# VAL: Interactive Task Learning with GPT Dialog Parsing

arXiv: https://arxiv.org/abs/2310.01627 · HTML: https://mlsanity.com/paper/2310.01627/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Scaling Laws for Reward Model Overoptimization](https://mlsanity.com/paper/2210.10760/) (2022)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://mlsanity.com/paper/2203.06566/) (2022)
- [ProAgent: Building Proactive Cooperative Agents with Large Language Models](https://mlsanity.com/paper/2308.11339/) (2023)
- [Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrations](https://mlsanity.com/paper/1909.00031/) (2019)
- [COACH: Cooperative Robot Teaching](https://mlsanity.com/paper/2302.06199/) (2023)
- [Unpacking Human Teachers' Intentions For Natural Interactive Task Learning](https://mlsanity.com/paper/2102.06755/) (2021)
- [Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks](https://mlsanity.com/paper/2208.09554/) (2022)

## Built on by (most cited first, 14 of 14)

- [Cocoa: Co-Planning and Co-Execution with AI Agents](https://mlsanity.com/paper/2412.10999/) (2024)
- [Large Language Models and Video Games: A Preliminary Scoping Review](https://mlsanity.com/paper/2403.02613/) (2024)
- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [GPT for Games: An Updated Scoping Review (2020-2024)](https://mlsanity.com/paper/2411.00308/) (2024)
- [SPHERE: An Evaluation Card for Human-AI Systems](https://mlsanity.com/paper/2504.07971/) (2025)
- [When Should Users Check? Modeling Confirmation Frequency in Multi-Step Agentic AI Tasks](https://mlsanity.com/paper/2510.05307/) (2025)
- [AI2T: Building Trustable AI Tutors by Interactively Teaching a Self-Aware Learning Agent](https://mlsanity.com/paper/2411.17924/) (2024)
- [AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments](https://mlsanity.com/paper/2506.11773/) (2025)
- [Interactive Program Synthesis for Modeling Collaborative Physical Activities from Narrated Demonstrations](https://mlsanity.com/paper/2509.24250/) (2025)
- [STAND: Self-Aware Precondition Induction for Interactive Task Learning](https://mlsanity.com/paper/2409.07653/) (2024)
- [Online Learning of HTN Methods for integrated LLM-HTN Planning](https://mlsanity.com/paper/2511.12901/) (2025)
- [Challenges in Grounding Language in the Real World](https://mlsanity.com/paper/2506.17375/) (2025)
- [Using Natural Language for Human-Robot Collaboration in the Real World](https://mlsanity.com/paper/2508.11759/) (2025)
- [OrchVis: Hierarchical Multi-Agent Orchestration for Human Oversight](https://mlsanity.com/paper/2510.24937/) (2025)

## Abstract (arXiv)

Machine learning often requires millions of examples to produce static, black-box models. In contrast, interactive task learning (ITL) emphasizes incremental knowledge acquisition from limited instruction provided by humans in modalities such as natural language. However, ITL systems often suffer from brittle, error-prone language parsing, which limits their usability. Large language models (LLMs) are resistant to brittleness but are not interpretable and cannot learn incrementally. We present VAL, an ITL system with a new philosophy for LLM/symbolic integration. By using LLMs only for specific tasks--such as predicate and argument selection--within an algorithmic framework, VAL reaps the benefits of LLMs to support interactive learning of hierarchical task knowledge from natural language. Acquired knowledge is human interpretable and generalizes to support execution of novel tasks without additional training. We studied users' interactions with VAL in a video game setting, finding that most users could successfully teach VAL using language they felt was natural.