Game Development as Human-LLM Interaction
Authors: Jiale Hong, Hongqiu Wu, hai zhao
Accepted at: acl25

Game Development as Human-LLM Interaction

Jiale Hong*1,2,3, Hongqiu Wu*1,2,3, Hai Zhao†1,2,3
1 School of Computer Science, Shanghai Jiao Tong University
2 Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University
3 Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3
{hongjiale, wuhongqiu}@sjtu.edu.cn, zhaohai@cs.sjtu.edu.cn

Abstract
Game development is a highly specialized task that relies on a complex game engine powered by complex programming languages, preventing many gaming enthusiasts from handling it. This paper introduces the Chat Game Engine (ChatGE) powered by LLM, which allows everyone to develop a custom game using natural language through Human-LLM interaction. To enable an LLM to function as a ChatGE, we instruct it to perform the following processes in each turn: (1) \( P_{script} \): configure the game script segment based on the user’s input; (2) \( P_{code} \): generate the corresponding code snippet based on the game script segment; (3) \( P_{utter} \): interact with the user, including guidance and feedback. We propose a data synthesis pipeline based on LLM to generate game script-code pairs and interactions from a few manually crafted seed data. We propose a three-stage training strategy following curriculum learning principles to transfer the dialogue-based LLM to ChatGE smoothly. We construct ChatGE for poker games as a case study and comprehensively evaluate it from two perspectives: interaction quality and code correctness.

1 Introduction
A game engine is a software framework primarily designed for the development of games and generally includes relevant libraries and support programs (Valencia-García et al., 2016). Game engines help streamline the game development process, enabling developers to focus more on designing gameplay and content. Popular game engines include Unity, Unreal Engine, CryENGINE, etc.

Game development is a highly specialized task that relies on game engines powered by complex programming languages. The learning curve can be steep for those who wish to develop games based on their own designs. To make game development accessible to everyone, we propose the Chat Game Engine (ChatGE), powered by LLMs (Brown et al., 2020; Achiham et al., 2023; Touvron et al., 2023). This engine is designed to support the development of custom games using natural language through Human-LLM interaction.

Compared to traditional game engines, our ChatGE eliminates the learning curve. While traditional game engines provide users with software interfaces powered by complex technologies and programming languages, our ChatGE offers a more flexible natural language interface powered by LLM. One can simply input natural language under the guidance of the engine through Human-LLM interaction. In ChatGE, a user’s natural language input is equivalent to calling software interfaces in a traditional game engine. The LLM generates implementation code based on the user’s input, mirroring the process of implementing software interfaces through complex technologies and programming

Finally, add a flops phase, where discard one card from the deck and flop x cards from the deck to the community.

User
ChatGE

<Script>
<script>Phase['flop'] = 'Discard one card from the deck. Flop x cards from the deck to the community.'
</script>
<code>
def flops(self, x):
    self.deck.pop()
    for i in range(x):
        self.community += [self.deck.pop()]
</code>
<utter>
OK. The phase 'flop' has been added. Any other phases?
</utter>

Figure 1: An illustration of the ChatGE process for a poker game. See Appendix E for a complete example.
languages in traditional game engines.

ChatGE is based on large language models (LLMs), which have shown exceptional capabilities in natural language processing across various aspects. In this work, we explore the joint capability of interaction and programming of the LLM to serve as a game engine, enabling development through natural language via Human-LLM interaction. As illustrated in Figure 1, we instruct the LLM to perform the following processes in each turn: (1) \( P_{script} \): configure the game script segment based on the user’s input; (2) \( P_{code} \): generate the corresponding code snippet based on the game script segment; (3) \( P_{utter} \): interact with the user, including guidance and feedback.

We propose a comprehensive training paradigm to fine-tune an LLM to excel as a ChatGE, rather than relying solely on prompting. There are two main challenges. First, it is an exhausting process to acquire a large number of game script-code pairs. We propose an efficient data synthesis pipeline to generate game script-code pairs automatically from a few manually crafted seed data. Moreover, our framework requires the LLM to perform \( P_{script} \), \( P_{code} \), and \( P_{utter} \) step by step, challenging its joint capability of interaction and programming. Additionally, a straightforward strategy to train on sufficient complete interaction data is inefficient. Therefore, we propose a three-stage training strategy following curriculum learning principles to transfer the dialogue-based LLM to ChatGE smoothly.

Eventually, we construct a ChatGE for Poker, a worldwide card game, e.g. Texas hold’em. We utilize the proposed data synthesis pipeline to generate the corresponding dataset and fine-tune a ChatGE using the presented strategy. Then we propose a fine-grained evaluation process, measuring the performance from two perspectives: interaction quality and code correctness.

In summary, this paper:

• introduces the ChatGE framework for game development as Human-LLM interaction;

• presents the data generation technique that fuels the learning of ChatGE;

• proposes a three-stage training strategy for effectively training ChatGE;

• constructs a ChatGE for poker games and evaluates its performance from two perspectives: interaction quality and code correctness.

2 Related works

AI for Games AI for games is an exciting area in AI research. A great amount of recent work studies learning for agents, e.g. as game players for Atari (Mnih et al., 2013), Minecraft (Fan et al., 2022; Wang et al., 2023a), StarCraft, (Vinyals et al., 2019), NetHack (Küttler et al., 2020; Lowe et al., 2020), Werewolf (Xu et al., 2023); non-play characters (NPCs) (Shanahan et al., 2023; Uludagli and Oguz, 2023); player assistants (Gallotta et al., 2024); game commentators (Eladhari, 2018; Ranella and Eger, 2023). Recently, there has been work focused on building a neural engine based on LLMs. IDGE (Wu et al., 2024a) autoregressively predicts in-game states based on player actions, functioning primarily as a runtime environment for executing games through natural language instructions. It lacks support for iterative game development and refinement. Our ChatGE addresses this limitation by providing a comprehensive development framework that enables multi-turn interactions throughout the entire game creation process, similar to a traditional game engine.

LLMs as Training Data Generators With the immense power demonstrated by large language models(LLMs), researchers have recently explored their potential as as training data generators (Yu et al., 2024a). Such applications include generating tabular data (Borisov et al., 2022), medical dialogue (Chintagunta et al., 2021), sentence pairs (Schick and Schütze, 2021), role-play dialogue (Shao et al., 2023a), instruction data (Peng et al., 2023; Shao et al., 2023b; Sun et al., 2024; Wang et al., 2022), etc.. In this paper, we propose a data synthesis pipeline that leverages LLMs as training data generators to produce game script-code pairs and user-LLM interactions from a few manually crafted seed data.

