Summary of Towards Reliable Latent Knowledge Estimation in Llms: Zero-prompt Many-shot Based Factual Knowledge Extraction, by Qinyuan Wu et al.
Towards Reliable Latent Knowledge Estimation in LLMs: Zero-Prompt Many-Shot Based Factual Knowledge Extraction
by Qinyuan Wu, Mohammad Aflah Khan, Soumi Das, Vedant Nanda, Bishwamittra Ghosh, Camila Kolling, Till Speicher, Laurent Bindschaedler, Krishna P. Gummadi, Evimaria Terzi
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 called Zero-Prompt Latent Knowledge Estimator (ZP-LKE) to reliably estimate factual knowledge embedded in large language models (LLMs). The method eliminates prompt engineering, leveraging in-context learning to communicate questions and expected answer formats. ZP-LKE is demonstrated to surface more latent knowledge than prior approaches, with design choices affecting performance. A large-scale evaluation of open-source LLMs like OPT, Pythia, Llama(2), Mistral, Gemma, etc., over Wikidata relations and facts reveals differences in factual knowledge between model families, sizes, and finetuned counterparts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how well large language models know certain facts. It creates a new way to ask these models questions without needing special instructions. The method works by giving the model both the question and what it should look like as an answer. This makes it easier to find out what the model knows, but doesn’t depend on the specific type of model used. By testing many different language models, we can see that some know more facts than others, and even how good or bad they are at knowing certain types of information. |
Keywords
» Artificial intelligence » Llama » Prompt
