Papers with ICL accuracy
Parallel Structures in Pre-training Data Yield In-Context Learning (2024.acl-long)
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| Challenge: | Pre-trained language models (LMs) are capable of in-context learning (ICL) however, it is unclear where this ability comes from as there is a stark distribution shift between pre-training text and ICL prompts. |
| Approach: | They find that pre-trained language models are capable of in-context learning (ICL) they detect parallel structures in the pre-training data and conduct ablation experiments to study their effect on ICL. |
| Outcome: | The proposed model can adapt to a task with a few examples given in the prompt without any parameter update. |
UCS: Estimating Unseen Coverage for Improved In-Context Learning (2026.findings-acl)
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| Challenge: | Existing selection methods prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set. |
| Approach: | They propose a training-free, subset-level coverage prior that is unrevealed by a model-consistent embedding and a Smoothed Good-Turing estimator to estimate the number of unrevelled clusters within a candidate subset. |
| Outcome: | Experiments on multiple intent-classification and reasoning benchmarks show that augmenting strong baselines with UCS improves ICL accuracy by 2-6% under the same selection budget. |