Papers by Settaluri Sravanthi
IndiFoodVQA: Advancing Visual Question Answering and Reasoning with a Knowledge-Infused Synthetic Data Generation Pipeline (2024.findings-eacl)
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| Challenge: | Large Vision Language Models lack domain-specific data for reasoning on complex problems. |
| Approach: | They propose to use explicit knowledge-infused questions, answers, and reasons to answer and reason upon the questions. |
| Outcome: | The proposed model improves by 25% over the baseline model. |
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs’ Pragmatics Capabilities (2024.findings-acl)
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| Challenge: | Pragmatics understanding is not well studied in LLMs, but their understanding of pragmatics is lacking. |
| Approach: | They propose to use a dataset to measure LLMs' understanding of pragmatics to evaluate their models. |
| Outcome: | The proposed dataset includes 14 tasks in four pragmatics phenomena, namely; Implicature, Presupposition, Reference, and Deixis. |