Curriculum learning Curriculum learning (Bengio et al., 2009), a progressive training strategy, gradually increases the complexity of data samples during the training process. Recent studies show the promising role of curriculum learning in empowering the language models to tackle more challenging tasks (Vakil and Amiri, 2023; Wu et al., 2023, 2024a). In this paper, we propose a three-stage training strategy following curriculum learning principles to transfer the dialogue-based LLM to our ChatGE smoothly.
Figure 2: ChatGE framework. The LLM processes the user’s input in the orange stream, while simultaneously generating script in the yellow stream, code in the green stream, and interaction in the blue stream.

3 ChatGE

In this section, we present our ChatGE framework, illustrated in Figure 2.

3.1 Overview

The ChatGE framework introduces a new paradigm of game development as Human-LLM interaction. Users describe their game concepts in natural language, with the LLM guiding them in refining details and offering feedback. A predefined generic script template for specific game types ensures effective LLM guidance. The LLM generates script segments and code snippets in each interaction, progressively building the complete game code, CustomGame. Once developed through multi-turn interactions, a code interpreter executes CustomGame for play.

3.2 Formulation

The complete process of ChatGE framework can be seen as a multi-turn human-LLM interaction. We first formulate the multi-turn Human-LLM interaction and then extend this concept to ChatGE.

In a multi-turn Human-LLM interaction, both the user input and the LLM’s output may be related to the interaction history, such as references to prior content. The interaction history \( h_t \) at turn \( t \) can be simply defined as:

\[
h_t = \begin{cases}
\emptyset & \text{if } t = 0 \\
\{(i_{\tau}, o_{\tau}) \mid \tau = 1, 2, \ldots, t\} & \text{if } t > 0
\end{cases}
\]

where the subscript \( t \) refers to the increasing number of turns, \( i_t \) refers to the user input and \( o_t \) refers to the LLM’s output, formulated as:

\[
o_t = \mathcal{F}_{\theta}(h_{t-1}, i_t)
\]

where \( \mathcal{F}_{\theta} \) refers to the LLM, and \( \theta \) denotes its parameters. Consequently, an LLM with parameters \( \theta \) seeks to maximize the likelihood: \( \sum_{t=1}^T \log p_\theta(o_t | h_{t-1}, i_t) \), where \( T \) refers to the total number of interaction turns.

The distinction between ChatGE and a general multi-turn Human-LLM interaction lies in the specialization of the input and output. The user input \( i_t \) consists of instructions about their game concept and feedback to the LLM. The LLM’s output \( o_t \) includes both interactions with the user and code snippets to implement the user’s game concept in one turn. To enable an LLM to function as a ChatGE, we instruct the LLM to perform the following processes in each turn: (1) \( P_{script} \): configure the game script segment based on the user’s input(Enclosed by <script></script>: in Figure 1); (2) \( P_{code} \): generate the corresponding code snippet based on the game script segment(Enclosed by <code></code>: in Figure 1); (3) \( P_{utter} \): interact with the user, including guidance and feedback(Enclosed by <utter></utter>: in Figure 1). For interaction and coding requirements, \( P_{code} \) and \( P_{utter} \) are essential. \( P_{script} \) serves as an intermediate process, akin to the reasoning in chain-of-thought (CoT) (Wei et al., 2022). Additionally, it can also act as a visual representation of the current development progress. Compared to code, a script is much easier for people to understand, especially those without a programming background. Therefore, \( o_t \) can be specified as:

\[
o_t = (s_t, c_t, u_t) = \mathcal{F}_{\theta}(s_t, c_t, u_t | h_{t-1}, i_t).
\]

where \( s_t, c_t, u_t \) refer to the outputs of \( P_{script}, P_{code}, \) and \( P_{utter} \) respectively. Furthermore, the ultimate objective of this task, CustomGame \( C \) can be obtained by merging \( c_t \) across all turns:

\[
C = Merge(c_1, c_2, \ldots, c_T)
\]
where \( Merge \) denotes the merge function. It can be determined by the specific game implementation. In our implementaion, we embed \( c_t \) into the base code of the specific game.

4 Data Generation

In this section, we discuss our attempt in data generation. Utilizing LLMs to create ChatGE requires fine-tuning on substantial supervised data. However, manually crafting diverse interactions with script-code pairs is a challenging task. Compared to fully manual annotation, harnessing LLMs to synthesize data is more efficient and has become a popular method for addressing the issue of insufficient data. We propose a pipeline consisting of three main steps to synthesize data, starting with a small set of manually crafted seed data, as illustrated in Figure 3. We use GPT-4o as the generator.

Init pool First, we manually craft a few script-code pairs for different custom games, which serve as seed data. They are then split into script segments and code snippets, added to the pool.

Generate new pairs Then, we sample pairs of script segments and code snippets and generate new pairs based on these selections. We prompt the generator to modify the code snippet, then generate the corresponding script segment. This order ensures higher-quality data, as it’s easier to describe code with a script than generate code from a script. We filter out any code that fails to execute, continuing until the pool contains sufficient valid entries.

Generate interaction data Finally, we generate the interaction data depicted in Figure 1 using the script-code pairs. This process involves two lines: (1) generating interaction snippets based on pairs of script segments and code snippets from the pool; (2) generating complete interactions from complete script-code pairs. The necessity of these two data components will be discussed in the next section.

5 Training Strategy

In this section, we present our training strategy. In our framework, the LLM will perform \( P_{script} \), \( P_{code} \) and \( P_{utter} \) step by step, challenging its joint capability of interaction and programming. On the other hand, directly training on complete interaction data is inefficient. Therefore, we propose a three-stage training strategy following curriculum learning principles to transfer the dialogue-based LLM to ChatGE smoothly.

Stage-1: Base Training This stage aims to train the base interaction ability of the model. Interaction ability is the most fundamental ability for ChatGE and serves as the foundation for the following two stages. Since most LLMs have already undergone sufficient and efficient supervised fine-tuning (SFT) (Brown et al., 2020; Raffel et al., 2020; Ouyang et al., 2022), we can directly use such models for Stage-1.

Stage-2: Core Training This stage aims to train the core capabilities of the model, namely the joint capability of programming and interaction. It fine-tunes the model from Stage-1 on interaction snippets that follow the ChatGE format. As illustrated
in Figure 1, we instruct the model to perform the \( P_{script}, P_{code} \) and \( P_{utter} \) step by step to extract the user’s concept of the game, implement it in code, and provide guidance and feedback for interaction.

Stage-3: Alignment This stage aims to align the model with a complete interaction context to fully develop a game as a ChatGE. It fine-tunes the model from Stage-2, which already possesses significant programming and interaction capabilities. At this stage, we only need to extend its ability for multi-turn interactions as a ChatGE, particularly in guiding users to complete game development according to the predefined script. The input is the whole history of multi-turn interaction. Since the model already possesses strong multi-turn interaction and long-context capabilities following Stage-1 training, only a small dataset is required for alignment at this stage.

