Papers by Sinchana Ramakanth Bhat
CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering (2023.emnlp-industry)
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Md Rashad Al Hasan Rony, Christian Suess, Sinchana Ramakanth Bhat, Viju Sudhi, Julia Schneider, Maximilian Vogel, Roman Teucher, Ken Friedl, Soumya Sahoo
| Challenge: | Large language models (LLMs) have demonstrated remarkable performance by following natural language instructions without fine-tuning them on domain-specific tasks and data. |
| Approach: | They propose an in-car retrieval-augmented conversational question-answering system that uses large language models to generate natural, safe and domain-specific answers. |
| Outcome: | The proposed system outperforms state-of-the-art LLMs in generating safe and domain-specific answers. |
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler (2024.lrec-main)
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| Challenge: | State-of-the-art intent classification and slot filling methods rely on data-intensive deep learning models . large language models exhibit remarkable zero-shot performance across various natural language tasks. |
| Approach: | They propose an approach framing IC and SF as language generation tasks for instruction-LLMs with a more efficient SF-prompting method. |
| Outcome: | The proposed approach outperforms state-of-the-art IC+SF method and in-context learning methods with GPT3.5 (175B). |