Summary of Scenarios and Approaches For Situated Natural Language Explanations, by Pengshuo Qiu et al.
Scenarios and Approaches for Situated Natural Language Explanations
by Pengshuo Qiu, Frank Rudzicz, Zining Zhu
First submitted to arxiv on: 7 Jun 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 paper presents a benchmarking dataset called Situation-Based Explanation (SBE), which aims to evaluate the adaptation of large language models (LLMs) in generating natural language explanations (NLEs). The SBE dataset consists of 100 explanandums, each paired with three explanations tailored to distinct audience types, such as educators, students, and professionals. This allows for assessing how well LLMs adapt explanations to specific contexts. The authors examine the performance of various prompting methods, including rule-based, meta-prompting, and in-context learning prompts, on a range of pretrained language models with different sizes. The findings suggest that LLMs can generate prompts leading to more situation-specific explanations, but explicit modeling of an “assistant” persona is not necessary for situated NLE tasks. Furthermore, the in-context learning prompts only help LLMs learn demonstration templates without improving inference performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how language models adapt their natural language explanations to different situations and audiences. It creates a special dataset with 100 examples of things that need explaining, each with three explanations written for specific groups like teachers, students, or parents. The authors tested many language models with different sizes using different ways of asking questions, called prompting methods. They found that the language models can be taught to generate better explanations by using certain prompts, but they don’t always need a special assistant persona to do this. This research will help others make more accurate and helpful language models in the future. |
Keywords
» Artificial intelligence » Inference » Prompting
