Papers by Joshua Robinson
An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels (2022.acl-long)
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Taylor Sorensen, Joshua Robinson, Christopher Rytting, Alexander Shaw, Kyle Rogers, Alexia Delorey, Mahmoud Khalil, Nancy Fulda, David Wingate
| Challenge: | Existing prompt engineering methods require labeled data and access to model parameters . a new method for selecting prompt templates without labeles and without direct access to the model is needed. |
| Approach: | They propose a method for selecting prompt templates without labeled examples and without direct access to the model. |
| Outcome: | The proposed method performs at almost oracle levels, without labels, on 7 datasets representing 7 different NLP tasks. |
On Retrieval Augmentation and the Limitations of Language Model Training (2024.naacl-short)
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| Challenge: | Recent efforts to improve the performance of language models (LMs) have focused on scaling up model and training data size, though with steep accompanying energy and compute resource costs. |
| Approach: | They propose to augment a language model with k-nearest neighbors retrieval on its training data to reduce its perplexity. |
| Outcome: | The proposed model reduces storage costs by over 25x compared to traditional retrieval methods for GPT-2 and Mistral 7B . |