Summary of Benchmarking Uncertainty Quantification Methods For Large Language Models with Lm-polygraph, by Roman Vashurin et al.
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
by Roman Vashurin, Ekaterina Fadeeva, Artem Vazhentsev, Lyudmila Rvanova, Akim Tsvigun, Daniil Vasilev, Rui Xing, Abdelrahman Boda Sadallah, Kirill Grishchenkov, Sergey Petrakov, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov, Artem Shelmanov
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 addresses the issue of hallucinations and low-quality outputs in large language models (LLMs) by introducing a novel benchmark for uncertainty quantification (UQ). The authors implement a collection of state-of-the-art UQ baselines and provide an environment for evaluating novel techniques on various text generation tasks. They also assess confidence normalization methods’ ability to provide interpretable scores. A large-scale empirical investigation is conducted across eleven tasks, identifying the most effective approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem with language models that can be tricked into saying things that aren’t true. The authors created a special test to see how well different ways of measuring uncertainty work for these models. They looked at many different types of text and found which methods are the best for making sure language models give accurate answers. |
Keywords
* Artificial intelligence * Text generation
