A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Shubham Vatsal, Harsh Dubey
A survey of 44 papers on 39 prompt engineering methods across 29 NLP tasks, showing how structured prompts improve LLM performance without retraining.
L. Ein-Dor, Orith Toledo-Ronen, Artem Spector, et al.
Proposes Conversational Prompt Engineering (CPE), a tool that uses chat interaction to help users create personalized, high-performing prompts for LLMs.
Krzysztof Czarnecki, Ulrich W. Eisenecker
This book introduces generative programming, a paradigm for automating software component assembly using domain engineering, feature modeling, and code generation.
Ronan Collobert, Jason Weston, Léon Bottou, et al.
A unified neural network architecture that learns internal representations from unlabelled data achieves state-of-the-art on multiple NLP tasks without task-specific feature engineering.
Seyedali Mirjalili, Seyed Mohammad Mirjalili, Seyed Mohammad Mirjalili, et al.
Proposes Grey Wolf Optimizer (GWO), a meta-heuristic inspired by grey wolf leadership hierarchy and hunting behavior, achieving competitive results on benchmark functions and engineering design problems.
Freitas, Tiago Carvalho, Costa Neto, Alvaro, Pereira, Maria João Varanda, et al.
This paper outlines the potential role of LLMs in knowledge engineering, proposing hybrid neuro-symbolic systems and natural language knowledge engineering.
Yan Hu, Qingyu Chen, Jingcheng Du, et al.
This paper shows that task-specific prompt engineering, incorporating medical knowledge and few-shot examples, significantly improves GPT-3.5 and GPT-4 performance on clinical NER tasks, though still below BioClinicalBERT.
Sai Vemprala, Rogerio Bonatti, Arthur Bucker, et al.
This paper presents a strategy combining prompt engineering and a high-level function library to enable ChatGPT for diverse robotics tasks, and introduces the PromptCraft research tool.
Lingrui Mei, Jiayu Yao, Yuyao Ge, et al.
This survey introduces Context Engineering as a formal discipline for systematically optimizing information payloads for LLMs, revealing a critical asymmetry between understanding and generating complex contexts.
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Reinforcement learning boosts LLM performance on competitive programming and software engineering, comparing OpenAI models o1, o1-ioi, and o3.
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DSPy introduces a programming model that abstracts LM pipelines as text transformation graphs, using declarative modules and automatic optimization to replace manual prompt engineering.
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Proposes a simple continuous prompt learning approach to automate prompt engineering for pre-trained vision-language models.