Summary of Knowledge-based Consistency Testing Of Large Language Models, by Sai Sathiesh Rajan et al.
Knowledge-based Consistency Testing of Large Language Models
by Sai Sathiesh Rajan, Ezekiel Soremekun, Sudipta Chattopadhyay
First submitted to arxiv on: 3 Jul 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 This paper systematically examines the inconsistencies and knowledge gaps of Large Language Models (LLMs) by proposing an automated testing framework, KonTest. The framework utilizes a knowledge graph to create test cases that probe and measure the LLM’s knowledge via semantically-equivalent queries and test oracles. KonTest also mitigates knowledge gaps through a weighted ensemble of LLM models. Using four state-of-the-art LLMs (Falcon, Gemini, GPT3.5, and Llama2), the study reveals that KonTest generates error-inducing inputs, exposing a 16.5% knowledge gap across all tested LLMs. A mitigation method informed by KonTest’s test suite reduces this gap by 32.48%. The ablation study also shows that GPT3.5 is not suitable for consistency testing due to its limited effectiveness in knowledge construction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) know things. It finds that these models have a lot of mistakes and don’t really understand the world very well. To fix this, it creates a special tool called KonTest that can test if LLMs are correct or not. The tool uses a special graph to make sure the tests are fair and makes sure different parts of the model agree with each other. Using four popular models, the study shows that KonTest finds lots of mistakes (19.2% error rate) and reveals that these models don’t know as much as we thought they did (16.5% knowledge gap). The study also suggests a way to fix some of these problems by using different parts of the model together. |
Keywords
* Artificial intelligence * Gemini * Knowledge graph
