Papers by Vidhyakshaya Kannan
KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation (2026.acl-long)
Copied to clipboard
Nikita Tatarinov, Vidhyakshaya Kannan, Haricharana Srinivasa, Arnav Raj, Harpreet Singh Anand, Varun Singh, Aditya Luthra, Ravij Lade, Agam Shah, Sudheer Chava
| Challenge: | KG-MulQA extracts QA pairs at multiple complexity levels along three key dimensions: multi-hop retrieval, set operations, and answer plurality. |
| Approach: | They propose a framework that extracts QA pairs at multiple complexity levels along three key dimensions: multi-hop retrieval, set operations, and answer plurality. |
| Outcome: | The framework extracts QA pairs at multiple complexity levels along key dimensions . it enables fine-grained assessment of model performance across controlled difficulty levels. |
ConfReady: A RAG based Assistant and Dataset for Conference Checklist Responses (2025.emnlp-demos)
Copied to clipboard
Michael Galarnyk, Rutwik Routu, Vidhyakshaya Kannan, Kosha Bheda, Prasun Banerjee, Agam Shah, Sudheer Chava
| Challenge: | ARR Responsible NLP Research checklist is designed to encourage best practices for responsible research . previous research has shown that self-reported checklist responses don't always accurately represent papers . |
| Approach: | They propose a retrieval-augmented generation application that can be used to assist authors with conference checklists. |
| Outcome: | The proposed application can be used to help authors with conference checklists and review their work. |
T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Generic embedding models struggle to represent telecom-specific semantics . specialized terminology and ambiguous terms often limit their utility in retrieval and downstream tasks. |
| Approach: | They propose a domain-adapted embedding model fine-tuned from a gte-Qwen2-1.5B-instruct backbone. |
| Outcome: | The proposed model outperforms MPNet, BGE, Jina and E5 on a custom benchmark . it is open source and has a triplet loss objective . |