Papers by Settaluri Lakshmi Sravanthi
Understand the Implication: Learning to Think for Pragmatic Understanding (2025.findings-acl)
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| Challenge: | Existing methods rely on annotated labels but overlook the reasoning process humans naturally use to interpret implicit meaning. |
| Approach: | They propose a dataset that includes explicit reasoning for both correct and incorrect interpretations and propose supervised fine-tuning to improve their performance. |
| Outcome: | The proposed dataset improves LLMs' pragmatic understanding by 11.12% across model families and 16.10% over label trained models. |
RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering (2025.findings-emnlp)
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Settaluri Lakshmi Sravanthi, Pulkit Agarwal, Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda, Ankit Mishra, Kiran Pradeep, Srihari K B, Godawari Sudhakar Rao, Pushpak Bhattacharyya
| Challenge: | Existing methods to improve the reasoning capabilities of VQA systems are limited due to complexity of graph neural networks and end-to-end training. |
| Approach: | They propose a method to integrate Dense Passage Retrievers with Vision Language Models to boost the reasoning capabilities of VQA systems. |
| Outcome: | The proposed method outperforms human accuracy and GPT-4 in the ScienceQA dataset. |
From Perception to Reasoning: Enhancing Vision-Language Models for Mobile UI Understanding (2025.findings-acl)
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Settaluri Lakshmi Sravanthi, Ankit Mishra, Debjyoti Mondal, Subhadarshi Panda, Rituraj Singh, Pushpak Bhattacharyya
| Challenge: | Accurately grounding visual and textual elements within mobile user interfaces remains a challenge for Vision-Language Models (VLMs). |
| Approach: | They propose a mobile UI understanding model trained on a dataset specifically tailored for mobile screen understanding and grounding. |
| Outcome: | The proposed model achieves significant gains in accuracy across all perception tasks and on reasoning benchmarks. |