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The Quill

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes - Summary

The paper introduces a new mechanism called Distilling step-by-step that trains smaller models to outperform larger language models (LLMs) while using less training data and smaller model sizes. The mechanism extracts LLM rationales as additional supervision for small models within a multi-task tra

AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head - Summary

The paper proposes a multi-modal AI system named AudioGPT that complements Large Language Models (LLMs) with foundation models to process complex audio information and solve numerous understanding and generation tasks. AudioGPT is connected with an input/output interface (ASR, TTS) to support spoke

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning - Summary

The paper discusses the limitations of large language models (LMs) and proposes a neuro-symbolic architecture called the Modular Reasoning, Knowledge and Language (MRKL) system that combines LMs with external knowledge sources and discrete reasoning modules to overcome these limitations.

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

Just Tell Me: Prompt Engineering in Business Process Management - Summary

The paper discusses the use of prompt engineering to leverage pre-trained language models for business process management (BPM) tasks. It identifies the potentials and challenges of prompt engineering for BPM research.

Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models - Summary

The paper introduces Chameleon, a plug-and-play compositional reasoning framework that augments large language models (LLMs) to address their inherent limitations and tackle a broad range of reasoning tasks. Chameleon synthesizes programs to compose various tools, including LLM models, off-the-shel

MiLMo:Minority Multilingual Pre-trained Language Model - Summary

The paper presents a multilingual pre-trained language model named MiLMo that performs better on minority language tasks, including Mongolian, Tibetan, Uyghur, Kazakh and Korean. The authors also construct a minority multilingual text classification dataset named MiTC, and train a word2vec model fo