prompting

提示工程、思维链、自洽、上下文学习

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2026

Interaction Scaling: Grounding the Third Axis of Test-Time Compute — paper=interaction-scaling-grounding-the-third-axis@arXiv:2607.11598v1

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning — paper=deepseek-r1@arXiv:2501.12948v2
MCP-Zero: Active Tool Discovery for Autonomous LLM Agents — paper=mcp-zero@arXiv:2506.01056v4
Understanding R1-Zero-Like Training: A Critical Perspective — paper=understanding-r1-zero-like-training-a@arXiv:2503.20783v2

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models — paper=deepseekmath-grpo@arXiv:2402.03300v3

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines — paper=dspy@arXiv:2310.03714v1
EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers — paper=evoprompt@arXiv:2309.08532v3
Large Language Models as Optimizers — paper=large-language-models-as-optimizers@arXiv:2309.03409v3
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework — paper=metagpt@arXiv:2308.00352v7
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection — paper=not-what-youve-signed-up-for@arXiv:2302.12173v2
Segment Anything — paper=segment-anything@arXiv:2304.02643v1
Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models — paper=take-a-step-back-evoking-reasoning@arXiv:2310.06117v2
Tree of Thoughts: Deliberate Problem Solving with Large Language Models — paper=tree-of-thoughts@arXiv:2305.10601v2
When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models — paper=when-a-helpful-assistant-is-not@arXiv:2311.10054v3

Automatic Chain of Thought Prompting in Large Language Models — paper=automatic-chain-of-thought-prompting-in@arXiv:2210.03493v1
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models — paper=chain-of-thought@arXiv:2201.11903v6
Conditional Prompt Learning for Vision-Language Models — paper=conditional-prompt-learning-for-vision-language@arXiv:2203.05557v2
Large Language Models Are Human-Level Prompt Engineers — paper=large-language-models-are-human-level@arXiv:2211.01910v2
Large Language Models are Zero-Shot Reasoners — paper=zero-shot-cot@arXiv:2205.11916v4
ReAct: Synergizing Reasoning and Acting in Language Models — paper=react@arXiv:2210.03629v3
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? — paper=rethinking-the-role-of-demonstrations-what@arXiv:2202.12837v2
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning — paper=rlprompt@arXiv:2205.12548v3
Self-Consistency Improves Chain of Thought Reasoning in Language Models — paper=self-consistency@arXiv:2203.11171v4
TabLLM: Few-shot Classification of Tabular Data with Large Language Models — paper=tabllm@arXiv:2210.10723v2
Training language models to follow instructions with human feedback — paper=instructgpt@arXiv:2203.02155v1

Generated Knowledge Prompting for Commonsense Reasoning — paper=generated-knowledge-prompting-for-commonsense-reasoning@arXiv:2110.08387v3
Learning to Prompt for Vision-Language Models — paper=learning-to-prompt-for-vision-language@arXiv:2109.01134v6
Prefix-Tuning: Optimizing Continuous Prompts for Generation — paper=prefix-tuning@arXiv:2101.00190v1
Show Your Work: Scratchpads for Intermediate Computation with Language Models — paper=show-your-work-scratchpads-for-intermediate@arXiv:2112.00114v1