Summary of Large Language Models Are Null-shot Learners, by Pittawat Taveekitworachai et al.
Large Language Models are Null-Shot Learners
by Pittawat Taveekitworachai, Febri Abdullah, Ruck Thawonmas
First submitted to arxiv on: 16 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 This paper introduces null-shot prompting, a technique that exploits hallucination in large language models (LLMs) to improve performance on tasks. By instructing LLMs to utilize information from the “Examples” section that doesn’t exist within the provided context, researchers demonstrate improvements in reading comprehension, arithmetic reasoning, and closed-book question answering across eight datasets using eight LLMs. The results show varying degrees of inherent hallucination in each model, suggesting that null-shot prompting can be used to detect these differences. This technique is particularly relevant given the current limitations of LLMs, which still exhibit significant hallucination. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how large language models (LLMs) can get better at doing tasks by pretending information exists that doesn’t! Researchers call this “null-shot prompting” and it works by telling LLMs to use pretend information from a section called “Examples”. They tested this idea on eight different LLMs and found that it helped them do better on lots of tasks, like reading comprehension and math. The results also showed that each LLM is good at pretending in its own special way! This matters because right now, LLMs are still pretty bad at not making things up. |
Keywords
* Artificial intelligence * Hallucination * Prompting * Question answering
