Summary of Generative Language Models with Retrieval Augmented Generation For Automated Short Answer Scoring, by Zifan Wang et al.
Generative Language Models with Retrieval Augmented Generation for Automated Short Answer Scoring
by Zifan Wang, Christopher Ormerod
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 A novel pipeline combining vector databases, transformer-based encoders, and Generative Language Models (GLMs) is proposed for Automated Short Answer Scoring (ASAS). The approach leverages off-the-shelf capabilities and performance in various domains to enhance short answer scoring accuracy. A GLM analyzes retrieved semantically similar responses from a vector database to determine scores. Fine-tuned retrieval processes and prompt engineering are used to optimize the system. Evaluation on the SemEval 2013 dataset shows significant improvement on SCIENTSBANK tasks compared to existing methods, highlighting the potential of GLMs in advancing ASAS technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated Short Answer Scoring is important for education assessment. Researchers are using new kinds of language models to make scoring better. This study uses these models to improve short answer scoring. They combine three things: a special kind of database that stores answers, a machine learning model called a transformer, and another type of model called a Generative Language Model. The GLM looks at similar answers in the database and gives scores based on what it sees. To make it work even better, they fine-tuned how it retrieves answers and made changes to the questions themselves. They tested their method on some data and found that it did much better than other methods. |
Keywords
» Artificial intelligence » Language model » Machine learning » Prompt » Transformer
