Papers by Abhishek Divekar
SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation (2024.emnlp-main)
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| Challenge: | Prior approaches to synthesis use few-shot prompting, which relies on the LLM’s parametric knowledge to generate usable examples. |
| Approach: | They propose to use a dataset to generate examples of each label from the LLM. |
| Outcome: | The proposed model significantly improves lexical and semantic diversity, similarity to human-written text, and distillation performance, when compared to 32-shot prompting and four prior approaches. |
CorrSynth - A Correlated Sampling Method for Diverse Dataset Generation from LLMs (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance in diverse tasks using zero-shot and few-shot prompting. |
| Approach: | They propose a method which generates data that is more diverse and faithful to the input prompt using a correlated sampling strategy. |
| Outcome: | The proposed method overcomes the complexity drawbacks of other guidance-based techniques and improves student metrics and intrinsic metrics upon competitive baselines across four datasets. |