6 Experiments

In this section, we construct a ChatGE for a poker game. We employ the proposed data synthesis pipeline to generate the corresponding dataset, fine-tune a ChatGE using the presented strategy and evaluate its performance.

6.1 Dataset

<table>
  <tr>
    <th>Texas hold’em</th>
  </tr>
  <tr>
    <td><b>Config:</b><br>Number of players: 3<br>Min bet: 10<br>Max bet: 1000<br>Suit: H, D, C, S<br>Suit have rank: False<br>Card value rank: 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, J, Q, K, A<br>Card combinations rank: High Card, Pair, Two Pair, Three of a Kind, Straight, Flush, Full House, Four of a Kind, Straight Flush</td>
  </tr>
  <tr>
    <td><b>Phase:</b><br>start: Config the game, prepare the deck and chips for all players.<br>blind: Randomly assign two: small blind bets minimum, big blind double.<br>deal: Deal x cards to each player.<br>switch: Each discard and draws the same number of cards from the deck.<br>bet: Each bets until all the unfolded match the highest or only 1 remains.<br>flop: Discard a card from the deck. Reveal x community cards.<br>Flow: start, blind, deal2, bet, flop3, bet, flop1, bet, flop1, bet, show, prize</td>
  </tr>
</table>

Table 1: An example game script for a poker game.

Poker Game Poker, a worldwide card game, e.g. Texas hold’em, Badugi. These poker games can be abstracted into a generic game script. Table 1 presents an example example of such a script for the classic Texas hold’em. This generic script allows for the configuration of several common elements across different poker games, including the number of players, minimum and maximum bet limits, suit types and rankings, single-card rankings, multi-card combination rankings, game phases, and overall game flow. By adjusting these elements, virtually infinite variations of poker can be created. Notably, each game in our dataset corresponds to a unique configuration, including customizable phases. For example, a standard “flopX” phase might involve discarding one card from the deck and then revealing x community cards. This phase can be customized by adding a rule such as, “After each flop, discard one more card,” thereby creating a new variant of the “flopX” phase.

<table>
  <tr>
    <th>Statistics</th>
    <th>Training</th>
    <th>Test</th>
  </tr>
  <tr>
    <td colspan="3"><i>manually crafted(seed data for training)</i></td>
  </tr>
  <tr>
    <td># of games</td>
    <td>20</td>
    <td>10</td>
  </tr>
  <tr>
    <td># of script-code pairs(functions)</td>
    <td>180</td>
    <td>90</td>
  </tr>
  <tr>
    <td colspan="3"><i>synthesis</i></td>
  </tr>
  <tr>
    <td># of complete interactions</td>
    <td>36</td>
    <td>/</td>
  </tr>
  <tr>
    <td># of interaction snippets</td>
    <td>3718</td>
    <td>/</td>
  </tr>
</table>

Table 2: Statistics of training and test data.

Data Statistics Table 2 shows the statistics of the training and test data that we construct. The interaction data format follows Figure 1.

6.2 Setup

We employ LLaMA3.1-8B-Instruct\(^1\) (Dubey et al., 2024) for Stage-1 and finetune it using LoRA (Hu et al., 2021) with \( r = 8, \alpha = 32 \), and a learning rate of 3e-4. We train 3 epochs on the 3718 interaction snippets for Stage-2 and 5 epochs on the 36 complete interactions for Stage-3.

To assess the performance of the LLM in a dynamic multi-turn interaction environment, we require a user to interact with the LLM, as demonstrated in our ChatGE framework. Simulating the user using a rule-based approach is complex, and employing human annotators poses challenges related to inconsistent standards and high costs. To address these issues, we use GPT-4o-mini as the interactor to simulate the user, a practice increasingly adopted in dynamic multi-turn interaction environments (Wang et al., 2023b; Terragni et al., 2023; Davidson et al., 2023; Sekulić et al., 2024; Luo et al., 2024; Xiang et al., 2024; Yu et al., 2024b). For evaluation, we provide the interactor with a manually crafted game script and instruct them to treat it as the game concept they have in mind. The interactor then interacts with the LLM, resulting

\(^1\)https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct
<table>
  <tr>
    <th>Metric</th>
    <th>Description</th>
    <th>Evaluation Metrics</th>
    <th>Score</th>
    <th>Scoring Guide Criteria</th>
  </tr>
  <tr>
    <td>Guidance Logic</td>
    <td>How the response guide the user step-by-step to complete the game.</td>
    <td></td>
    <td>1 Poor<br>2 Below Avg.<br>3 Above Avg.<br>4 Strong</td>
    <td>Significant deficiencies or inaccuracies.<br>Noticeable weaknesses, lacking in several areas.<br>Mostly on target with a few minor shortcomings.<br>Strong performance, often surpasses expectations.</td>
  </tr>
  <tr>
    <td>Relevance</td>
    <td>Logical structure and soundness of reasoning, including the support and validity of conclusions.</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Coherence</td>
    <td>The extent to which the response stays on topic and within the scope of the assistant role.</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>Conciseness</td>
    <td>Brevity and clarity of the response, avoiding unnecessary elaboration or repetition.</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
</table>

Table 3: Evaluation Metrics and Scoring Guide. We design the criteria following Yu et al. (2024b); Wu et al. (2024b); Zheng et al. (2024); Wang et al. (2023c); Guo et al. (2023).

in a multi-turn interaction about a specific custom game. This allows us to use the game script and its corresponding code as the ground truth for evaluating the generated interaction.

6.3 Metrics

We assess model performance from two perspectives: interaction quality and code correctness.

Interaction Quality The interaction quality is assessed by an evaluator model, which assesses the output for guidance, logic, relevance, coherence and conciseness. Following KIEval (Yu et al., 2024b), we implement a scoring system to quantitatively grade model performance in different aspects. Responses are rated on a definitive scale from 1 to 4 for each aspect, where 1 and 4 denote ‘Poor’ and ‘Strong’ performance, respectively, as detailed in Table 3. These scores are designed to encourage decisive evaluations. To facilitate comparison, we normalize the scores, ensuring that a rating of 1.0 indicates perfect performance. We utilize GPT-4o as the evaluator, run 5 times and average the results.

Code correctness We evaluate code correctness using two functional-level metrics and two overall-level metrics to measure correctness of individual functions and the entire game, respectively.

• \( F\text{-}ESR \) represents the execution success rate on individual functions across the entire test set to measure the model’s basic coding capability. Execution success means no runtime errors.

• \( F\text{-}Acc \) represents the accuracy on individual functions of the code, assessed through black-box testing to determine if the generated code is correct. Specifically, we replace player input with random input and, for each run, fix the random seed. We then compare the resulting game states after multiple turns until the game ends of the generated code with the ground truth. We conduct 40 runs, each with a different random seed, for every entry. If all runs produce identical states, the code is considered correct.

