Papers text-based games
“text-based games” 태그가 달린 논문 75편 · 필터 해제
SituatedThinker: Grounding LLM Reasoning with Real-World through Situated Thinking
Recent advances in large language models (LLMs) demonstrate their impressive reasoning capabilities. However, the reasoning confined to internal parametric space limits LLMs' access to real-time information and understan…
Mathematical ReasoningMulti-hop Question AnsweringQuestion Answeringtext-based gamesMonte Carlo Planning with Large Language Model for Text-Based Game Agents
Text-based games provide valuable environments for language-based autonomous agents. However, planning-then-learning paradigms, such as those combining Monte Carlo Tree Search (MCTS) and reinforcement learning (RL), are …
Language ModelingLanguage ModellingLarge Language ModelReinforcement Learning (RL)+1TextArena
TextArena is an open-source collection of competitive text-based games for training and evaluation of agentic behavior in Large Language Models (LLMs). It spans 57+ unique environments (including single-player, two-playe…
text-based gamesPersona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games
Artificial agents are increasingly central to complex interactions and decision-making tasks, yet aligning their behaviors with desired human values remains an open challenge. In this work, we investigate how human-like …
Decision Makingtext-based gamesTextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model Reasoning
Reasoning is a fundamental capability of large language models (LLMs), enabling them to comprehend, analyze, and solve complex problems. In this paper, we introduce TextGames, an innovative benchmark specifically crafted…
Instruction FollowingLanguage ModelingLanguage ModellingLogical Reasoning+1Positive Experience Reflection for Agents in Interactive Text Environments
Intelligent agents designed for interactive environments face significant challenges in text-based games, a domain that demands complex reasoning and adaptability. While agents based on large language models (LLMs) using…
text-based gamesMalinowski in the Age of AI: Can large language models create a text game based on an anthropological classic?
Recent advancements in Large Language Models (LLMs) like ChatGPT and GPT-4 have shown remarkable abilities in a wide range of tasks such as summarizing texts and assisting in coding. Scientific research has demonstrated …
Misinformationtext-based gamesBetter than Your Teacher: LLM Agents that learn from Privileged AI Feedback
While large language models (LLMs) show impressive decision-making abilities, current methods lack a mechanism for automatic self-improvement from errors during task execution. We propose LEAP, an iterative fine-tuning f…
Decision Makingtext-based gamesYou Have Thirteen Hours in Which to Solve the Labyrinth: Enhancing AI Game Masters with Function Calling
Developing a consistent and reliable AI game master for text-based games is a challenging task due to the limitations of large language models (LLMs) and the complexity of the game master's role. This paper presents a no…
text-based gamesSTARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models
Interactive fiction games have emerged as an important application to improve the generalization capabilities of language-based reinforcement learning (RL) agents. Existing environments for interactive fiction games are …
Reinforcement Learning (RL)text-based gamesAutomatic Bug Detection in LLM-Powered Text-Based Games Using LLMs
Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs). However, LLMs may exhibit flaws such…
Game Designtext-based gamesEXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning. A key challenge for agents attempting t…
Natural Language Understandingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Large Language Models Are Neurosymbolic Reasoners
A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential application of Large Language Models (LLM…
Common Sense ReasoningMathtext-based gamesvalidLanguage Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games
Large Language Models (LLMs) have demonstrated superior performance in language understanding benchmarks. CALM, a popular approach, leverages linguistic priors of LLMs -- GPT-2 -- for action candidate recommendations to …
Language ModelingLanguage Modellingtext-based gamesConstructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Games
In natural language processing, interactive text-based games serve as a test bed for interactive AI systems. Prior work has proposed to play text-based games by acting based on discrete knowledge graphs constructed by th…
Graph Neural NetworkKnowledge Graphstext-based gamesScriptWorld: Text Based Environment For Learning Procedural Knowledge
Text-based games provide a framework for developing natural language understanding and commonsense knowledge about the world in reinforcement learning based agents. Existing text-based environments often rely on fictiona…
Language ModelingLanguage ModellingNatural Language Understandingtext-based gamesByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games
In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks. We operationalize this as a task of generatin…
Code GenerationCommon Sense ReasoningIn-Context Learningtext-based gamesKnowledge-enhanced Agents for Interactive Text Games
Communication via natural language is a key aspect of machine intelligence, and it requires computational models to learn and reason about world concepts, with varying levels of supervision. Significant progress has been…
Instruction FollowingKnowledge GraphsLanguage ModellingProcedural Text Understanding+3A Minimal Approach for Natural Language Action Space in Text-based Games
Text-based games (TGs) are language-based interactive environments for reinforcement learning. While language models (LMs) and knowledge graphs (KGs) are commonly used for handling large action space in TGs, it is unclea…
Knowledge Graphstext-based gamesLearn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games
Language models pre-trained on large self-supervised corpora, followed by task-specific fine-tuning has become the dominant paradigm in NLP. These pre-training datasets often have a one-to-many structure--e.g. in dialogu…
Knowledge Distillationtext-based gamesvalid