Papers with Statement-Tuning
Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot settings, but they struggle with extending to few- shot and zero- shot settings due to their architectural design. |
| Approach: | They propose a technique that models discriminative tasks as a set of finite statements and trains an encoder model to discriminate between the potential statements to determine the label. |
| Outcome: | The proposed method achieves competitive performance compared to state-of-the-art LLMs with significantly fewer parameters. |