Summary of Towards Trustable Language Models: Investigating Information Quality Of Large Language Models, by Rick Rejeleene et al.
Towards Trustable Language Models: Investigating Information Quality of Large Language Models
by Rick Rejeleene, Xiaowei Xu, John Talburt
First submitted to arxiv on: 23 Jan 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 The paper presents a novel approach to evaluating the information quality generated by large language models (LLMs), which are increasingly relied upon for decision-making. Despite their remarkable capabilities, LLMs are prone to generating unreliable, biased, and even fabricated information due to tokenization challenges during pre-training. This issue can have severe consequences, such as flawed business decisions that impact economic activity. The authors introduce a mathematical framework for assessing the quality of LLM-generated information and highlight the scaling laws necessary to develop more trustworthy language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are creating lots of information fast, but we need to trust what they’re saying! Unfortunately, this information isn’t always reliable or true. This is because the way these models learn from text can lead to biased or made-up info. If we make decisions based on this fake information, it could cause problems in businesses and affect the economy. The researchers in this paper are trying to solve this problem by creating a new way to measure how good the information is that these language models generate. |
Keywords
* Artificial intelligence * Scaling laws * Tokenization
