Papers by Jesus Solano
SparseFit: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations (2024.acl-long)
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| Challenge: | Models that generate natural language explanations (NLEs) for their predictions often require large datasets of human-written NLEs at training time, which can be expensive and time-consuming to collect. |
| Approach: | They propose a sparse few-shot fine-tuning strategy that leverages discrete prompts to jointly generate predictions and NLEs. |
| Outcome: | The proposed approach compares sparse few-shot fine-tuning with existing parametric fine- tuning techniques on three sizes of the T5 language model and four datasets and produces competitive results for both task performance and NLE quality. |