LLMs compress concreteness into a consistent 1D direction in mid-to-late layers that separates literal from figurative noun uses and supports efficient classification plus steering.
Prompting large language model for machine translation: A case study
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Jailbreak prompts grouped into ten patterns and three categories successfully evade ChatGPT restrictions across 40 scenarios using 3,120 test questions.
Local LLMs via Ollama match or exceed some local NMT systems and a frontier LLM on a new multilingual corpus but lag behind top commercial NMTs like DeepL.
citing papers explorer
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Exploring Concreteness Through a Figurative Lens
LLMs compress concreteness into a consistent 1D direction in mid-to-late layers that separates literal from figurative noun uses and supports efficient classification plus steering.
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Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
Jailbreak prompts grouped into ten patterns and three categories successfully evade ChatGPT restrictions across 40 scenarios using 3,120 test questions.
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Translation Analytics for Freelancers II: Benchmarking Local LLMs for Confidential Translation Workflows
Local LLMs via Ollama match or exceed some local NMT systems and a frontier LLM on a new multilingual corpus but lag behind top commercial NMTs like DeepL.