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

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James R. Kirk, Robert E. Wray, Peter Lindes, John E. Laird

Machine Learning · Submitted 13 Sep 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our results demonstrate that actionable task knowledge can be obtained from LLMs in support of online agent task learning.

how it is stated · asserted 1.00 read the record →

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arXiv ID2209.07636 (version 2)
Pith IDB7WMB5WG, Pith's permanent record of this paper and its review
CitationJames R. Kirk, John E. Laird, Peter Lindes, Robert E. Wray. "Improving Language Model Prompting in Support of Semi-autonomous Task Learning." arXiv:2209.07636 (2022). https://pith.science/paper/B7WMB5WG
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,292 input tokens.

What the abstract itself states, as probabilities: claims first 0.11 · states limitation 0.17 · human participants 0.09 · controlled comparison 0.11 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.22 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Language models (LLMs) offer potential as a source of knowledge for agents that need to acquire new task competencies within a performance environment. background 1.00 0.06 0.03 hedged possibility 0.97
s1 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. contribution 1.00 0.90 0.04 asserted 0.88
s2 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. contribution 0.36 0.12 0.03 asserted 0.99
s3 We summarize a series of empirical investigations of prompting strategies and evaluate responses against the goals of targeted and actionable responses for task learning. method 0.61 0.91 0.05 asserted 0.62
s4 Our results demonstrate that actionable task knowledge can be obtained from LLMs in support of online agent task learning. result 0.96 0.95 0.92 asserted 1.00

What kind of paper the classifier reports this is: empirical study 0.73 · evidence from observation or experiment 0.99.

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  • P abstract
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  • S sections
  • T body sentences
  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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