Summary of Synthesizrr: Generating Diverse Datasets with Retrieval Augmentation, by Abhishek Divekar and Greg Durrett
SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
by Abhishek Divekar, Greg Durrett
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach, Synthesize by Retrieval and Refinement (SynthesizRR), to distill the capabilities of large language models into smaller student models for classification tasks. The method uses retrieval augmentation to introduce variety in dataset synthesis, generating examples of each label from the LLM. This is achieved by seeding the LLM with different content retrieved from passages, leading to greater lexical and semantic diversity, similarity to human-written text, and distillation performance compared to prior approaches. SynthesizRR demonstrates improved results on six datasets covering topic classification, sentiment analysis, tone detection, and humor, which require complex synthesis strategies. The authors release their code to perform all steps at https://github.com/amazon-science/synthesizrr. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how we can make large language models smaller and easier to use. It does this by making a new way to create training examples for these models, which is called dataset synthesis. The old way used the model’s knowledge to generate examples, but it had some problems. This new method, called SynthesizRR, uses retrieved passages to introduce variety in the training examples and makes them more like human-written text. The authors tested their approach on six different tasks and found that it worked better than other methods. They released their code so others can use it too. |
Keywords
» Artificial intelligence » Classification » Distillation
