4 papers
Are You Sure Your LLM Is Sure? Repairing and Auditing Confidence
Arijit Dey, Bhartendu Kumar, Ramya Hebbalaguppe +1
Confidence scores determine whether an LLM answer is accepted, reviewed, or regenerated, yet scalar recalibration can only correct errors visible through the score itself. We intro…
When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs
Tanay Sodha, Aditya Sharma, Ramya Hebbalaguppe +2
Reliable confidence estimation remains a key limitation of test-time adaptation in vision-language models (VLMs), where prompt tuning improves zero-shot accuracy but often degrades…
Enhancing Deep Neural Network Reliability with Refinement and Calibration
Ramya Hebbalaguppe, Ajay Shastry, Soumya Suvra Ghosal +1
Although deep neural networks (DNNs) achieve high predictive accuracy, their confidence estimates are often unreliable, potentially compromising user trust in their decisions. This…
Prompting without Panic: Attribute-aware, Zero-shot, Test-Time Calibration
Ramya Hebbalaguppe, Tamoghno Kandar, Abhinav Nagpal +1
Vision-language models (VLM) have demonstrated impressive performance in image recognition by leveraging self-supervised training on large datasets. Their performance can be furthe…