Papers by Anum Afzal
DA-Pred: Performance Prediction for Text Summarization under Domain-Shift and Instruct-Tuning (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) often don’t perform as expected under Domain Shift or after Instruct-tuning. |
| Approach: | They propose a method that uses the known performance in high-resource domains and fine-tuning settings to predict performance in low-resourced domains or base models. |
| Outcome: | The proposed method can help researchers decide if resources should be allocated for data labeling and LLM Instruct-tuning. |
Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion (2025.findings-acl)
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| Challenge: | Using later reasoning steps does not always improve classification, suggesting LLMs encode key information early. |
| Approach: | They propose a method to predict the success of a zero-shot Chain-of-Thought process by using LLM representations that are based on initial steps representations. |
| Outcome: | The proposed method performs well even before a single token is generated, suggesting that crucial information about the reasoning process is already present in the initial steps representations. |
Beyond Grid Search: Leveraging Bayesian Optimization for Accelerating RAG Pipeline Optimization (2026.eacl-industry)
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| Challenge: | Finding optimal configurations via grid search is computationally prohibitive, limiting real-world scalability. |
| Approach: | They compare BO with grid search to find optimal configurations for RAG pipelines . their framework explores global pipeline and local component-wise optimization . |
| Outcome: | The proposed approach reduces optimization time by up to 84% while maintaining comparable accuracy. |