GATE: Solve prompt issue for you
GATE: Solve prompt issue for you
MIT researchers developed a GATE framework. GATE will proactively engage in open dialogue with you and understand your needs and preferences through a series of conversations.
After understanding your requirements, GATE will generate appropriate prompts and pass them to LLMs. This way the model can more accurately generate answers that fit your needs.
It’s equivalent to writing prompts for you…
This also means that those who engage in prompt word training and develop prompt word products will soon be unemployed.
GATE advantages:
GATE understands user needs through dialogue, so users do not need to prepare a lot of information or perform complex thinking in advance.
This kind of interaction may also make users think about some issues they have not considered before, so as to gain a more comprehensive understanding of their needs.
The core idea of the GATE framework:
In short, GATE (Generative Active Task Elicitation) helps users generate more effective prompts by actively talking to users, thereby improving the accuracy and usability of LLMs.
Open-ended interaction: The model may ask open-ended questions, such as “What type of music are you looking for?” or “Do you have any special opinions on this topic?”
Edge case generation: The model may also generate some special or edge cases for users to mark or comment on to more accurately understand user preferences.
Working principle:
Core components of the GATE framework:
Open interaction: The model engages in free-form, language-based interaction with the user. This could be asking questions, generating examples, or any other form of language output.
User feedback:
Users provide feedback by responding to the model’s output, which is used to update the model’s understanding and predictions.
Model updates:
Based on collected user feedback, the parameters or structure of the model are updated accordingly.
Work process:
1. Initialization: The model starts with a basic understanding or preset task.
2. Interaction: The model generates one or more language-based outputs to guide the user to provide more information.
3. Feedback collection: Users respond to the output of the model, providing their opinions, needs, or preferences.
4. Model update: Use the collected feedback to update the model.
5. Iteration: This process is repeated until the model can accurately understand and perform the user’s tasks.
Below we explain how the GATE framework works through a specific usage scenario:
User needs:
The user wants to create an interesting game and requests the GATE system to design it.
GATE questions:
The GATE system asks users which platform or type of game they are considering when creating a game. For example, is it a mobile game, a PC game, or an arcade game.
User response:
Users said they were considering mobile games and particularly liked puzzle games.
Further questions from GATE:
The system asks the user if they have already considered the purpose and rules of the game, or if they need some ideas or suggestions.
Refined user needs:
Users expressed that they have not yet decided on specific game rules and hope to hear some new concepts or suggestions.
GATE’s suggestions:
The system suggests that you consider adding elements of time manipulation, such as allowing players to rewind time or pause time to solve puzzles.
User feedback:
Users found the idea interesting and requested more details about the game.
Final Prompt:
The GATE system generated a final prompt: “Design a puzzle game for mobile devices in which players can solve various obstacles and reach goals by manipulating time.”
This case shows how GATE can understand the specific needs of users through open dialogue with users, and generate effective prompts accordingly so that large-scale language models (LLMs) can more accurately meet user needs.
GitHub: https://t.co/MeaXl0qUIu
Paper: arxiv.org/abs/2310.11589
Experiments and results of the GATE framework in three different areas:
Content Recommendations:
Test a model’s ability to predict online articles that users want to read.
Ethical Judgment:
Test the model’s ability to predict the appropriateness of a given situation.
Email Validation:
Tests the model’s ability to assess the validity of a provided email address, which is done by comparing it to user opinions.
Experimental results:
The GATE framework performed well in tests in these three areas, especially in the two aspects of “content recommendation” and “email verification”, and its performance was significantly better than other learning models.
Experimental results show that GATE can more accurately understand people’s preferences and needs.
These experiments and results further prove the superiority of the GATE framework in understanding user needs and generating effective prompts.