• \( ESR \) represents the execution success rate of the entire game code.

• \( Acc \) represents the accuracy of the entire game.

6.4 Main Results

We evaluate ChatGE on the test data we construct, as shown in Table 2. Table 4 presents the performance of our ChatGE, including both interaction quality and code correctness. For comparison, we take several representative closed-source and open-source LLMs in a 5-shot setting as baselines. Intuitively, ChatGE excels in both interaction quality and code correctness.

6.4.1 Interaction Quality

All models exhibit high interaction quality. Our ChatGE excels across all dimensions, showcasing exceptional capabilities in interacting with the user throughout the interactive development process. Compared to Llama-3.1-8B-Instruct, our fine-tuned model excels in guidance and logic, effectively guiding the user to develop the game logically.

6.4.2 Code Correctness

In our results, all models significantly outperform in functional-level metrics compared to overall-level metrics. This suggests that while LLMs excel at producing functional code, they face challenges when generating long, complete code. Additionally, it is evident that executability is more easily achieved than accuracy across all models, with our model reaching a perfect ESR of 100. This indicates that LLMs excel at generating code that is syntactically executable. Notably, ChatGE outperforms in all metrics. It achieves an impressive F-Acc of 99.0, outperforming the second-best model by 9 points. Moreover, it reaches an ESR of 100, surpassing the second-best by 20 points. Furthermore, it attains an Acc of an astounding 90, outstripping the second-best by 60 points.

To conduct a more in-depth analysis, we compute the function-level code correctness in Table 5. Most models excel on fixed functions and two simple variable functions: config and flow. These two
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="6">Interaction Quality</th>
    <th colspan="4">Code Correctness</th>
  </tr>
  <tr>
    <th>Gui.</th>
    <th>Log.</th>
    <th>Rel.</th>
    <th>Coh.</th>
    <th>Con.</th>
    <th>Overall</th>
    <th>F-ESR</th>
    <th>F-Acc</th>
    <th>ESR</th>
    <th>Acc</th>
  </tr>
  <tr>
    <th colspan="11">5-shot</th>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td>
    <td>94.5</td>
    <td>96.5</td>
    <td>100</td>
    <td>99.0</td>
    <td>96.5</td>
    <td>98.0</td>
    <td>95.8</td>
    <td>87.9</td>
    <td>60.0</td>
    <td>30.0</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>98.5</td>
    <td>98.0</td>
    <td>100</td>
    <td>100</td>
    <td>99.0</td>
    <td>99.0</td>
    <td>93.0</td>
    <td>88.5</td>
    <td>50.0</td>
    <td>30.0</td>
  </tr>
  <tr>
    <td>Llama-3.1-8B-Instruct</td>
    <td>97.5</td>
    <td>98.0</td>
    <td>100</td>
    <td>99.5</td>
    <td>99.0</td>
    <td>98.5</td>
    <td>100</td>
    <td>90.0</td>
    <td>60.0</td>
    <td>10.0</td>
  </tr>
  <tr>
    <th colspan="11">Fine-tuning</th>
  </tr>
  <tr>
    <td>ChatGE</td>
    <td><b>98.5</b></td>
    <td><b>99.0</b></td>
    <td><b>100</b></td>
    <td>99.5</td>
    <td><b>99.0</b></td>
    <td><b>100</b></td>
    <td><b>100</b></td>
    <td><b>99.0</b></td>
    <td><b>100</b></td>
    <td><b>90.0</b></td>
  </tr>
  <tr>
    <td>w/o. \( P_{script} \)</td>
    <td>98.0</td>
    <td>97.0</td>
    <td>100</td>
    <td>99.0</td>
    <td>96.5</td>
    <td>98.5</td>
    <td>100</td>
    <td>98.8</td>
    <td>100</td>
    <td>80.0</td>
  </tr>
  <tr>
    <td>w/o. synthesis</td>
    <td>96.5</td>
    <td>96.0</td>
    <td>100</td>
    <td>98.0</td>
    <td>96.5</td>
    <td>98.0</td>
    <td>97.4</td>
    <td>86.8</td>
    <td>70.0</td>
    <td>0</td>
  </tr>
  <tr>
    <td>w/o. Stage-2</td>
    <td>96.5</td>
    <td>97.5</td>
    <td>100</td>
    <td>99.0</td>
    <td>96.0</td>
    <td>98.5</td>
    <td>98.2</td>
    <td>89.2</td>
    <td>80.0</td>
    <td>10.0</td>
  </tr>
  <tr>
    <td>w. Mixed-stage</td>
    <td>92.5</td>
    <td>96.5</td>
    <td>99.5</td>
    <td>96.0</td>
    <td>92.0</td>
    <td>95.0</td>
    <td>95.0</td>
    <td>88.5</td>
    <td>80.0</td>
    <td>20.0</td>
  </tr>
</table>

Table 4: Main results of different models and the ablation study of ChatGE. The number of functions generated by the model can vary due to factors such as repeated modifications or missed queries. Functional-level metrics primarily assess the correctness of the generated code without accounting for recall rate, which is instead reflected in the overall-level metrics.

functions require only basic assignment statements to configure the game, allowing them to generalize effectively. However, for functions with more complex code logic, namely blind, dealx, and flopx, the baselines generally underperform, with the lowest F-Acc reaching just 20. These results indicate that the accumulation of errors across these functions leads non-fine-tuned models to exhibit low correctness in overall-level evaluation. It is important to note that the model is required to be all-round at each function; otherwise, the overall performance will degenerate in a way of Buckets effect (Wu et al., 2024a). Delightfully, our ChatGE achieves near-perfect performance across all functions, resulting in an Acc far exceeding the baselines.

6.5 Ablation Study

We ablate different variants from the full ChatGE architecture, the results are presented in Table 4 and Table 5. Training data statistics of ablations can be found in Appendix C.

Ablation on \( P_{script} \) A slight decrease can be observed in interaction quality across nearly all dimensions without \( P_{script} \). Additionally, F-Acc drops by 0.2 points and Acc by 10.0 points. As shown in Table 5, the only failure occurs on a flopx function when compared to the complete ChatGE architecture. This suggests that \( P_{script} \) can enhance both interaction and coding abilities in certain cases.

Ablation on synthetic data In this setting, we directly employ manually crafted script-code pairs, splitting them into snippets to generate complete interactions and interaction snippets. A slight decline can be observed in interaction quality across most dimensions, alongside a significant decrease in code correctness, with Acc dropping to 0. Notably, the code correctness is even lower than that of the 5-shot Llama-3.1-8B-Instruct. As shown in Table 5, this decline is attributed to poor performance on the two most challenging functions, dealx and flopx. This can be explained by the model overfitting on the limited data due to the absence of synthetic data, which leads to poor generalization.

