Papers by Raghav Magazine
PromptWizard: Optimizing Prompts via Task-Aware, Feedback-Driven Self-Evolution (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs. |
| Approach: | They propose a framework for discrete prompt optimization that generates human-readable prompts using feedback-driven critique and synthesis process. |
| Outcome: | The proposed framework improves prompt quality across 45 tasks and reduces API calls, token usage and overall cost. |
Adaptive LLM Routing under Budget Constraints (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have revolutionized natural language processing, but their varying capabilities and costs pose challenges in practical applications. |
| Approach: | They propose to use LLM routing as a contextual bandit problem to enable adaptive decision-making . they use offline human preference data to refine the embeddings through online bandit feedback . |
| Outcome: | The proposed model is based on a contextual bandit problem enabling adaptive decision-making . it learns from offline human preference data and refines it through online bandit feedback. |