Papers by Sahil Badyal
Deconstructing Instruction-Following: A New Benchmark for Granular Evaluation of Large Language Model Instruction Compliance Abilities (2026.eacl-long)
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| Challenge: | Existing benchmarks for ensuring Large Language Models (LLMs) follow complex instructions fail to reflect real-world use or isolate compliance from task success. |
| Approach: | They propose a modular framework that uses a dynamically generated dataset with up to 20 application-oriented generation constraints to enable a granular and independent analysis of LLM instruction compliance. |
| Outcome: | The proposed framework reveals that compliance is not a monolithic capability but varies significantly with constraint type, quantity, and position. |
Unsupervised Data Augmentation for Aspect Based Sentiment Analysis (2022.coling-1)
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| Challenge: | Recent approaches to Aspect-based Sentiment Analysis (ABSA) perform the subtasks of aspect term extraction (ATE) and aspect sentiment classification (ASC) simultaneously. |
| Approach: | They introduce an adaptation of Unsupervised Data Augmentation in semi-supervised learning that performs both aspects of Aspect-based Sentiment Analysis (ABSA) and aspect sentiment classification (ASC) they show that simple augmentations applied to modest-sized datasets along with consistency training lead to competitive performance with current ABSA state-of-the-art in restaurant and laptop domains . |
| Outcome: | The proposed approach performs well on a span-level classification task with minimal training data. |