# Improving Language Model Prompting in Support of Semi-autonomous Task Learning

arXiv: https://arxiv.org/abs/2209.07636 · HTML: https://mlsanity.com/paper/2209.07636/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [A Recipe For Arbitrary Text Style Transfer with Large Language Models](https://mlsanity.com/paper/2109.03910/) (2021)
- [GLUCOSE: GeneraLized and COntextualized Story Explanations](https://mlsanity.com/paper/2009.07758/) (2020)
- [GPT3-to-plan: Extracting plans from text using GPT-3](https://mlsanity.com/paper/2106.07131/) (2021)
- [Few-shot Subgoal Planning with Language Models](https://mlsanity.com/paper/2205.14288/) (2022)
- [Language Models as a Knowledge Source for Cognitive Agents](https://mlsanity.com/paper/2109.08270/) (2021)

## Built on by (most cited first, 6 of 6)

- [GPT as Knowledge Worker: A Zero-Shot Evaluation of (AI)CPA Capabilities](https://mlsanity.com/paper/2301.04408/) (2023)
- [Exploiting Language Models as a Source of Knowledge for Cognitive Agents](https://mlsanity.com/paper/2310.06846/) (2023)
- [Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis](https://mlsanity.com/paper/2306.06770/) (2023)
- [EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context](https://mlsanity.com/paper/2407.04472/) (2024)
- [Using Natural Language for Human-Robot Collaboration in the Real World](https://mlsanity.com/paper/2508.11759/) (2025)
- [LLM-Augmented Symbolic NLU System for More Reliable Continuous Causal Statement Interpretation](https://mlsanity.com/paper/2510.19988/) (2025)

## Abstract (arXiv)

Language models (LLMs) offer potential as a source of knowledge for agents that need to acquire new task competencies within a performance environment. We describe efforts toward a novel agent capability that can construct cues (or "prompts") that result in useful LLM responses for an agent learning a new task. Importantly, responses must not only be "reasonable" (a measure used commonly in research on knowledge extraction from LLMs) but also specific to the agent's task context and in a form that the agent can interpret given its native language capacities. We summarize a series of empirical investigations of prompting strategies and evaluate responses against the goals of targeted and actionable responses for task learning. Our results demonstrate that actionable task knowledge can be obtained from LLMs in support of online agent task learning.