地址: https://www.huaxiaozhuan.com/

新增 36 篇关于 Prompt Engineering 的热门论文:

《Chain of Thought Prompting Elicits Reasoning in Large Language Models》
《Least-to-Most Prompting Enables Complex Reasoning in Large Language Models》
《Automatic Chain of Thought Prompting in Large Language Models》
《Self-Consistency Improves Chain of Thought Reasoning in Language Models》
《Large Language Models are Zero-Shot Reasoners》
《Calibrate Before Use: Improving Few-Shot Performance of Language Models》
《What Makes Good In-Context Examples for GPT-3?》
《Making Pre-trained Language Models Better Few-shot Learners》
《It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners》
《Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference》
《GPT Understands, Too》
《P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks》
《Prefix-Tuning: Optimizing Continuous Prompts for Generation》
《The Power of Scale for Parameter-Efficient Prompt Tuning》
《How Can We Know What Language Models Know?》
《Eliciting Knowledge from Language Models Using Automatically Generated Prompts》
《Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity》
《Can language models learn from explanations in context?》
《Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?》
《Multitask Prompted Training Enables Zero-Shot Task Generalization》
《Language Models as Knowledge Bases?》
《Do Prompt-Based Models Really Understand the Meaning of Their Prompts?》
《Finetuned Language Models Are Zero-Shot Learners》
《Factual Probing Is [MASK]: Learning vs. Learning to Recall》
《How many data points is a prompt worth?》
《Learning How to Ask: Querying LMs with Mixtures of Soft Prompts》
《Learning To Retrieve Prompts for In-Context Learning》
《PPT: Pre-trained Prompt Tuning for Few-shot Learning》
《Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm》
《Show Your Work: Scratchpads for Intermediate Computation with Language Models》
《True Few-Shot Learning with Language Models》
《Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning》
《Improving and Simplifying Pattern Exploiting Training》
《MetaICL: Learning to Learn In Context》
《SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer》
《Noisy Channel Language Model Prompting for Few-Shot Text Classification》
--
FROM 114.92.158.*