Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Language Development01:22

Language Development

433
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
433
Language and Cognition01:27

Language and Cognition

413
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
413
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.7K
2.7K
Components of Language01:24

Components of Language

367
Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
367
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

142
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
142
Purposive Learning01:22

Purposive Learning

185
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
185

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Target ALS Global Natural History Study: Cross-platform proteomics to accelerate biofluid biomarker and drug target discovery in amyotrophic lateral sclerosis.

medRxiv : the preprint server for health sciences·2026
Same author

BRIDGE pilot study: a bilateral regulatory investigation of data governance and exchange.

NPJ digital medicine·2026
Same author

Clocks and Dominoes: Timing Mechanisms of Embryogenesis.

bioRxiv : the preprint server for biology·2026
Same author

Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

SmartEM: machine learning-guided electron microscopy.

Nature methods·2025
Same author

SynAnno: Interactive Guided Proofreading of Synaptic Annotations.

IEEE transactions on visualization and computer graphics·2025

Related Experiment Video

Updated: Aug 26, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

673

Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models.

Hendrik Strobelt, Albert Webson, Victor Sanh

    IEEE Transactions on Visualization and Computer Graphics
    |October 3, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Prompt engineering enables zero-shot learning for language models without retraining. PromptIDE facilitates prompt optimization and deployment for efficient ad-hoc task solving.

    More Related Videos

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    527
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    426

    Related Experiment Videos

    Last Updated: Aug 26, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    673
    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    527
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    426

    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • State-of-the-art neural language models (NLMs) excel at ad-hoc language tasks via zero-shot prompting.
    • This method bypasses the need for supervised training, gaining significant traction in recent years.
    • However, discovering effective prompts for new tasks remains a challenge due to sensitivity to wording and template variations.

    Purpose of the Study:

    • To introduce PromptIDE, a tool designed for systematic experimentation and optimization of prompts for NLMs.
    • To present a novel workflow for iterative prompt refinement, balancing model feedback with empirical validation.
    • To enable efficient deployment of custom ad-hoc models developed through prompt engineering.

    Main Methods:

    • PromptIDE enables users to explore prompt variations, visualize performance, and iteratively refine prompts.
    • A two-stage workflow is proposed: initial focus on model feedback with small data, followed by empirical grounding on large data.
    • Quantitative measures are used to validate and select optimal prompts.

    Main Results:

    • PromptIDE facilitates the discovery of high-accuracy prompts for specific natural language processing (NLP) tasks.
    • The proposed workflow effectively guides users from initial prompt ideation to empirically validated solutions.
    • Demonstrated utility across several real-world use cases, showcasing the practical applicability of PromptIDE.

    Conclusions:

    • PromptIDE and the associated workflow significantly streamline the process of prompt engineering for ad-hoc NLP tasks.
    • This approach democratizes the use of powerful NLMs by simplifying task adaptation without requiring extensive retraining.
    • The tool and methodology offer a practical solution for researchers and practitioners seeking to leverage zero-shot capabilities efficiently.