Understanding the Cognitive Challenges in Human-LLM Interactions

Core Concepts

Users face cognitive challenges in formulating clear intentions and prompts for LLMs, leading to the "Gulf of Envisioning."

Abstract

The content discusses the cognitive challenges users face when interacting with Large Language Models (LLMs) like ChatGPT. It explores the concept of envisioning intentions and how users struggle with planning, executing, and evaluating their interactions with LLMs. Three key gaps are identified: capability gap, instruction gap, and intentionality gap. The analysis is based on three interfaces: ChatGPT for writing tasks, Spellburst for creative coding, and Cursor for text editing.
ChatGPT:

Provides example prompts but lacks granularity in task breakdown.
Users discover how LLM interprets prompts through trial and error.
Custom instructions help align user values with model output.
Spellburst:

Offers example sketches and autocomplete suggestions.
Provides semantic operators for extending current ideas.
Allows comments in code output for evaluation.
Cursor:

Highlights text suggestions and provides code explanations.
Supports referencing external resources for better understanding.

Stats

None

Quotes

None

Key Insights Distilled From

Bridging the Gulf of Envisioning

by Hariharan Su... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2309.14459.pdf

Deeper Inquiries

How can users effectively bridge the intentionality gap when interacting with LLMs?

Users can effectively bridge the intentionality gap by developing a clear understanding of their goals and intentions before engaging with the LLM. This involves taking the time to plan out specific details, such as desired outcomes, key elements to include, tone or style preferences, and any constraints or requirements for the task. By having a well-defined mental model of what they aim to achieve, users can craft more precise prompts that align closely with their intentions. Additionally, utilizing features within the interface that allow for custom instructions or context setting can help guide the LLM towards generating outputs that better match user expectations.

What are the implications of relying on trial-and-error methods to understand LLM interpretations?

Relying solely on trial-and-error methods to understand LLM interpretations can have several implications. Firstly, it may lead to inefficiencies in interaction as users iterate through multiple prompts without a clear understanding of how slight changes impact output quality. This approach could result in frustration and wasted time for users who are unable to discern patterns or trends in how language nuances affect model responses. Moreover, without feedback mechanisms providing insights into why certain prompts yield specific outputs, users may struggle to learn from their interactions and make informed adjustments for future engagements.

How can interface designs better support users in formulating clear intentions for LLM interactions?

Interface designs can better support users in formulating clear intentions for LLM interactions by incorporating features that scaffold intention development throughout the interaction process. Providing structured templates or guidelines within the interface that prompt users to specify key details about their goals and desired outcomes can help clarify intentions before initiating dialogue with an LLM. Additionally, offering real-time feedback on prompt construction and highlighting potential areas where additional information is needed can assist users in crafting more effective prompts aligned with their objectives. By guiding users through a systematic process of intention formulation within the interface itself, designers can enhance user experience and facilitate more successful interactions with LLMs.