Summary of Auto-gda: Automatic Domain Adaptation For Efficient Grounding Verification in Retrieval-augmented Generation, by Tobias Leemann et al.
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation
by Tobias Leemann, Periklis Petridis, Giuseppe Vietri, Dionysis Manousakas, Aaron Roth, Sergul Aydore
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper addresses the issue of hallucination in large language model (LLM) outputs, where models generate incorrect or irrelevant information. The current detection strategy involves re-prompting the LLM to assess whether its response is grounded in retrieved evidence, but this approach is costly. To improve grounding verification efficiency, lightweight natural language inference (NLI) models can be used at inference time. However, existing pre-trained NLI models have subpar performance on realistic retrieval-augmented generation (RAG) inputs due to the complexity of RAG inputs and lack of labeled instances in the target domain. The authors introduce Automatic Generative Domain Adaptation (Auto-GDA), a framework for unsupervised domain adaptation through synthetic data generation, which enables iterative improvement of generated samples using weak labels from less efficient teacher models and discrete optimization. Experimental results demonstrate the effectiveness of Auto-GDA, with fine-tuned models often surpassing teacher model performance at 10% of the computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making sure that language models don’t make up false information. Right now, these models can sometimes generate incorrect or irrelevant answers. The usual way to check if an answer is correct is to ask the model another question, but this method is not very efficient. To solve this problem, the authors propose a new approach called Automatic Generative Domain Adaptation (Auto-GDA). This method uses fake data to train the model and make it better at checking its own answers. The results show that Auto-GDA works well and can even surpass the performance of larger models while using much less computer power. |
Keywords
» Artificial intelligence » Domain adaptation » Grounding » Hallucination » Inference » Large language model » Optimization » Prompting » Rag » Retrieval augmented generation » Synthetic data » Teacher model » Unsupervised
