Can Language Models Serve as Text-Based World Simulators?

Can Language Models Serve as Text-Based World Simulators?

The paper "Can Language Models Serve as Text-Based World Simulators?" by Ruoyao Wang et al. provides an empirical investigation into the feasibility of contemporary LLMs acting as simulators for virtual text environments. This work investigates whether LLMs, specifically GPT-4, can effectively simulate the state transitions within text-based games using a newly-developed benchmark named ByteSized32.

Methodology

The authors formulate the problem of world simulation in text-based environments as a specific task termed "LLM-as-a-Simulator" (LLM-Sim). In a text-based game, an agent receives observations and can take actions described in natural language to achieve particular goals. Each environment is represented as a goal-conditioned partially observable Markov decision process (POMDP) with states, actions, transitions, observations, and rewards. The LLM-Sim task defines three primary components to simulate these transitions:

1. Action-Driven Transition ($\mathcal{F}_{act}$): Predicts the immediate state change caused by an action.
2. Environment-Driven Transition ($\mathcal{F}_{env}$): Predicts state changes due to underlying environmental dynamics.
3. Game Progress ($\mathcal{F}_R$): Predicts rewards and game completion status.

Moreover, ByteSized32, the introduced benchmark, consists of 76,369 state transition tuples from 31 distinct text-based games. Each game includes a description and a set of rules for object properties, actions, and scoring, provided either by humans or generated by LLMs themselves.

Experimental Design

The experiments focus on evaluating GPT-4's performance in modeling action-driven transitions, environment-driven transitions, and complete state transitions. Two prediction regimes are considered:

• Full State Prediction: The model outputs the entire state.
• State Difference Prediction: The model outputs only the changes from the previous state.

Performance is quantified by the model's prediction accuracy compared to ground-truth labels in various settings, including with and without explicitly provided game rules.

Results and Observations

1. Action-Driven vs. Environment-Driven Transitions: GPT-4 is more adept at predicting action-driven transitions, with an accuracy of up to 77.1% on dynamic transitions, compared to 49.7% for environment-driven transitions.
2. Static vs. Dynamic Transitions: Static transitions (no change in state) are easier to predict than dynamic ones, highlighting the challenge of accurately simulating non-trivial state changes.
3. Impact of Rules: Providing game rules, whether human-written or LLM-generated, significantly enhances performance. However, there is no clear performance advantage of human rules over LLM-generated ones.
4. Game Progress Prediction: GPT-4 performs well in predicting game progress, achieving up to 92.1% accuracy with rules provided, but performance drops to 61.5% without rules.
5. Human vs. LLM Performance: Humans significantly outperform GPT-4 in modeling the complete transition function $\mathcal{F}$, with human accuracy at 80% versus GPT-4's 50% in a selected subset of challenging games.

Implications and Limitations

The key implication of this research is that while LLMs show promise in simulating text-based virtual environments, they are not yet reliable for this task without further innovations. The challenges are particularly pronounced in modeling environment-driven transitions and handling tasks requiring deep common-sense, arithmetic, or scientific reasoning.

The study identifies significant limitations:

• Generalization: The findings primarily pertain to common-sense and elementary scientific reasoning tasks. The utility in more specialized or high-impact domains (e.g., physical or medical simulations) remains untested.
• Model Scope: The experiments focus on GPT-3.5 and GPT-4, leaving open the possibility that other models might exhibit different performance characteristics on the LLM-Sim task.
• Representation: The JSON-based state representation chosen for compatibility reasons could be suboptimal, suggesting a need for exploring alternative representations.

Future work could address these limitations by broadening the range of domains tested, including more LLMs, and experimenting with various state representation formats. Moreover, enhancing LLMs' capability to internalize and apply complex rules dynamically could be pivotal in advancing their effectiveness as world simulators.

Conclusion

This paper provides a valuable benchmark and a thorough investigation into the capabilities and limitations of LLMs, particularly GPT-4, in simulating text-based virtual environments. The insights derived indicate substantial room for improvement, especially in modeling complex state transitions and leveraging common sense and domain-specific knowledge. Future advancements in these areas are essential for realizing the full potential of LLMs as versatile and reliable world simulators.