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Found 3 papers, 1 papers with code
When Large Language Models contradict humans? Large Language Models' Sycophantic Behaviour
Large Language Models have been demonstrating the ability to solve complex tasks by delivering answers that are positively evaluated by humans due in part to the intensive use of human feedback that refines responses.
Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts
Reasoning methods, best exemplified by the well-known Chain-of-Thought (CoT), empower the reasoning abilities of Large Language Models (LLMs) by eliciting them to solve complex tasks in a step-by-step manner.
Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations
This disparity is demanded in further fine-tuning and affecting the cross-lingual abilities of LLMs.
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