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prompting

ReAct: Synergizing Reasoning and Acting in Language Models - Summary

The paper introduces ReAct, a novel prompt-based paradigm that synergizes reasoning and acting in language models for general task solving. ReAct generates both verbal reasoning traces and actions in an interleaved manner, allowing the model to perform dynamic reasoning to create, maintain, and adj

Segment Everything Everywhere All at Once - Summary

The paper presents SEEM, a promptable, interactive model for Segmenting Everything Everywhere all at once in an image. It introduces a versatile prompting engine for different types of prompts, including points, boxes, scribbles, masks, texts, and referred regions of another image. The model can ef

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