Ablation on training strategy We conducted comprehensive ablation experiments on our three-stage training strategy, testing setups: w/o. Stage-1, w/o. Stage-2, w/o. Stage-3, and w. Mixed-stage. In the setups without Stage-1 or Stage-3, the model loses its guiding and interaction abilities in multi-turn scenarios, resulting in ESR and Acc values of 0, which we did not report. This suggests both Stage-1 and Stage-3 are crucial for enhancing interaction abilities. As shown in Table 4, the model’s interaction quality decreases across most dimensions without Stage-2, with a significant drop in code correctness (Acc = 10.0). A sharp decline in F-Acc for the dealx and flopx functions is clearly evident in Table 5. This indicates Stage-2 is crucial for core interactions and programming capabilities, especially programming capabilities. The Mixed-stage, mixing Stage-2 and Stage-3, combines all the complete interactions and snippets and fine-tuning in one stage. It results in worse performance in
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="2">config*</th>
    <th colspan="2">start</th>
    <th colspan="2">blind*</th>
    <th colspan="2">deals*</th>
    <th colspan="2">flop*</th>
    <th colspan="2">switch</th>
    <th colspan="2">bet</th>
    <th colspan="2">flow*</th>
    <th rowspan="2">Overall</th>
  </tr>
  <tr>
    <th>F-ESR F-Acc</th><th>F-ESR F-Acc</th>
    <th>F-ESR F-Acc</th><th>F-ESR F-Acc</th>
    <th>F-ESR F-Acc</th><th>F-ESR F-Acc</th>
    <th>F-ESR F-Acc</th><th>F-ESR F-Acc</th>
    <th>F-ESR F-Acc</th><th>F-ESR F-Acc</th>
    <th>F-ESR F-Acc</th><th>F-ESR F-Acc</th>
    <th>F-ESR F-Acc</th><th>F-ESR F-Acc</th>
  </tr>
  <tr>
    <td colspan="19"><b>5-shot</b></td>
  </tr>
  <tr>
    <td>GPT-3.5-turbo</td><td>100</td><td>88.9</td><td>100</td><td>100</td><td>87.5</td><td>87.5</td><td>88.9</td><td>55.6</td><td>87.5</td><td>75.0</td><td>100</td><td>100</td><td>100</td><td>100</td><td>88.9</td><td>66.7</td><td>95.8</td><td>87.9</td>
  </tr>
  <tr>
    <td>GPT-4o</td><td>100</td><td>100</td><td>100</td><td>100</td><td>70.0</td><td>70.0</td><td>90.0</td><td>90.0</td><td>100</td><td>60.0</td><td>100</td><td>100</td><td>80.0</td><td>80.0</td><td>100</td><td>100</td><td>93.0</td><td>88.5</td>
  </tr>
  <tr>
    <td>Llama-3.1-8B-Instruct</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>50.0</td><td>100</td><td>60.0</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>66.7</td><td>100</td><td>90.0</td>
  </tr>
  <tr>
    <td colspan="19"><b>Fine-tuning</b></td>
  </tr>
  <tr>
    <td>ChatGE</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>90.0</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>99.0</td>
  </tr>
  <tr>
    <td>w/o. Parrot</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>90.0</td><td>100</td><td>90.0</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>98.8</td><td>98.8</td>
  </tr>
  <tr>
    <td>w/o. synthesis</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>88.9</td><td>22.2</td><td>100</td><td>55.6</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>93.8</td><td>86.8</td>
  </tr>
  <tr>
    <td>w/o. Stage-2</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>90.0</td><td>20.0</td><td>90.0</td><td>60.0</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td><td>98.2</td><td>89.2</td>
  </tr>
  <tr>
    <td>w. Mixed-stage</td><td>100</td><td>100</td><td>100</td><td>100</td><td>81.8</td><td>81.8</td><td>100</td><td>33.3</td><td>90.0</td><td>90.0</td><td>90.0</td><td>90.0</td><td>81.8</td><td>81.8</td><td>100</td><td>75.0</td><td>95.0</td><td>88.5</td>
  </tr>
</table>

Table 5: Function-level code correctness of different models and the ablation study of ChatGE. Functions with an asterisk (*) are variable functions in the test set, while the remaining functions are fixed. Variable functions in test set are unseen in the training set.

both interaction quality and code correctness (Acc = 20.0). This indicates that a mixed-stage training strategy for complete interactions and snippets hinders both the interaction and programming capabilities of the model. This suggests that the three-stage training strategy effectively enhances the model’s joint interaction and programming capabilities.

6.6 Human Evaluation Study

We conduct human evaluation study to validate the reliability of LLM assessments. A total of 20 human volunteers with no experience in game development are required to interact with the model and evaluate the Interaction Quality across 10 test cases. As shown in Table 6, the results of the human evaluation study are consistent with the LLM evaluation. Participant distribution can be found in Appendix B.

<table>
  <tr>
    <th rowspan="2">Model</th>
    <th rowspan="2">Interactor</th>
    <th rowspan="2">Evaluator</th>
    <th colspan="5">Interaction Quality</th>
  </tr>
  <tr>
    <th>Gui.</th><th>Log.</th><th>Rel.</th><th>Coh.</th><th>Con.</th><th>Overall</th>
  </tr>
  <tr>
    <td>GPT-4o</td><td>GPT-4o</td><td>Human</td><td>98.5</td><td>98.0</td><td>100</td><td>100</td><td>99.0</td><td>99.0</td>
  </tr>
  <tr>
    <td>Human</td><td>Human</td><td>Human</td><td>96.00</td><td>94.25</td><td>100</td><td>99.12</td><td>97.12</td><td>96.25</td>
  </tr>
  <tr>
    <td>ChatGE</td><td>GPT-4o</td><td>Human</td><td>98.5</td><td>99.0</td><td>100</td><td>99.5</td><td>99.0</td><td>100</td>
  </tr>
  <tr>
    <td>Human</td><td>Human</td><td>Human</td><td>96.50</td><td>94.62</td><td>100</td><td>98.75</td><td>98.75</td><td>97.50</td>
  </tr>
</table>

Table 6: Human evaluation results compared with LLM evaluation results on the interaction quality of GPT-4o and ChatGE, based on various metrics as outlined in Table 3.

6.7 Case Study

In Table 7, we present two representative cases comparing GPT-4o and ChatGE. In Case 1, the code generated by GPT-4o is logically correct, but the function call is used incorrectly. The proper usage of “random.choice” should be “random.choice(x)”, but it seems to have confused this with the “random_choice” usage provided in the in-context examples. Similarly, in Case 2, GPT-4o mistakenly treated “self.players[player_id]” as a dict. This can be attributed to its misalignment with the engine, also known as hallucination (Ji et al., 2023). In comparison, ChatGE is well-aligned and does not exhibit this phenomenon in the test set.

