Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?
Source
- Person key:
ysymyth - Source kind:
paper - Canonical URL: https://arxiv.org/abs/2104.10809
- License:
NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
c2805a2ea2e2797907ad868100b3dccb1cf09ca5bdd914ef140bad726393e437 - First seen: 2026-05-16
- Last changed: 2026-05-16
- Identity guard: Do not confuse with yao-shunyu-alfred, the physics-to-AI researcher at alfredyao.github.io.
Classification
- Category: Reinforcement learning and reasoning
- Topic hub: shunyu-yao-public-corpora
- Project taxonomy: shunyu-yao-project-taxonomy
- Paper map: shunyu-yao-paper-map
Summary
Language models trained on billions of tokens have recently led to unprecedented results on many NLP tasks. This success raises the question of whether, in principle, a system can ever understand” raw text without access to some form of grounding. We formally investigate the abilities of ungrounded systems to acquire meaning. Our analysis focuses on the role of assertions”: textual contexts that provide indirect clues about the underlying semantics. We study whether assertions enable a system to emulate represent…
What This Teaches
How learning, planning, and feedback loops shape agent behavior and model capability.
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