Papers by Vijay Kumar Mago
Evaluating Structured Output Robustness of Small Language Models for Open Attribute-Value Extraction from Clinical Notes (2025.acl-srw)
Copied to clipboard
| Challenge: | Comparative analysis of structured outputs generated by small language models for open attribute-value extraction from clinical notes . structure of outputs improves with targeted prompting and larger models, but declines for longer documents and certain note types. |
| Approach: | They compare the parsability of structured outputs generated by small language models for open attribute-value extraction from clinical notes. |
| Outcome: | The proposed model performs well in open attribute-value extraction tasks, but fails to parse for longer documents and note types. |
Compact Multimodal Language Models as Robust OCR Alternatives for Noisy Textual Clinical Reports (2026.eacl-industry)
Copied to clipboard
| Challenge: | Conventional OCR systems perform poorly under noisy, real-world conditions . compact multimodal models outperform classical and neural OCR pipelines . |
| Approach: | They evaluate compact multimodal language models for transcribing noisy medical documents . they compare eight different models to find the best transcription accuracy and noise sensitivity . |
| Outcome: | The proposed models outperform classical and neural OCR pipelines in transcription accuracy, noise sensitivity, numeric accuracy and computational efficiency. |
From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models (2025.naacl-srw)
Copied to clipboard
| Challenge: | Using a synthetic sports feedback dataset, we evaluate open-weight LLMs’ ability to extract aspect-polarity pairs. |
| Approach: | They propose a metric to facilitate the evaluation of aspect extraction with generative models. |
| Outcome: | The proposed metric improves the performance of open-weight LLMs in the Aspect-Based Sentiment Analysis task. |