<table>
  <tr>
    <th colspan="2">Case 1</th>
    <th colspan="2">blind: Randomly choose two players as small blind and big blind respectively. Place 1/2 minimum bet for small blind and double minimum bet for big blind.</th>
  </tr>
  <tr>
    <th>GPT-4o</th>
    <td>def blind(self):</td>
    <td>def bet(player_id, amount):<br>self.players[player_id].bet += amount<br>self.players[player_id].remain -= amount<br>small_blind, big_blind = random.choice(self.player_ids, 2)<br>bet(small_blind, self.min_bet / 2)<br>bet(big_blind, self.min_bet * 2)</td>
  </tr>
  <tr>
    <th>ChatGE</th>
    <td>def blind(self):</td>
    <td>def bet(player_id, amount):<br>self.players[player_id].bet += amount<br>self.players[player_id].remain -= amount<br>small_blind, big_blind = random.choice(self.player_ids, 2)<br>bet(small_blind, self.min_bet / 2)<br>bet(big_blind, self.min_bet * 2)</td>
  </tr>
  <tr>
    <th colspan="2">Case 2</th>
    <th colspan="2">deals: Deal x cards to each player and discard 1 cards from the deck afterward.</th>
  </tr>
  <tr>
    <th>GPT-4o</th>
    <td>def deals(self, x):</td>
    <td>for i in range(x):<br>for player_id in self.players:<br>self.players[player_id]['hole'].append(self.deck.pop())<br>self.deck.pop()</td>
  </tr>
  <tr>
    <th>ChatGE</th>
    <td>def deals(self, x):</td>
    <td>for i in range(x):<br>for p in self.players:<br>self.players[p]['hole'] += [self.deck.pop()]<br>self.deck.pop()</td>
  </tr>
</table>

Table 7: Case study of the results of GPT-4o and ChatGE. Only the code part is retained.

7 Conclusion

This paper introduces the Chat Game Engine (ChatGE) and a paradigm for training it to enable users to create custom games using natural language. To enable an LLM to function as a ChatGE, we instruct it to generate script segments, code snippets and interactions for each turn in the development process. A data synthesis pipeline is proposed to generate sufficient training data, along
with a three-stage training strategy to enhance the joint capability of interaction and programming of the LLM. Embodied in a poker game, we demonstrate the performance of the ChatGE through a comprehensive evaluation.

Limitations

While our ChatGE offers exciting potential for applying LLMs as multi-turn game development, several limitations warrant further exploration: (1) Limited scalability: We notice that it is still very hard to generalize ChatGE to all games or all game engines. Instead, in this work, we choose a specific game to illustrate the idea of ChatGE. The entire data generation and training process must be repeated to adapt this approach to a new game. However, all the prompts we employ are intentionally designed to be game-agnostic, and our validation metrics, such as ESR and ACC based on game states, are universally applicable across various types of games. This design ensures that they can be adapted for use in other gaming contexts. Of course, our future work will definitely focuses on the scalability of ChatGE. (2) Limited scope and modalities: Our current ChatGE primarily support text-based games like Poker. Additional modalities such as images, sound, or video could enrich the game and are almost essential in modern video games, but this expansion presents technical and design challenges. These limitations highlight the importance of ongoing research and development efforts aimed at addressing the challenges associated with LLM-based game development.

Ethics Statement

Our research involves collecting evaluation data from real human participants. We adhere to strict ethical guidelines to ensure their privacy, consent, and well-being. Key ethical principles include: (1) Informed Consent: Participants are provided with detailed information about the study’s purpose, procedures, and their rights. They are informed that they can withdraw from the study at any time, without any negative consequences. (2) Data Anonymization: To safeguard participant privacy, all collected evaluation data, including dialogue data and questionnaires, is anonymized. Personal identifiers are removed, ensuring that individuals cannot be traced from the data. (3) Data Security: All collected data is stored securely, with access restricted to authorized personnel only. We implement stringent data protection measures to prevent unauthorized access, disclosure, or misuse of the data.

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A Technical details

We trained ChatGE on a single A800 GPU and the entire training process took just 1.5 hours. For data generation, the process utilized about 11M input tokens and 1.2M output tokens, costing about $40. For evaluation, each model evaluation costs about $1.2, bringing the total cost for all evaluations to approximately $10.

B Participant Distribution

A total of 20 human volunteers are recruited from diverse backgrounds to ensure a wide range of perspectives, tasked with interacting with the model
Figure 4: Participant distribution across different demographics, showcasing age, education level, and programming knowledge.

and evaluating the Interaction Quality across 10 test cases. All participants have no game development experience. Figure 4 provides a comprehensive overview of participant demographics showcasing age, education level, and programming knowledge.

C  Ablation statistics

<table>
  <tr>
    <th rowspan="2"></th>
    <th colspan="2">seed</th>
    <th colspan="2">synthesis</th>
  </tr>
  <tr>
    <th>Com. (20)</th>
    <th>Snip. (180)</th>
    <th>Com. (36)</th>
    <th>Snip. (3718)</th>
  </tr>
  <tr>
    <td>ChatGE</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
  </tr>
  <tr>
    <td>w/o. \( P_{script} \)</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
  </tr>
  <tr>
    <td>w/o. synthesis</td>
    <td>✓</td>
    <td>✓</td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>w/o. Stage-2</td>
    <td>✓</td>
    <td></td>
    <td></td>
    <td></td>
  </tr>
  <tr>
    <td>w. Mixed-stage</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
    <td>✓</td>
  </tr>
</table>

Table 8: Traning data statistics of ablations. Com. refers to complete interactions and Snip. refers to interaction snippets.

D  Prompts Demonstration

In this section, we provide the prompts used in the paper. Each {...} component above will be substituted with corresponding information. For more details, please refer to our code. These prompts are not designed for any specific game, they can be used to build a ChatGE for any {Game_name}.

Table 9-11 present the prompts used in the Data Generation. Table 12-13 present the system prompts for models. Table 14-15 present the prompts used in the Evaluation.
Prompt for New Script-code Pairs Generation

Generate new code snippet and the corresponding script segment of a {Game_name} game based on the given original code snippet and the corresponding original script segment.

