Summary of Reasoning Over Uncertain Text by Generative Large Language Models, By Aliakbar Nafar et al.
Reasoning over Uncertain Text by Generative Large Language Models
by Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi
First submitted to arxiv on: 14 Feb 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 This paper examines the challenges Large Language Models (LLMs) face when processing text with explicitly quantified probabilities, which is crucial in various contexts such as everyday conversations and medical decision-making. Despite advancements in mathematical reasoning, LLMs struggle with probabilistic reasoning tasks. To address this limitation, we introduce the Bayesian Linguistic Inference Dataset (BLInD), designed to test LLMs’ probabilistic reasoning capabilities. We utilize BLInD to identify LLM limitations for probabilistic reasoning tasks and propose various prompting strategies mapping problems to formal representations like Python code, probabilistic algorithms, and logical programming. Our evaluation on BLInD demonstrates the effectiveness of our proposed methods across multiple LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) can understand text that includes probabilities. This is important in many areas, such as having a conversation or making medical decisions. Even though LLMs have gotten better at math problems, they still struggle with using those probabilities to make smart decisions. To help them get better, the researchers created a special dataset called BLInD to test how well LLMs can use probabilities. They also tried different ways of asking questions that relate to programming and logical thinking. The results show that their methods work well for many different LLMs. |
Keywords
» Artificial intelligence » Inference » Prompting
