Difference between revisions of "AI tricks"
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* 2025-03: [https://arxiv.org/abs/2503.04818 Prompting Science Report 1: Prompt Engineering is Complicated and Contingent] | * 2025-03: [https://arxiv.org/abs/2503.04818 Prompting Science Report 1: Prompt Engineering is Complicated and Contingent] | ||
* 2024-06: [https://arxiv.org/abs/2406.06608 The Prompt Report: A Systematic Survey of Prompting Techniques] | * 2024-06: [https://arxiv.org/abs/2406.06608 The Prompt Report: A Systematic Survey of Prompting Techniques] | ||
| + | * 2025-06: [https://arxiv.org/abs/2506.05614 Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks] | ||
==In-Context Learning== | ==In-Context Learning== | ||
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* 2024-11: [https://arxiv.org/abs/2411.10541 Does Prompt Formatting Have Any Impact on LLM Performance?] | * 2024-11: [https://arxiv.org/abs/2411.10541 Does Prompt Formatting Have Any Impact on LLM Performance?] | ||
| − | ==Position Bias== | + | ==Brittleness== |
| + | * 2025-03: [https://arxiv.org/abs/2503.01781 Cats Confuse Reasoning LLM: Query Agnostic Adversarial Triggers for Reasoning Models] | ||
| + | |||
| + | ===Position Bias=== | ||
* 2023-07: [https://arxiv.org/abs/2307.03172 Lost in the Middle: How Language Models Use Long Contexts] | * 2023-07: [https://arxiv.org/abs/2307.03172 Lost in the Middle: How Language Models Use Long Contexts] | ||
* 2024-11: [https://arxiv.org/abs/2411.01101 Self-Consistency Falls Short! The Adverse Effects of Positional Bias on Long-Context Problems] | * 2024-11: [https://arxiv.org/abs/2411.01101 Self-Consistency Falls Short! The Adverse Effects of Positional Bias on Long-Context Problems] | ||
Latest revision as of 09:09, 7 July 2025
Contents
Prompt Engineering
- 2025-03: Prompting Science Report 1: Prompt Engineering is Complicated and Contingent
- 2024-06: The Prompt Report: A Systematic Survey of Prompting Techniques
- 2025-06: Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks
In-Context Learning
- 2020-05: Language Models are Few-Shot Learners
- 2025-03: Learning to Search Effective Example Sequences for In-Context Learning
Chain of Thought (CoT)
- 2022-05: Large Language Models are Zero-Shot Reasoners "Let's think step-by-step"
- 2024-06: TextGrad: Automatic "Differentiation" via Text
- 2025-06: Prompting Science Report 2: The Decreasing Value of Chain of Thought in Prompting
Multi-step
Tool-use, feedback, agentic
Retrieval-Augmented Generation (RAG)
Input/Output Formats
- 2024-08: LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs
- 2024-11: Does Prompt Formatting Have Any Impact on LLM Performance?
Brittleness
Position Bias
- 2023-07: Lost in the Middle: How Language Models Use Long Contexts
- 2024-11: Self-Consistency Falls Short! The Adverse Effects of Positional Bias on Long-Context Problems
- 2025-02: On the Emergence of Position Bias in Transformers
- Testing models:
- Needle-in-a-Haystack tests
- 2023-08: LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
- 2024-02: ∞Bench: Extending Long Context Evaluation Beyond 100K Tokens
- 2024-06: [Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models https://arxiv.org/abs/2406.11230]
- 2024-07: Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack
- 2025-04: Reasoning on Multiple Needles In A Haystack
- Mitigation:
