE) language model with 16 billion total parameters and 2.8 billion active parameters per token, trained…

cs.CL2025

Instella: Fully Open Language Models with Stellar Performance

Jiang Liu, Jialian Wu, Xiaodong Yu +10

Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet the majority of high-performing models remain closed-source or partially ope…

cs.CL2025

SAND-Math: Using LLMs to Generate Novel, Difficult and Useful Mathematics Questions and Answers

Chaitanya Manem, Pratik Prabhanjan Brahma, Prakamya Mishra +2

The demand for Large Language Models (LLMs) at multiple scales, capable of sophisticated and sound mathematical reasoning, continues to grow. However, the development of performant…

cs.CL2025

TTT-Bench: A Benchmark for Evaluating Reasoning Ability with Simple and Novel Tic-Tac-Toe-style Games

Prakamya Mishra, Jiang Liu, Jialian Wu +3

Large reasoning models (LRMs) have demonstrated impressive reasoning capabilities across a broad range of tasks including Olympiad-level mathematical problems, indicating evidence…

cs.CL2024

SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization

Prakamya Mishra, Zonghai Yao, Parth Vashisht +4

Large Language Models (LLMs) such as GPT & Llama have demonstrated significant achievements in summarization tasks but struggle with factual inaccuracies, a critical issue in clini…

cs.CL2023

Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization

Prakamya Mishra, Zonghai Yao, Shuwei Chen +3

Large Language Models (LLMs) like the GPT and LLaMA families have demonstrated exceptional capabilities in capturing and condensing critical contextual information and achieving st…

cs.CL2020

STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning

Prakamya Mishra

In this paper, we present a novel multi-modal deep neural network architecture that uses speech and text entanglement for learning phonetically sound spoken-word representations. S…

cs.CL2020★ 3 cited

Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text

Prakamya Mishra, Saroj Kaushik, Kuntal Dey

Many online comments on social media platforms are hateful, humorous, or sarcastic. The sarcastic nature of these comments (especially the short ones) alters their actual implied s…

cs.CL2020

Contextualized Spoken Word Representations from Convolutional Autoencoders

Prakamya Mishra, Pranav Mathur

A lot of work has been done to build text-based language models for performing different NLP tasks, but not much research has been done in the case of audio-based language models.…

cs.SI2019

Correlated Feature Selection for Tweet Spam Classification

Prakamya Mishra

The identification of spam messages on social networks is a very challenging task. Social media sites like Twitter \& Facebook attracts a lot of users and companies to advertise an…