Papers with training-free metric
Training-free Neural Architecture Search for RNNs and Transformers (2023.acl-long)
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| Challenge: | Neural architecture search (NAS) has allowed for the automatic creation of new and effective neural network architectures. |
| Approach: | They develop a new NAS metric that predicts the trained performance of an RNN architecture and significantly outperforms existing NAS metrics. |
| Outcome: | The proposed metric outperforms existing training-free metrics on the NAS-Bench-NLP benchmark. |
How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective (2026.acl-long)
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| Challenge: | Low-resource languages are a long-tail problem for multilingual LLMs due to limited high-quality training data. |
| Approach: | They propose a method that translates high-quality, knowledge-rich English data into low-resource languages . they propose SynRank, which leverages synthetic data as positive samples to train a classifier . |
| Outcome: | The proposed method matches handcrafted rule-based filtering by human experts and significantly improves knowledge-intensive tasks with less data. |
Valid Necessary: Diagnosing Latent Inefficiency in Chain-of-Thought (2026.findings-acl)
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| Challenge: | Existing reasoning step evaluators fail to distinguish “valid but inefficient” reasoning steps from necessary reasoning. |
| Approach: | They propose a training-free metric that identifies low-utility steps and a post-hoc compression strategy to quantify their impact on token usage. |
| Outcome: | The proposed metric reduces token consumption by 31–53% while maintaining accuracy at substantially higher compression rates. |