5 papers · 1 filter
Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents
Ankan Mullick, Sombit Bose, Rounak Saha +6
In this paper, we introduce Spotlight, a novel paradigm for information extraction that produces concise, engaging narratives by highlighting the most compelling aspects of a docum…
Fine-tuning Language Models for Recipe Generation: A Comparative Analysis and Benchmark Study
Anneketh Vij, Changhao Liu, Rahul Anil Nair +3
This research presents an exploration and study of the recipe generation task by fine-tuning various very small language models, with a focus on developing robust evaluation metric…
Better RAG using Relevant Information Gain
Marc Pickett, Jeremy Hartman, Ayan Kumar Bhowmick +2
A common way to extend the memory of large language models (LLMs) is by retrieval augmented generation (RAG), which inserts text retrieved from a larger memory into an LLM's contex…
Leveraging the Power of LLMs: A Fine-Tuning Approach for High-Quality Aspect-Based Summarization
Ankan Mullick, Sombit Bose, Rounak Saha +6
The ever-increasing volume of digital information necessitates efficient methods for users to extract key insights from lengthy documents. Aspect-based summarization offers a targe…
On The Persona-based Summarization of Domain-Specific Documents
Ankan Mullick, Sombit Bose, Rounak Saha +5
In an ever-expanding world of domain-specific knowledge, the increasing complexity of consuming, and storing information necessitates the generation of summaries from large informa…