6 citations · 6 across the 5 of their papers we have counts for
9 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…
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…
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…