Skip to content
Trending storyDeveloping

Simple few-example prompts match complex methods on natural language inference

1 article1 sourcesince Nov 10Last article

Overview

AISummary of 1 article

Researchers report that a short, direct prompt with a few examples lets small open LLMs match more complex prompting methods on natural language inference.

The study tested five small models, including Gemma 2 9B, Mistral 7B and Llama 3.1 8B, on three NLI datasets using 0 to 15 examples and no reasoning steps.

According to a post by Rohan Paul summarizing the paper, Mistral 7B reached above 63.4% MNLI accuracy, beating the score a published in-context sampling method achieved with the same model.

Written by AI from the articles below · updated Nov 10, 9:00 PM ET

Check the sources:

Latest developmentNov 10, 8:39 PM ET
Simple few-example prompts match complex methods on natural language inference

Article timeline

The articles in this story. Times are ET.

Nov 10
  1. Rohan PaulX
    Simple few-example prompts match complex methods on natural language inference

    AIResearchers find that a short, direct prompt with a few examples lets small open LLMs match more complex prompting methods on natural language inference. On three NLI datasets, five small models, including Gemma 2 9B, Mistral 7B and Llama 3.1 8B, were tested with 0 to 15 examples and no reasoning steps. Mistral 7B beat the 63.4% MNLI accuracy that a published in-context sampling method achieved with the same model.

    Image from @rohanpaul_ai's post

Heat Trend

Current comparable heat
8
Peak comparable heat
10Nov 10, 9:00 PM ET
Change in past 24 hours
–

The trend compares only continuously observed participants, so its scope may be smaller than the current heat total. Hover or tap for hourly heat; use arrow keys with a keyboard.