Summary of Crafting Interpretable Embeddings by Asking Llms Questions, By Vinamra Benara et al.
Crafting Interpretable Embeddings by Asking LLMs Questions
by Vinamra Benara, Chandan Singh, John X. Morris, Richard Antonello, Ion Stoica, Alexander G. Huth, Jianfeng Gao
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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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 explores whether large language models can be made more interpretable through prompting, with the goal of applying these techniques to scientific domains like neuroscience. The authors introduce question-answering embeddings (QA-Emb), where each feature represents an answer to a yes/no question asked to the LLM. By selecting underlying questions rather than learning model weights, the authors demonstrate that QA-Emb can be trained efficiently and effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates how large language models can become more understandable by using prompts. The idea is to create embeddings that are easy to interpret, which would be very helpful in fields like neuroscience. To achieve this, the researchers created something called question-answering embeddings (QA-Emb). Each part of QA-Emb represents an answer to a simple yes or no question asked to the model. This approach makes training faster and more effective. |
Keywords
» Artificial intelligence » Prompting » Question answering
