activity
20152025
most citedObservations on LLMs for Telecom Domain: Capabilities and Limitations

31 citations · 92 across the 18 of their papers we have counts for

collaborators

19 papers

cs.CL2025

A Graph-based Approach for Multi-Modal Question Answering from Flowcharts in Telecom Documents

Sumit Soman, H. G. Ranjani, Sujoy Roychowdhury +4

Question-Answering (QA) from technical documents often involves questions whose answers are present in figures, such as flowcharts or flow diagrams. Text-based Retrieval Augmented…

eess.AS2025

Intelligibility of Text-to-Speech Systems for Mathematical Expressions

Sujoy Roychowdhury, H. G. Ranjani, Sumit Soman +3

There has been limited evaluation of advanced Text-to-Speech (TTS) models with Mathematical eXpressions (MX) as inputs. In this work, we design experiments to evaluate quality and…

cs.CL2025

Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecom

Rishika Sen, Sujoy Roychowdhury, Sumit Soman +2

Knowledge Distillation (KD) is one of the approaches to reduce the size of Large Language Models (LLMs). A LLM with smaller number of model parameters (student) is trained to mimic…

cs.IR2024

Evaluation of Table Representations to Answer Questions from Tables in Documents : A Case Study using 3GPP Specifications

Sujoy Roychowdhury, Sumit Soman, HG Ranjani +3

With the ubiquitous use of document corpora for question answering, one important aspect which is especially relevant for technical documents is the ability to extract information…

cs.SE2024★ 1 cited

Icing on the Cake: Automatic Code Summarization at Ericsson

Giriprasad Sridhara, Sujoy Roychowdhury, Sumit Soman +2

This paper presents our findings on the automatic summarization of Java methods within Ericsson, a global telecommunications company. We evaluate the performance of an approach cal…

cs.CL2024★ 4 cited

Evaluation of RAG Metrics for Question Answering in the Telecom Domain

Sujoy Roychowdhury, Sumit Soman, H G Ranjani +3

Retrieval Augmented Generation (RAG) is widely used to enable Large Language Models (LLMs) perform Question Answering (QA) tasks in various domains. However, RAG based on open-sour…