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Rohit Agarwal

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

LoRA: Low-Rank Adaptation of Large Language Models - Summary

The paper proposes Low-Rank Adaptation (LoRA) as an approach to reduce the number of trainable parameters for downstream tasks in natural language processing. LoRA injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable

chain-of-thought-prompting vs standard prompting

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models - Summary

The paper explores how generating a chain of thought can improve the ability of large language models to perform complex reasoning. The authors introduce a simple method called chain-of-thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiment

Language Models are Few-Shot Learners - Summary

The paper discusses the limitations of pre-trained language representations in NLP systems and the need for task-specific datasets and fine-tuning. The authors show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with pri

Attention Is All You Need - Summary

The paper proposes a new network architecture called Transformer that relies solely on attention mechanisms, dispensing with recurrence and convolutions entirely. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality while requirin