A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Annie Wong, Thomas H. W. Back, A. Plaat, et al.
Evaluates prompting strategies in dynamic environments, finding strategic prompting can close performance gaps but reveals persistent reasoning limitations in LLMs.
Jundong Xu, Hao Fei, Liangming Pan, et al.
Proposes SymbCoT, a fully LLM-based framework integrating symbolic expressions and logic rules with Chain-of-Thought prompting to enhance logical reasoning.
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Magpie extracts high-quality instruction data at scale by prompting an aligned LLM with left-side templates, generating 4M instructions and responses.
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, et al.
CodeGen introduces a 16.1B parameter LLM for program synthesis that improves code generation through multi-turn prompting.
Yasaman Zarinkia, Venkatesh Srinivasan, Alex Thomo
This paper identifies and addresses the reasoning bottleneck in Graph-RAG systems for multi-hop QA through structured prompting and context compression.
Haiyan Zhao, Hanjie Chen, Fan Yang, et al.
A structured survey of explainability techniques for Transformer-based LLMs, categorizing methods by fine-tuning and prompting paradigms.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.
This paper systematically surveys prompt-based learning in NLP, a paradigm where pre-trained language models are adapted to tasks via textual prompts for few-shot or zero-shot learning.
Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.
Showed that including step-by-step reasoning examples in prompts dramatically improves LLM performance on arithmetic, commonsense, and symbolic reasoning tasks — an emergent ability appearing only at scale.