1. Modify the code logic to obtain a new code segment and output the corresponding script segment.
2. The new code snippet is obtained by modifying the original code snippet.
3. Keep the input parameters unchanged, do not introduce new input parameters.
4. The generated new code snippet should not introduce new instance attributes and involved methods such as ‘self.xxx’ or ‘self.xxx...’ compared to the original code snippet. The generated new code snippet can only include instance attributes and instance methods involved in the original code snippet. You cannot create new ones. For example, there is a original code snippet below:

def bet_done(self, wait_to_bet):
    all_bet = [self.players[p].bet for p in self.get_unfold_players()]
    if not wait_to_bet and all([b == all_bet[0] for b in all_bet]):
        return True
    return False

In this code snippet, the instance attributes and instance methods involved are only ‘self.players’ and ‘self.get_unfold_players()’. Therefore, in the new code snippet generated from this original code snippet, the instance attributes and instance methods involved should also only be ‘self.players’ and ‘self.get_unfold_players()’, other created ones such as ‘self.group’, ‘self.discard_pile’, ‘self.burn_pile’, ‘self.burn_card’ are not allowed to be used.
5. Do not use ‘print’ or logging information.
6. The script segment can be seen as a description of the code snippet.
7. Try to be creative and diverse.
8. The output format should follow the original, without any redundant information.

# Examples
{In-context Examples}

# Start of Official Requests
## original code snippet:
{original_code}

## original script segment:
{original_script}

Table 9: Prompt for generating new pairs in Data Generation for ChatGE.
Prompt for Interaction Snippets Generation

Generate a dialogue between a user and an assistant based on the following rules and given script segment and code snippet.

1. The user edits game script segments using natural language during interactions with the assistant.
2. The assistant interacts with the user to achieve interactive game development. The assistant guides the user in editing game script segments, generates corresponding code snippets, and interacts with the user through dialogue.
3. Each turn of the assistant’s output should include three processes: “script”, “code”, and “utter”, corresponding to three blocks: <script></script>, <code></code>, <utter></utter>. Formally, these three blocks must exist, even if the content is empty.
4. The ‘script’ process: The assistant generates the game script segment based on the user’s input of the current turn. Return modifications to the script as changes, rather than returning the entire script. The script is a Python dict, so you can use simple Python code to represent modifications to it, such as: script[‘xxx’] = ‘xxx’. The ‘script’ process should be enclosed using `<script>` tag.
5. The ‘code’ process: The assistant generates the corresponding Python code snippet based on the game script segment from the ‘script’ process. The complete code is a CustomGame class that inherits from GameBase class, but only the methods related to the given script segment need to be generated. The ‘code’ process should be enclosed using `<code>` tag.
6. The ‘utter’ process: The assistant interacts with the user, including responding to the user’s input of the current turn, summarizing the results of the current turn, and guiding the user to continue with the next turn of interaction. The ‘utter’ process should be enclosed using `<utter>` tag.
7. The script segment and code snippet have already been provided. In the assistant’s ‘script’ and ‘code’ process, use the given script segment and code snippet; do not write your own.
8. The assistant does not know about the existence of the script segment in the dialogue and needs to obtain it from the user’s input.
9. The given script segment and code snippet are essentially an outline of the plot development. The assistant’s ‘script’ and ‘code’ process must be entirely derived from or inferred from the user’s input. The user’s input should be more natural language-based and not a direct copy of the given script segment.
10. The dialogue must cover and only cover the given script segment, and no other content should appear.

{Formatting Instruction}

__________________________
# Examples
{In-context Examples}
__________________________

# Start of Official Requests
## script segment:
{script segment}

## code snippet:
{code snippet}

## dialogue:
Prompt for Complete Interactions Generation

Generate a dialogue between a user and an assistant based on the following rules and given script segment and code snippet.

1. The user edits game script segments using natural language during interactions with the assistant.
2. The assistant interacts with the user to achieve interactive game development. The assistant guides the user in editing game script segments, generates corresponding code snippets, and interacts with the user through dialogue.
3. Each turn of the assistant’s output should include three processes: "script", "code", and "utter", corresponding to three blocks: <script></script>, <code></code>, <utter></utter>. Formally, these three blocks must exist, even if the content is empty.
4. The 'script' process: The assistant generates the game script segment based on the user’s input of the last turn. Return modifications to the script as changes, rather than returning the entire script. The script is a Python dict, so you can use simple Python code to represent modifications to it, such as: script[ 'xxx' ] = 'xxx'. The 'script' process should be enclosed using '<script>' tag.
5. The 'code' process: The assistant generates the corresponding Python code snippet based on the game script segment from the 'script' process. The complete code is a CustomGame class that inherits from GameBase class, but only the methods related to the given script segment need to be generated. The 'code' process should be enclosed using '<code>' tag.
6. The 'utter' process: The assistant interacts with the user, including responding to the user’s input of the last turn, summarizing the results of the current turn, and guiding the user to continue with the current turn of interaction. The 'utter' process should be enclosed using '<utter>' tag.
7. The script segment and code snippet have already been provided. You need to randomly distribute them across multiple turns and generate an interactive dialogue between the assistant and the user. This means the assistant guides the user step by step to complete this game script segment. In a single turn of dialogue, the user’s input should not contain too much information. If a large input is required, it should be divided into multiple turns.
8. In the assistant’s 'script' and 'code' process, use the given script segment and code snippet; do not write your own.
9. The dialogue must cover and only cover all the given script segment, and no other content should appear.
10. The assistant does not know about the existence of the script segment in the dialogue and needs to obtain it from the user’s input.
11. The given script segment and code snippet are essentially an outline of the plot development. The assistant’s 'script' and 'code' process must be entirely derived from or inferred from the user’s input. The user’s input should be more natural language-based and not a direct copy of the given script segment.
12. In the first turn, the 'script' and 'code' process of the assistant should be empty because the user has not yet input a game script segment. In the first turn, the assistant should greet the user and start guiding them. In the end, after the user has completed the entire script under the assistant’s guidance, the assistant should convey to the user that the game development is complete.
13. The assistant should guide the user step by step along a specific line to complete each part of the game script:
{Game_script_line}

{Formatting Instruction}

# Examples
{In-context Examples}

# Start of Official Requests
## script segment:
{script segment}

## code snippet:
{code snippet}

## dialogue:
System Prompt for Baselines in a 5-shot Setting

You are a helpful assistant assigned to interact with the user for the interactive development of a {Game_name} game.

