Основные понятия
VAL, a neuro-symbolic hybrid system, acquires hierarchical task knowledge from natural language dialogs by leveraging GPT-based subroutines for specific linguistic subtasks within a broader algorithmic framework.
Аннотация
The paper presents VAL, an interactive task learning (ITL) system that integrates large language models (LLMs) like GPT in a principled way to enable the acquisition of hierarchical task knowledge from natural language dialogs.
Key highlights:
VAL uses GPT-based subroutines for specific linguistic subtasks like predicate and argument selection, while the overall task learning algorithm remains symbolic. This allows VAL to leverage the linguistic flexibility of LLMs while maintaining interpretability and incremental learning.
VAL acquires hierarchical task knowledge represented as Hierarchical Task Networks (HTNs) through a recursive clarification process, where unknown actions are defined in terms of known ones.
VAL includes user-centric features like confirmatory dialogs, knowledge display, real-time action performance, and an undo button to support natural and productive teaching interactions.
A user study in a video game environment shows that most users could successfully teach VAL using natural language, with the GPT subroutines achieving high success rates.
The study also identifies areas for improvement, such as reducing the need for confirmatory dialogs and improving the robustness of the system to handle edge cases.
Статистика
VAL achieved a 93% user approval rate for its segmentGPT subroutine.
VAL's mapGPT subroutine had an 82% user approval rate with gpt-3.5-turbo and a 97% user approval rate with gpt-4.
VAL's groundGPT subroutine had an 88% user approval rate.
VAL's genGPT subroutine had an 81% user approval rate.
VAL's verbalizeGPT and paraphraseGPT subroutines had a 79% true positive rate and a 99% true negative rate.
Цитаты
"VAL was able to correctly perform what I asked it to"
"I found the display of VAL's current knowledge easy to understand"
"VAL processed my explanations quickly"