Papers with training-free adaptation framework
D-CoDe: Scaling Image-Pretrained VLMs to Video via Dynamic Compression and Question Decomposition (2025.emnlp-main)
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| Challenge: | Existing training-free adaptation frameworks for video large language models are limited by the scarcity of high-quality video-text data. |
| Approach: | They propose a training-free adaptation framework that incorporates dynamic compression and question decomposition to address perception bottleneck and token overload. |
| Outcome: | The proposed framework improves video understanding across various benchmarks and on long-video benchmarks. |
Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning (2025.emnlp-main)
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| Challenge: | Existing approaches to improve mathematical reasoning require extensive datasets for training or depend on few-shot methods that compromise computational accuracy. |
| Approach: | They propose a training-free adaptation framework that efficiently equips general-purpose pre-trained language models with enhanced mathematical reasoning capabilities. |
| Outcome: | The proposed framework outperforms Qwen2.5-72B-Math-Instruct on MMLU-STEM with a score of 90.9%, compared to 87.3%. |
Training-Free Test-Time Contrastive Learning for Large Language Models (2026.findings-acl)
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| Challenge: | Existing training-free alternatives to training-based models are static or depend on external guidance. |
| Approach: | They propose a training-free adaptation framework that enables a frozen LLM to improve online by distilling supervision from its own inference experiences. |
| Outcome: | The proposed framework outperforms existing test-time adaptation methods under online evaluation. |