11 research outputs found
Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
Human intelligence thrives on the concept of cognitive synergy, where
collaboration and information integration among different cognitive processes
yield superior outcomes compared to individual cognitive processes in
isolation. Although Large Language Models (LLMs) have demonstrated promising
performance as general task-solving agents, they still struggle with tasks that
require intensive domain knowledge and complex reasoning. In this work, we
propose Solo Performance Prompting (SPP), which transforms a single LLM into a
cognitive synergist by engaging in multi-turn self-collaboration with multiple
personas. A cognitive synergist refers to an intelligent agent that
collaborates with multiple minds, combining their individual strengths and
knowledge, to enhance problem-solving and overall performance in complex tasks.
By dynamically identifying and simulating different personas based on task
inputs, SPP unleashes the potential of cognitive synergy in LLMs. We have
discovered that assigning multiple, fine-grained personas in LLMs elicits
better problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs, SPP
effectively elicits internal knowledge acquisition abilities, reduces
hallucination, and maintains strong reasoning capabilities. Code, data, and
prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git.Comment: work in progres
Extensible Prompts for Language Models on Zero-shot Language Style Customization
We propose eXtensible Prompt (X-Prompt) for prompting a large language model
(LLM) beyond natural language (NL). X-Prompt instructs an LLM with not only NL
but also an extensible vocabulary of imaginary words. Registering new imaginary
words allows us to instruct the LLM to comprehend concepts that are difficult
to describe with NL words, thereby making a prompt more descriptive. Also,
these imaginary words are designed to be out-of-distribution (OOD) robust so
that they can be (re)used like NL words in various prompts, distinguishing
X-Prompt from soft prompt that is for fitting in-distribution data. We propose
context-augmented learning (CAL) to learn imaginary words for general
usability, enabling them to work properly in OOD (unseen) prompts. We
experiment X-Prompt for zero-shot language style customization as a case study.
The promising results of X-Prompt demonstrate its potential to facilitate
advanced interaction beyond the natural language interface, bridging the
communication gap between humans and LLMs.Comment: Accepted by NeurIPS 202
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents
This paper introduces Alympics (Olympics for Agents), a systematic simulation
framework utilizing Large Language Model (LLM) agents for game theory research.
Alympics creates a versatile platform for studying complex game theory
problems, bridging the gap between theoretical game theory and empirical
investigations by providing a controlled environment for simulating human-like
strategic interactions with LLM agents. In our pilot case study, the "Water
Allocation Challenge," we explore Alympics through a challenging strategic game
focused on the multi-round auction on scarce survival resources. This study
demonstrates the framework's ability to qualitatively and quantitatively
analyze game determinants, strategies, and outcomes. Additionally, we conduct a
comprehensive human assessment and an in-depth evaluation of LLM agents in
strategic decision-making scenarios. Our findings not only expand the
understanding of LLM agents' proficiency in emulating human strategic behavior
but also highlight their potential in advancing game theory knowledge, thereby
enriching our understanding of both game theory and empowering further research
into strategic decision-making domains with LLM agents. Codes, prompts, and all
related resources are available at https://github.com/microsoft/Alympics
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
Artificial Intelligence (AI) has made incredible progress recently. On the
one hand, advanced foundation models like ChatGPT can offer powerful
conversation, in-context learning and code generation abilities on a broad
range of open-domain tasks. They can also generate high-level solution outlines
for domain-specific tasks based on the common sense knowledge they have
acquired. However, they still face difficulties with some specialized tasks
because they lack enough domain-specific data during pre-training or they often
have errors in their neural network computations on those tasks that need
accurate executions. On the other hand, there are also many existing models and
systems (symbolic-based or neural-based) that can do some domain-specific tasks
very well. However, due to the different implementation or working mechanisms,
they are not easily accessible or compatible with foundation models. Therefore,
there is a clear and pressing need for a mechanism that can leverage foundation
models to propose task solution outlines and then automatically match some of
the sub-tasks in the outlines to the off-the-shelf models and systems with
special functionalities to complete them. Inspired by this, we introduce
TaskMatrix.AI as a new AI ecosystem that connects foundation models with
millions of APIs for task completion. Unlike most previous work that aimed to
improve a single AI model, TaskMatrix.AI focuses more on using existing
foundation models (as a brain-like central system) and APIs of other AI models
and systems (as sub-task solvers) to achieve diversified tasks in both digital
and physical domains. As a position paper, we will present our vision of how to
build such an ecosystem, explain each key component, and use study cases to
illustrate both the feasibility of this vision and the main challenges we need
to address next
Steadfastly Maintain Our Direction and Explore New Roads: Sixty Years of Socialist Practice in China
China's transition to markets: market-preserving federalism, chinese style
This paper studies the relationship between decentralization and the success of reform in China. We argue that a particular form of decentralization—called market-preserving federalism Chinese style—provides the critical foundations for market success. China's form of decentralization has served the critical purpose of creating markets at a time when political resistance to economic reform remained strong and when the durability of the reforms was important. Nonetheless, federalism, Chinese style, lacks some national public goods, and the new system needs to be institutionalized. We also highlight some parallels between the United States under the Articles of Confederation (1781-1787) and those of modern China.decentralization, federalism, reform, China, institution,