Summary of A Formalism and Approach For Improving Robustness Of Large Language Models Using Risk-adjusted Confidence Scores, by Ke Shen and Mayank Kejriwal
A Formalism and Approach for Improving Robustness of Large Language Models Using Risk-Adjusted Confidence Scores
by Ke Shen, Mayank Kejriwal
First submitted to arxiv on: 5 Oct 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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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 research paper investigates the risks posed by Large Language Models (LLMs) like ChatGPT in natural language processing (NLP). The authors define two types of risk: decision risk and composite risk, and propose a risk-centric evaluation framework to assess LLMs on these risks. They also introduce four novel metrics for measuring LLM performance in both in-domain and out-of-domain settings. Furthermore, the paper presents a risk-adjusted calibration method called DwD that helps LLMs minimize risks in an overall NLI architecture. The authors demonstrate the practical utility of their framework and the efficacy of DwD using detailed experiments on four NLI benchmarks, three baselines, and two LLMs including ChatGPT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at big language models like ChatGPT that can understand and generate human-like text. While these models are super smart, they also have some major risks. The authors of this paper want to know more about what those risks are, so they come up with two kinds of risk: decision risk and composite risk. They also create a special way to measure how well the models do on these risks. This is important because it helps us understand how we can make sure these models don’t make mistakes or cause problems. |
Keywords
* Artificial intelligence * Natural language processing * Nlp
