Papers with model-free approach
Probing via Prompting (2022.naacl-main)
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| Challenge: | Pre-trained language models have increased the performance of data-driven natural language processing (NLP) models on a wide variety of tasks. |
| Approach: | They propose a model-free approach to probing via prompting which formulates probing as a prompting task and combine pruning to analyze where the model stores the linguistic information in its architecture. |
| Outcome: | The proposed approach extracts information from pre-trained models while learning much less on its own. |
Accelerated Test-Time Scaling with Model-Free Speculative Sampling (2025.emnlp-main)
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Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin, Aram Galstyan, Sravan Babu Bodapati
| Challenge: | Language models have demonstrated remarkable capabilities in reasoning tasks through test-time scaling techniques like best-of-N sampling and tree search. |
| Approach: | They propose a model-free speculative decoding approach that exploits redundancy in reasoning trajectories to achieve significant acceleration without compromising accuracy. |
| Outcome: | The proposed approach reduces inference latency by 60-65% while maintaining accuracy. |