Papers by Harvey Fu
Estimating Large Language Model Capabilities without Labeled Test Data (2023.findings-emnlp)
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| Challenge: | Large Language Models have shown impressive ability to perform in-context learning from only a few examples, but their accuracy varies widely from task to task. |
| Approach: | They propose a method that trains a meta-model using LLM confidence scores as features to perform ICL accuracy estimation. |
| Outcome: | The proposed method improves over baselines across 7 out of 12 settings and achieves the same accuracy as evaluating on 40 sampled examples per task. |
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench (2023.findings-emnlp)
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| Challenge: | a recent study shows that large language models can be used to predict performance on new configurations. |
| Approach: | They investigate the predictability of large language model capabilities by using BIG-bench . they find a subset of BIG-Bench tasks as informative as BIG-bnch Hard . |
| Outcome: | The proposed model achieves an R2 score greater than 95% on BIG-bench . the model is 3 smaller than BIG-Bench Hard, and the model performs better on the full set. |