Papers by Ameya Prabhu
ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities (2025.acl-long)
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| Challenge: | ONEBench enables custom benchmarks for specific capabilities while reusing and aggregating samples. |
| Approach: | They propose a new paradigm that consolidates individual evaluation datasets into a unified, ever-expanding sample pool. |
| Outcome: | The proposed model evaluation framework is based on dynamic, sample-level evaluation. |
Sampling Bias in Deep Active Classification: An Empirical Study (D19-1)
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| Challenge: | Existing studies on active learning identify sampling bias in large datasets . cost and time needed for labeling and model training are bottlenecks preventing new and/or better models from being trained . |
| Approach: | They propose to use active learning to identify representative data samples for training . they propose to create tiny datasets that can be used for cheap training if needed . |
| Outcome: | The proposed model outperforms the state-of-the-art on active text classification using small representative datasets with active learning. |