Editing Openai/6910f96f-f278-8006-9996-5cf14ff7a27f (section)

=== Assistant: Title: iPrOp: Interactive Prompt Optimization for Large Language Models with a Human in the Loop ===
Title: iPrOp: Interactive Prompt Optimization for Large Language Models with a Human in the Loop
Authors: Jiahui Li, Roman Klinger
Venue & Year: arXiv preprint, 2025
Link/DOI: https://arxiv.org/abs/2412.12644v2

==== Executive Summary ====
* The paper tackles a very practical problem: how non-experts can reliably find good prompts for tools like ChatGPT, instead of guessing and tweaking by trial and error.
* The authors propose iPrOp, an interactive system where a person and an AI work together: the AI suggests variations of a prompt, shows example outputs and scores, and the human chooses or refines the best ones.
* The system focuses on text classification tasks (e.g., detecting emotions in text) and uses example texts, model predictions, short explanations, and a performance score to help the user judge which prompt works better.
* In a first test on three public emotion datasets, repeated optimization cycles led to gradually better performance (measured by a standard “accuracy-like” metric called F1), showing the approach can improve prompts.
* The work is still early-stage: no user study yet, limited to one model and one task type, and performance gains depend on how well prompt rephrasing works. But it offers a structured way to move from “prompt hacking” to a more guided, transparent process.

==== Background & Research Question ====

Large language models (LLMs) like ChatGPT respond based on the prompt—the instruction you give them. Small wording changes can dramatically change the output, which makes “prompt engineering” tricky and often frustrating.

Existing work falls into two extremes:
* Manual prompting: humans handcraft prompts by trial-and-error.
* Automatic prompt optimization: algorithms search for better prompts, often without human oversight.

Both have problems. Manual prompting is time-consuming and not accessible to non-technical experts. Fully automatic methods often ignore domain nuance and user preferences (e.g., what counts as “good enough” or “readable” in a medical, legal, or educational setting).

Research question:
How can we combine human judgment and data-driven optimization so that people—especially non-technical domain experts—get guided help to create better prompts for their own tasks?

==== Methods (High-Level) ====

The authors introduce iPrOp, an “interactive prompt optimizer” with a web interface. The basic workflow:
# User sets up the task - Starts a conversation, chooses an LLM, and uploads a small labeled dataset (e.g., texts tagged with emotions like joy or sadness). - Provides an initial simple prompt describing the task.
# System generates prompt variations - An LLM is asked to rephrase the initial prompt into alternative wordings (e.g., “Classification task with labels joy and sadness” → “Classify the emotion of the text into joy or sadness.”).
# System evaluates each prompt in two ways For each candidate prompt: - It selects some example texts and asks the model to predict labels and explain its choices in plain language. - It uses another subset of the data to calculate an F1 score, a standard metric indicating how well the prompt+model combo is classifying.
# Human-in-the-loop decision - The user is shown, side by side: - The candidate prompts - Example texts - Model predictions and short explanations - Performance scores (F1) - The user picks the better prompt or edits a prompt manually and starts another round.
# Iteration - This loop continues until the user is satisfied with the prompt.

For the initial evaluation, the authors simulate the human choice using only the F1 score (i.e., automatically choosing the prompt with better performance), to test whether the process can, in principle, improve prompts.

They use:
* Task type: Emotion classification on text
* Datasets: Three public emotion datasets (tweets and fairy tales)
* Model: llama3.1:8b-instruct-fp16

==== Key Results ====
* Over 15 rounds of prompt optimization, the F1 scores on both training and validation data show an overall upward trend for all three emotion datasets.
* This suggests that iteratively rephrasing prompts and selecting the better ones can gradually improve task performance.
* The authors observe that: - Some datasets react more strongly to prompt changes than others. - In some cases, even a simple first prompt can perform surprisingly well, leaving less room for improvement.

Importantly, in this paper they only evaluate classification performance; they do not yet systematically evaluate:
* How users perceive readability and clarity of prompts, or
* How useful the textual explanations are in practice.

Those aspects are left for future user studies.

==== Limitations & Caveats ====

The authors explicitly list several limitations:
* No real user study yet: The “human choice” is simulated by picking the prompt with the best F1 score. We don’t yet know how real users would interact with the system, what they prefer, or how much effort they are willing to spend.
* Computation time: Generating explanations for many examples is expensive and slow, and the current prototype doesn’t yet handle streaming results to users smoothly.
* Data splitting challenges: For user-provided datasets of different sizes, it’s non-trivial to choose good train/validation/test splits within the tool.
* Dependence on rephrasing quality: If the LLM rephrases prompts in a limited or repetitive way, later prompts may remain low-quality, and improvements can stall.
* Task and dataset sensitivity: Some datasets are not very sensitive to prompt wording, so even naive prompts can perform well.
* Ethical and privacy risks: User data stays local in their design, but underlying LLMs may still pose privacy risks; prompts found by the system are not guaranteed to be globally “best” or unbiased.

==== Why It Matters / Implications ====

In practice today, many people—from doctors and lawyers to teachers and analysts—are expected to “just know” how to write good prompts, often without technical training. At the same time, fully automatic prompt optimizers may ignore important human preferences like clarity, domain nuance, or acceptable trade-offs between accuracy and simplicity.

This paper’s contribution is to:
* Propose a concrete, structured workflow where human judgment and AI optimization are combined, rather than treated as separate worlds.
* Provide a prototype web tool that could help non-technical domain experts gradually arrive at effective prompts for their own classification tasks using their own data.
* Offer a research framework to study: - Which prompt properties (examples, explanations, readability, metrics) actually guide human choices, - How prompts evolve over multiple optimization steps, and - How much human involvement is really needed before automation can take over.

In short, iPrOp moves prompt engineering away from pure guesswork toward a more systematic, transparent, and collaborative process between humans and LLMs.