Leveraging Word Guessing Games to Assess the Intelligence of Large
  Language Models
The automatic evaluation of LLM-based agent intelligence is critical in
developing advanced LLM-based agents. Although considerable effort has been
devoted to developing human-annotated evaluation datasets, such as AlpacaEval,
existing techniques are costly, time-consuming, and lack adaptability. In this
paper, inspired by the popular language game ``Who is Spy'', we propose to use
the word guessing game to assess the intelligence performance of LLMs. Given a
word, the LLM is asked to describe the word and determine its identity (spy or
not) based on its and other players' descriptions. Ideally, an advanced agent
should possess the ability to accurately describe a given word using an
aggressive description while concurrently maximizing confusion in the
conservative description, enhancing its participation in the game. To this end,
we first develop DEEP to evaluate LLMs' expression and disguising abilities.
DEEP requires LLM to describe a word in aggressive and conservative modes. We
then introduce SpyGame, an interactive multi-agent framework designed to assess
LLMs' intelligence through participation in a competitive language-based board
game. Incorporating multi-agent interaction, SpyGame requires the target LLM to
possess linguistic skills and strategic thinking, providing a more
comprehensive evaluation of LLMs' human-like cognitive abilities and
adaptability in complex communication situations. The proposed evaluation
framework is very easy to implement. We collected words from multiple sources,
domains, and languages and used the proposed evaluation framework to conduct
experiments. Extensive experiments demonstrate that the proposed DEEP and
SpyGame effectively evaluate the capabilities of various LLMs, capturing their
ability to adapt to novel situations and engage in strategic communication.
6541c04b23e8ef68e44f7aae
Tian Liang
6541c04b23e8ef68e44f7aaf
Zhiwei He
6541c04b23e8ef68e44f7ab0
Jen-tes Huang
6541c04b23e8ef68e44f7ab1
Wenxuan Wang
6541c04b23e8ef68e44f7ab2
Wenxiang Jiao
6541c04b23e8ef68e44f7ab3
Rui Wang
6541c04b23e8ef68e44f7ab4
Yujiu Yang
6541c04b23e8ef68e44f7ab5
Zhaopeng Tu
claimed_verified
2024-11-28T11:44:40.759Z
67485743561b1e6f9579389f
/avatars/8a4cc63bd7be388010bc329bb74582a1.svg
Zhaopeng Tu
user
zptu
6541c04b23e8ef68e44f7ab6
Shuming Shi
6541c04b23e8ef68e44f7ab7
Xing Wang
Leveraging Word Guessing Games to Assess the Intelligence of Large
  Language Models
The automatic evaluation of LLM-based agent intelligence is critical in
developing advanced LLM-based agents. Although considerable effort has been
devoted to developing human-annotated evaluation datasets, such as AlpacaEval,
existing techniques are costly, time-consuming, and lack adaptability. In this
paper, inspired by the popular language game ``Who is Spy'', we propose to use
the word guessing game to assess the intelligence performance of LLMs. Given a
word, the LLM is asked to describe the word and determine its identity (spy or
not) based on its and other players' descriptions. Ideally, an advanced agent
should possess the ability to accurately describe a given word using an
aggressive description while concurrently maximizing confusion in the
conservative description, enhancing its participation in the game. To this end,
we first develop DEEP to evaluate LLMs' expression and disguising abilities.
DEEP requires LLM to describe a word in aggressive and conservative modes. We
then introduce SpyGame, an interactive multi-agent framework designed to assess
LLMs' intelligence through participation in a competitive language-based board
game. Incorporating multi-agent interaction, SpyGame requires the target LLM to
possess linguistic skills and strategic thinking, providing a more
comprehensive evaluation of LLMs' human-like cognitive abilities and
adaptability in complex communication situations. The proposed evaluation
framework is very easy to implement. We collected words from multiple sources,
domains, and languages and used the proposed evaluation framework to conduct
experiments. Extensive experiments demonstrate that the proposed DEEP and
SpyGame effectively evaluate the capabilities of various LLMs, capturing their
ability to adapt to novel situations and engage in strategic communication.
A new evaluation framework using interactive multi-agent games assesses LLMs' intelligence, expression, and strategic communication skills.
LLM-based agents AlpacaEval word guessing game DEEP SpyGame multi-agent framework linguistic skills strategic thinking human-like cognitive abilities complex communication situations