We explore the self-play training procedure of large language models (LLMs)
in a two-player adversarial language game called Adversarial Taboo. In this
game, an attacker and a defender communicate with respect to a target word only
visible to the attacker. The attacker aims to induce the defender to utter the
target word unconsciously, while the defender tries to infer the target word
from the attacker's utterances. To win the game, both players should have
sufficient knowledge about the target word and high-level reasoning ability to
infer and express in this information-reserved conversation. Hence, we are
curious about whether LLMs' reasoning ability can be further enhanced by
Self-Play in this Adversarial language Game (SPAG). With this goal, we let LLMs
act as the attacker and play with a copy of itself as the defender on an
extensive range of target words. Through reinforcement learning on the game
outcomes, we observe that the LLMs' performance uniformly improves on a broad
range of reasoning benchmarks. Furthermore, iteratively adopting this self-play
process can continuously promote LLM's reasoning ability. The code is at
this https URL

在一个叫做 Adversarial Taboo 的对抗性语言游戏中，我们探索了大型语言模型（LLMs）的自我训练过程。通过强化学习和自我博弈，我们观察到 LLMs 在广泛的推理基准上表现出统一的提升，并且通过迭代采用自我博弈过程可以不断提升 LLM 的推理能力。

自主对抗性语言游戏增强 LLM 推理能力

Self-playing Adversarial Language Game Enhances LLM Reasoning

We study the problem of adversarial language games, in which multiple agents
with conflicting goals compete with each other via natural language
interactions. While adversarial language games are ubiquitous in human
activities, little attention has been devoted to this field in natural language
processing. In this work, we propose a challenging adversarial language game
called Adversarial Taboo as an example, in which an attacker and a defender
compete around a target word. The attacker is tasked with inducing the defender
to utter the target word invisible to the defender, while the defender is
tasked with detecting the target word before being induced by the attacker. In
Adversarial Taboo, a successful attacker must hide its intention and subtly
induce the defender, while a competitive defender must be cautious with its
utterances and infer the intention of the attacker. Such language abilities can
facilitate many important downstream NLP tasks. To instantiate the game, we
create a game environment and a competition platform. Comprehensive experiments
and empirical studies on several baseline attack and defense strategies show
promising and interesting results. Based on the analysis on the game and
experiments, we discuss multiple promising directions for future research.

该论文研究对抗性语言游戏及其在自然语言处理中的应用，提出 Adversarial Taboo 作为一个典型的挑战性语言游戏，分别考虑攻击和防御策略，并在多个基准攻击和防御策略的实验中，得到了有趣的和有前途的结果。