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Paper Summaries

Self-Consistency Improves Chain of Thought Reasoning in Language Models - Summary

The paper proposes a new decoding strategy called self-consistency to improve the performance of chain-of-thought prompting in language models for complex reasoning tasks. Self-consistency first samples a diverse set of reasoning paths and then selects the most consistent answer by marginalizing ou

The Power of Scale for Parameter-Efficient Prompt Tuning - Summary

The paper explores prompt tuning, a mechanism for learning soft prompts to condition frozen language models for specific downstream tasks. The approach outperforms GPT-3's few-shot learning and becomes more competitive with scale. Prompt tuning confers benefits in robustness to domain transfer and

GPT Understands, Too - Summary

The paper proposes a novel method called P-tuning, which employs trainable continuous prompt embeddings to improve the performance of GPTs on natural language understanding (NLU) tasks. The method is shown to be better than or comparable to similar-sized BERTs on NLU tasks and substantially improve

Large Language Models Are Human-Level Prompt Engineers - Summary

The paper proposes Automatic Prompt Engineer (APE), an algorithm that generates and selects natural language instructions for large language models (LLMs) to improve task performance. APE treats the instruction as a program and optimizes it by searching over a pool of instruction candidates propose

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts - Summary

The paper introduces AUTOPROMPT, an automated method to create prompts for a diverse set of tasks based on a gradient-guided search. The prompts elicit more accurate factual knowledge from masked language models (MLMs) than manually created prompts on the LAMA benchmark. MLMs can perform sentiment

Expressive Text-to-Image Generation with Rich Text - Summary

The paper proposes a method for text-to-image generation using rich text prompts that support various text attributes such as font family, size, color, and footnote. The method enables precise control of text-to-image synthesis regarding colors, styles, and object details compared to plain text. Th

Training language models to follow instructions with human feedback - Summary

The paper presents a method for aligning language models with user intent by fine-tuning with human feedback. The resulting models, called InstructGPT, show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Th