Summary of A Survey on Uncertainty Quantification Of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions, by Ola Shorinwa et al.
A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions
by Ola Shorinwa, Zhiting Mei, Justin Lidard, Allen Z. Ren, Anirudha Majumdar
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has led to their widespread integration into various aspects of society. However, concerns about the reliability and trustworthiness of LLMs arise due to their tendency to generate hallucinations: plausible, factually-incorrect responses expressed with striking confidence. Previous research has shown that detecting these hallucinations is possible by examining the uncertainty of the LLM in its response to the pertinent prompt. This survey aims to provide an extensive review of existing uncertainty quantification methods for LLMs, highlighting their features, strengths, and weaknesses within a relevant taxonomy. The review also explores applications of uncertainty quantification methods for LLMs, including chatbot and textual applications to embodied artificial intelligence in robotics. Furthermore, the paper concludes with open research challenges in uncertainty quantification of LLMs, encouraging future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are great at generating text and solving problems. They’re being used more and more in many areas of life. However, there’s a concern that these models can produce false information that sounds believable. Researchers have found ways to detect when an LLM is giving wrong answers by looking at how sure the model is about its response. This survey takes a close look at different methods for measuring uncertainty in LLMs and explores what they’re good for, like making chatbots or robots more intelligent. The review also highlights some areas where there’s still work to be done. |
Keywords
» Artificial intelligence » Prompt
