6 citations · 9 across the 16 of their papers we have counts for
18 papers
Learning Together to Perform Better: Teaching Small-Scale LLMs to Collaborate via Preferential Rationale Tuning
Sohan Patnaik, Milan Aggarwal, Sumit Bhatia +1
LLMssuch as GPT-4 have shown a remarkable ability to solve complex questions by generating step-by-step rationales. Prior works have utilized this capability to improve smaller and…
It Helps to Take a Second Opinion: Teaching Smaller LLMs to Deliberate Mutually via Selective Rationale Optimisation
Sohan Patnaik, Milan Aggarwal, Sumit Bhatia +1
Very large language models (LLMs) such as GPT-4 have shown the ability to handle complex tasks by generating and self-refining step-by-step rationales. Smaller language models (SLM…
CABINET: Content Relevance based Noise Reduction for Table Question Answering
Sohan Patnaik, Heril Changwal, Milan Aggarwal +3
Table understanding capability of Large Language Models (LLMs) has been extensively studied through the task of question-answering (QA) over tables. Typically, only a small part of…
Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems
Shivani Kumar, Sumit Bhatia, Milan Aggarwal +1
Sharing ideas through communication with peers is the primary mode of human interaction. Consequently, extensive research has been conducted in the area of conversational AI, leadi…
INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models
H S V N S Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia +4
A salient characteristic of pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and emergence of new capabilities with increasing mod…
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
Abhinav Java, Shripad Deshmukh, Milan Aggarwal +3
Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text…
LM-CORE: Language Models with Contextually Relevant External Knowledge
Jivat Neet Kaur, Sumit Bhatia, Milan Aggarwal +2
Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture factual knowledge in their parame…
Persuasion Strategies in Advertisements
Yaman Kumar Singla, Rajat Jha, Arunim Gupta +7
Modeling what makes an advertisement persuasive, i.e., eliciting the desired response from consumer, is critical to the study of propaganda, social psychology, and marketing. Despi…
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer
Rachit Bansal, Milan Aggarwal, Sumit Bhatia +2
Pre-trained Language Models (PTLMs) have been shown to perform well on natural language tasks. Many prior works have leveraged structured commonsense present in the form of entitie…
INDIGO: Intrinsic Multimodality for Domain Generalization
Puneet Mangla, Shivam Chandhok, Milan Aggarwal +2
For models to generalize under unseen domains (a.k.a domain generalization), it is crucial to learn feature representations that are domain-agnostic and capture the underlying sema…
Form2Seq : A Framework for Higher-Order Form Structure Extraction
Milan Aggarwal, Hiresh Gupta, Mausoom Sarkar +1
Document structure extraction has been a widely researched area for decades with recent works performing it as a semantic segmentation task over document images using fully-convolu…
Multi-Modal Association based Grouping for Form Structure Extraction
Milan Aggarwal, Mausoom Sarkar, Hiresh Gupta +1
Document structure extraction has been a widely researched area for decades. Recent work in this direction has been deep learning-based, mostly focusing on extracting structure usi…
TAN-NTM: Topic Attention Networks for Neural Topic Modeling
Madhur Panwar, Shashank Shailabh, Milan Aggarwal +1
Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take documen…
SHERLock: Self-Supervised Hierarchical Event Representation Learning
Sumegh Roychowdhury, Sumedh A. Sontakke, Nikaash Puri +5
Temporal event representations are an essential aspect of learning among humans. They allow for succinct encoding of the experiences we have through a variety of sensory inputs. Al…
Exploring Neural Models for Parsing Natural Language into First-Order Logic
Hrituraj Singh, Milan Aggrawal, Balaji Krishnamurthy
Semantic parsing is the task of obtaining machine-interpretable representations from natural language text. We consider one such formal representation - First-Order Logic (FOL) and…
Document Structure Extraction using Prior based High Resolution Hierarchical Semantic Segmentation
Mausoom Sarkar, Milan Aggarwal, Arneh Jain +2
Structure extraction from document images has been a long-standing research topic due to its high impact on a wide range of practical applications. In this paper, we share our find…
ReDecode Framework for Iterative Improvement in Paraphrase Generation
Milan Aggarwal, Nupur Kumari, Ayush Bansal +1
Generating paraphrases, that is, different variations of a sentence conveying the same meaning, is an important yet challenging task in NLP. Automatically generating paraphrases ha…
Improving Search through A3C Reinforcement Learning based Conversational Agent
Milan Aggarwal, Aarushi Arora, Shagun Sodhani +1
We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our app…