# Automating Knowledge Acquisition for Content-Centric Cognitive Agents Using LLMs

arXiv: https://arxiv.org/abs/2312.16378 · HTML: https://mlsanity.com/paper/2312.16378/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 27 Dec 2023.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks](https://mlsanity.com/paper/2305.11430/) (2023)

## Built on by (most cited first, 4 of 4)

- [Neuro-Symbolic AI in 2024: A Systematic Review](https://mlsanity.com/paper/2501.05435/) (2025)
- [HARMONIC: A Framework for Explanatory Cognitive Robots](https://mlsanity.com/paper/2409.18037/) (2024)
- [HARMONIC: A Content-Centric Cognitive Robotic Architecture](https://mlsanity.com/paper/2509.13279/) (2025)
- [Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition](https://mlsanity.com/paper/2604.23413/) (2026)

## Abstract (arXiv)

The paper describes a system that uses large language model (LLM) technology to support the automatic learning of new entries in an intelligent agent's semantic lexicon. The process is bootstrapped by an existing non-toy lexicon and a natural language generator that converts formal, ontologically-grounded representations of meaning into natural language sentences. The learning method involves a sequence of LLM requests and includes an automatic quality control step. To date, this learning method has been applied to learning multiword expressions whose meanings are equivalent to those of transitive verbs in the agent's lexicon. The experiment demonstrates the benefits of a hybrid learning architecture that integrates knowledge-based methods and resources with both traditional data analytics and LLMs.