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We formally introduce a improvisational wordplay game called Connections to explore reasoning capabilities of AI agents. Playing Connections combines skills in knowledge retrieval, summarization and awareness of cognitive states of other agents. We show how the game serves as a good benchmark for social intelligence abilities of language model based agents that go beyond the agents&#39; own memory and deductive reasoning and also involve gauging the understanding capabilities of other agents. Finally, we show how through communication with other agents in a constrained environment, AI agents must demonstrate social awareness and intelligence in games involving collaboration.

ACL 2024

Improvisational Games as a Benchmark for Social Intelligence of AI Agents: The Case of Connections

embedding representations

iterative games

cooperation

wordplay

social intelligence

We formally introduce a improvisational wordplay game called Connections to explore reasoning capabilities of AI agents. Playing Connections combines skills in knowledge retrieval, summarization and awareness of cognitive states of other agents. We show how the game serves as a good benchmark for social intelligence abilities of language model based agents that go beyond the agents' own memory and deductive reasoning and also involve gauging the understanding capabilities of other agents. Finally, we show how through communication with other agents in a constrained environment, AI agents must demonstrate social awareness and intelligence in games involving collaboration.

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof states. The current model, while trained solely on successful proof paths, faces a discrepancy at the inference stage, as it must sample and try various tactics at each proof state until finding success, unlike its training which does not incorporate learning from failed attempts. Intuitively, a tactic that leads to a failed search path would indicate that similar tactics should receive less attention during the following trials. In this paper, we demonstrate the benefit of training models that additionally learn from failed search paths. Facing the lack of such trial-and-error data in existing open-source theorem-proving datasets, we curate a dataset on intuitionistic propositional logic theorems and formalize it in Lean, such that we can reliably check the correctness of proofs. We compare our model trained on relatively short trial-and-error information (TrialMaster) with models trained only on the correct paths and discover that the former solves more unseen theorems with lower trial searches.

Learn from Failure: Fine-tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving

The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is known that the effective design of task-specific prompts is critical for LLMs' ability to produce high-quality answers. In particular, an effective approach for complex question-and-answering tasks is example-based prompting with chain-of-thought (CoT) reasoning, which significantly improves the performance of LLMs. However, current CoT methods rely on a fixed set of human-annotated exemplars, which are not necessarily the most effective examples for different tasks. This paper proposes a new method, Active-Prompt, to adapt LLMs to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning). For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful to annotate from a pool of task-specific queries. By borrowing ideas from the related problem of uncertainty-based active learning, we introduce several metrics to characterize the uncertainty so as to select the most uncertain questions for annotation. Experimental results demonstrate the superiority of our proposed method, achieving superior performance on eight complex reasoning tasks. Further analyses of different uncertainty metrics, pool sizes, zero-shot learning, and accuracy-uncertainty relationships demonstrate the effectiveness of our method.

Active Prompting with Chain-of-Thought for Large Language Models

With the booming of Large Language Models (LLMs), prompt-learning has become a promising method mainly researched in various research areas. Recently, many attempts based on prompt-learning have been made to improve the performance of text classification. However, most of these methods are based on heuristic Chain-of-Thought (CoT), and tend to be more complex but less efficient. In this paper, we rethink the LLM-based text classification methodology, propose a simple and effective transfer learning strategy, namely LLMEmbed, to address this classical but challenging task. To illustrate, we first study how to properly extract and fuse the text embeddings via various lightweight LLMs at different network depths to improve their robustness and discrimination, then adapt such embeddings to train the classifier. We perform extensive experiments on publicly available datasets, and the results show that LLMEmbed achieves strong performance while enjoys low training overhead using lightweight LLM backbones compared to recent methods based on larger LLMs, *i.e.* GPT-3, and sophisticated prompt-based strategies. Our LLMEmbed achieves adequate accuracy on publicly available benchmarks without any fine-tuning while merely use 4\% model parameters, 1.8\% electricity consumption and 1.5\% runtime compared to its counterparts. Code is available at: https://github.com/ChunLiu-cs/LLMEmbed-ACL2024.

LLMEmbed: Rethinking Lightweight LLM's Genuine Function in Text Classification

We present a new method for large language models to solve compositional tasks. Although they have shown strong performance on traditional language understanding tasks, large language models struggle to solve compositional tasks, where the solution depends on solving smaller instances of the same problem. We propose a natural approach to solve compositional tasks recursively. Our method, Re-Tuning, tunes models to break down a problem into subproblems, solve those subproblems, and combine the results. We show that our method significantly improves model performance on three representative compositional tasks: integer addition, dynamic programming, and parity. Compared to state-of-the-art methods that keep intermediate steps towards solving the problems, Re-Tuning achieves significantly higher accuracy and is more GPU memory efficient.

Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning

We explore the strategic capabilities of generative agents in a competitive game that simulates the television show Survivor. Large Language Models have been shown to act as intelligent agents through the addition of external cognitive architectures, but it is still unknown how these agents perform in competitive, multi-agent environments. We suggest a framework, built on top of a frozen large language model, GPT-4, for designing generative agents in competitive, episodic environments and we evaluate their game performance. We provide new modules allowing agents to set and evaluate strategic goals, develop theories of mind about other agents, and embody descriptive personas that affect their behavior. Across many simulations, we show that agents with varied cognitive abilities exhibit altered behaviors and that goal-driven agents display an emergent capacity to rapidly utilize information perceived from their environment.

Outwit, Outplay, Out-Generate: A Framework for Designing Strategic Generative Agents in Competitive Environments

Interactive fiction (IF) games are a genre of games where the player interacts with the fictional world via text-based commands, solving puzzles primarily by exploring the world and using items they collect along the way. Although there has been much work on playing IF using AI, there is relatively less work on the creation of such games using AI. While large language models (LLMs) have made the generation of text far easier in the past several years, they still struggle to be able to generate such highly-structured and consistent story worlds that one might see in IF. We present a three-part system called BERALL, which generates unique text adventure games by 1) maintaining the current state story world, 2) using retrieval-augmented generation (RAG) to create relevant location descriptions, and 3) combining these components to create a coherent experience for the player. Our results show that our approach is effective at generating room and story descriptions from setting and knowledge graphs, demonstrating the benefits of LLMs in IF. We find challenges remain in maintaining current game state due in part to LLMs not understanding the impact of actions on knowledge graphs.

BERALL: Generating Retrieval-augmented State-based Interactive Fiction Games

Large Language Models (LLMs) have shown impressive capabilities in complex tasks and interactive environments, yet their creativity remains underexplored. This paper introduces a simulation framework utilizing the game Balderdash to evaluate both the creativity and logical reasoning of LLMs. In Balderdash, players generate fictitious definitions for obscure terms to deceive others while identifying correct definitions. Our framework enables multiple LLM agents to participate in this game, assessing their ability to produce plausible definitions and strategize based on game rules and history. We implemented a centralized game engine featuring various LLMs as participants and a judge LLM to evaluate semantic equivalence. Through a series of experiments, we analyzed the performance of different LLMs, examining metrics such as True Definition Ratio, Deception Ratio, and Correct Guess Ratio. The results provide insights into the creative and deceptive capabilities of LLMs, highlighting their strengths and areas for improvement. Specifically, the study reveals that infrequent vocabulary in LLMs' input leads to poor reasoning on game rules and historical context.

Evaluating Creativity and Deception in Large Language Models: A Simulation Framework for Multi-Agent Balderdash

Text-adventure games and text role-playing games are grand challenges for reinforcement learning game playing agents. Text role-playing games are open-ended environments where an agent must faithfully play a particular character. We consider the distinction between {\em characters} and {\em actors}, where an actor agent has the ability to play multiple characters. We present a framework we call a {\em thespian agent} that can learn to emulate multiple characters along with a soft prompt that can be used to direct it as to which character to play at any time. We further describe an attention mechanism that allows the agent to learn new characters that are based on previously learned characters in a few-shot fashion. We show that our agent outperforms the state of the art agent framework in multi-character learning and few-shot learning.

Thespian: Multi-Character Text Role-Playing Game Agents

Strategic social deduction games serve as valuable testbeds for evaluating the understanding and inference skills of language models, offering crucial insights into social science, artificial intelligence, and strategic gaming. This paper focuses on creating proxies of human behavior in simulated environments, with “Among Us” utilized as a tool for studying simulated human behavior. The study introduces a text-based game environment, named AmongAgents, that mirrors the dynamics of “Among Us”. Players act as crew members aboard a spaceship, tasked with identifying impostors who are sabotaging the ship and eliminating the crew. Within this environment, the behavior of simulated language agents is analyzed. The experiments involve diverse game sequences featuring different configurations of Crewmates and Impostor personality archetypes. Our work demonstrates that state-of-the-art large language models (LLMs) can effectively grasp the game rules and make decisions based on the current context. This work aims to promote further exploration of LLMs in goal-oriented games with incomplete information and complex action spaces, as these settings offer valuable opportunities to assess language model performance in socially driven scenarios.

AmongAgents: Evaluating Large Language Models in the Interactive Text-Based Social Deduction Game

Role-playing games (RPGs) provide players with a rich, interactive world to explore. Dialogue serves as the primary means of communication between developers and players, manifesting in various forms such as guides, NPC interactions, and storytelling. While most games rely on written scripts to define the main story and character personalities, player immersion can be significantly enhanced through casual interactions between characters. With the advent of large language models (LLMs), we introduce a dialogue filler framework that utilizes LLMs enhanced by knowledge graphs to generate dynamic and contextually appropriate character interactions. We test this framework within the environments of Final Fantasy VII Remake and Pokémon, providing qualitative evidence that demonstrates GPT-4’s capability to act as specific characters and generate dialogue. However, some flaws remain, such as GPT-4 being overly positive when portraying Cloud. This study aims to assist developers in crafting more nuanced filler dialogues, thereby enriching player immersion and enhancing the overall RPG experience.

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Improvisational Games as a Benchmark for Social Intelligence of AI Agents: The Case of Connections

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Learn from Failure: Fine-tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving

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