Learning to Ask: Autonomous Knowledge Acquisition Using a Frozen Language Model as Environment
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901686259286016 |
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| author | Aggarwal, Mohit |
| author_facet | Aggarwal, Mohit |
| contents | <p>This paper presents an architecture where a lightweight outer learning layer uses a frozen large language model (LLM) as an environment for autonomous knowledge acquisition. The outer layer observes the LLM's outputs, extracts concepts and features, builds a knowledge graph, and generates questions driven by the graph's own structural gaps. Crucially, the outer layer forms its own abstractions and then queries the LLM about these self-generated abstractions, creating a cycle where learned structure drives new inquiry.</p> <p>A four-way ablation study over 200 steps shows that (1) feedback-driven questioning produces 4.9x denser knowledge graphs than structure-driven questioning without feedback, (2) every question strategy improves by 34-97% when feedback is enabled, and (3) structure alone performs worse than random exploration. An extended 2000-step run demonstrates that information gain accelerates rather than plateaus as the knowledge graph densifies. All code and data are publicly available.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20113783 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Learning to Ask: Autonomous Knowledge Acquisition Using a Frozen Language Model as Environment Aggarwal, Mohit autonomous learning knowledge graphs language models information gain curiosity-driven exploration concept formation ablation study <p>This paper presents an architecture where a lightweight outer learning layer uses a frozen large language model (LLM) as an environment for autonomous knowledge acquisition. The outer layer observes the LLM's outputs, extracts concepts and features, builds a knowledge graph, and generates questions driven by the graph's own structural gaps. Crucially, the outer layer forms its own abstractions and then queries the LLM about these self-generated abstractions, creating a cycle where learned structure drives new inquiry.</p> <p>A four-way ablation study over 200 steps shows that (1) feedback-driven questioning produces 4.9x denser knowledge graphs than structure-driven questioning without feedback, (2) every question strategy improves by 34-97% when feedback is enabled, and (3) structure alone performs worse than random exploration. An extended 2000-step run demonstrates that information gain accelerates rather than plateaus as the knowledge graph densifies. All code and data are publicly available.</p> |
| title | Learning to Ask: Autonomous Knowledge Acquisition Using a Frozen Language Model as Environment |
| topic | autonomous learning knowledge graphs language models information gain curiosity-driven exploration concept formation ablation study |
| url | https://doi.org/10.5281/zenodo.20113783 |