A new research paper titled "Semantic Deception" explores the limitations of large language models (LLMs) in symbolic reasoning. Researchers introduced a framework where symbols and operators were redefined, and LLMs were tasked with performing simple calculations. The study found that LLMs often rely on learned semantic associations rather than abstract symbolic manipulation, leading to performance degradation even on basic tasks when semantic cues were misleading. This reliance on surface-level semantics, potentially amplified by chain-of-thought prompting, raises concerns about the trustworthiness of LLMs in critical decision-making contexts. AI
IMPACT Highlights potential failures in LLM reasoning, impacting trust in AI for critical decision-making.
RANK_REASON Research paper detailing limitations of LLMs in symbolic reasoning. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- large-language models
- Marceau Nahon
- Semantic Deception
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