1. The user edits game script segments using natural language.
2. The assistant guides the user in editing game script segments, generates corresponding code snippets, and interacts with the user through dialogue.
3. Each turn of the assistant’s output should include three processes: "script", "code", and "utter", corresponding to three blocks: <script></script>, <code></code>, <utter></utter>. Formally, these three blocks must exist, even if the content is empty.
4. The 'script' process: The assistant generates the game script segment based on the user’s input of the current turn. Return modifications to the script as changes, rather than returning the entire script. The script is a existing Python dict, so you can use simple Python code to represent modifications to it, such as: script['xxx'] = 'xxx'. The 'script' process should be enclosed using `<script>` tag.
5. The 'code' process: The assistant generates the corresponding Python code snippet based on the game script segment from the 'script' process. The complete code is a CustomGame class that inherits from GameBase class, but only the methods related to the given script segment need to be generated. The 'code' process should be enclosed using `<code>` tag.
6. The 'utter' process: The assistant interacts with the user, including responding to the user’s input of the current turn, summarizing the results of the current turn, and guiding the user to continue with the next turn of interaction. The 'utter' process should be enclosed using `<utter>` tag.
7. The assistant’s 'script' and 'code' process must be entirely derived from or inferred from the user’s input. If the user’s input lacks the required information, ask the user for further details, and both the 'script' process and the 'code' process of the assistant should be empty.
8. If the user’s input is unrelated to the script or insufficient to cause changes in the script, the 'script' process and the 'code' process of the assistant should both be empty.
9. If the user has any questions, answer them instead of randomly modifying the script and code on your own.
10. In the first turn, the 'script' and 'code' process of the assistant should be empty because the user has not yet input a game script segment. In the first turn, the assistant should greet the user and start guiding them. In the end, after the user has completed the entire script under the assistant’s guidance, the assistant should convey to the user that the game development is complete.
11. The assistant should guide the user step by step along a specific line to complete each part of the game script, referring to the given script template.

{Formatting Instruction}

# script template
{script template}

# Examples
{In-context Examples}

Table 12: System prompt for baselines in a 5-shot Setting.
System Prompt for ChatGE

You are a helpful assistant assigned to interact with the user for the interactive development of a {Game_name} game.

1. The user edits game script segments using natural language.
2. The assistant guides the user in editing game script segments, generates corresponding code snippets, and interacts with the user through dialogue.
3. Each turn of the assistant’s output should include three processes: "script", "code", and "utter", corresponding to three blocks: <script></script>, <code></code>, <utter></utter>. Formally, these three blocks must exist, even if the content is empty.
4. The 'script' process: The assistant generates the game script segment based on the user’s input of the current turn. Return modifications to the script as changes, rather than returning the entire script. The script is a existing Python dict, so you can use simple Python code to represent modifications to it, such as: script['xxx'] = 'xxx'. The 'script' process should be enclosed using '<script>' tag.
5. The 'code' process: The assistant generates the corresponding Python code snippet based on the game script segment from the 'script' process. The complete code is a CustomGame class that inherits from GameBase class, but only the methods related to the given script segment need to be generated. The 'code' process should be enclosed using '<code>' tag.
6. The 'utter' process: The assistant interacts with the user, including responding to the user’s input of the current turn, summarizing the results of the current turn, and guiding the user to continue with the next turn of interaction. The 'utter' process should be enclosed using '<utter>' tag.
7. The assistant’s 'script' and 'code' process must be entirely derived from or inferred from the user’s input. If the user’s input lacks the required information, ask the user for further details, and both the 'script' process and the 'code' process of the assistant should be empty.
8. If the user’s input is unrelated to the script or insufficient to cause changes in the script, the 'script' process and the 'code' process of the assistant should both be empty.
9. If the user has any questions, answer them instead of randomly modifying the script and code on your own.

Table 13: System prompt for ChatGE.

System Prompt for Interactor

You are a user (as in the Example) of an interactive {Game_name} game development application of a {Game_name} game, interacting with me (the assistant).

1. You should attempt to use natural language to edit game script segments.
2. You should focus on the "utter" part enclosed by the <utter></utter> tag in my output and interact with it according to its guidance.
3. Your response does not need to include any tags.
4. A game script will be given. Assume this is the game script you have in mind. You need to interactively present your ideas under the guidance of the me step by step, i.e., respond based on the relevant parts of the given script. Try not to output too much in one turn.
5. Try to use natural language instead of directly copying the given script segments.
6. Your responses should be as concise as possible and should not include the thought process.

# Examples
{In-context Examples}

# Start of Official Requests
## given game script:
{game script}

Table 14: System prompt for interactor in evaluation.
System Prompt for Evaluator

You are an objective evaluator in an interview. Your task is to evaluate a assistant’s performance during a series of interactions with an user. The conversation alternates between the user (marked with 'user: ') and the assistant (marked with 'assistant'). Evaluate the assistant’s performance in the interactions as well as in context, based on the following aspects independently, rating each on a scale from 1 (Poor) to 4 (Good):

Guidance: How the response guide the user step-by-step to complete the game.
Logic: Logical structure and soundness of reasoning, including the support and validity of conclusions. Whether conclusions are well-supported and arguments are free from logical fallacies.
Relevance: How the response relates to the topic. Ensure responses are within the scope of the "assistant" role, avoiding unpermitted role shifts.
Coherence: How well the response integrates into the context. Consistency with previous statements and overall conversational flow.
Conciseness: Brevity and clarity of the response. Clear, to-the-point communication, free from extraneous elaboration or repetitive words.

Scoring Guide:
1 (Poor): Significant deficiencies or inaccuracies in the aspect.
2 (Below Average): Noticeable weaknesses, partially on target but lacking in several areas.
3 (Above Average): Solid and competent, mostly on target with only a few minor shortcomings.
4 (Good): Strong performance, fully meets and often surpasses expectations.

Evaluation Rules:
1. Evaluate the assistant consistently and objectively without bias, strictly adhering to scoring guide.
2. Score from 1 to 4 for each aspect independently, using only integers. Low score in one aspect should not influence another aspect. Write a brief comment before scoring in the JSON output structure.
3. Write a overall comment and then give an overall score (same scoring guide). The overall comment should be brief and clear. Consider the performance throughout the interaction, not just in the latest round.
4. Format of Evaluation: Output in JSON format strictly following the template, without any other words:
   guidance": "comment": "", "score": 0, "logic": "comment": "", "score": 0, "relevance": "comment": "", "score": 0, "coherence": "comment": "", "score": 0, "conciseness": "comment": "", "score": 0, "overall": "comment": "", "score": 0

# interactions:
{dialogue}

# Evaluation:

Table 15: System prompt for evaluator in evaluation.
E  Example Demonstration

A complete dialogue of User and ChatGE

user:
[start interactive game development]

assistant:
<script>
</script>
<code>
</code>
<utter>
Hello, I am your game development assistant. Let's interactively develop a new poker game.
    Alright, let's start by configuring the essential parameters for our game. Could you provide the number of players and the betting range (minimum and maximum bet)?
</utter>

user:
There should be 3 players. The minimum bet should be 10 and the maximum bet should be 1000.

assistant:
<script>
script['Config']['Number of players'] = 3
script['Config']['Min bet'] = 10
script['Config']['Max bet'] = 1000
</script>
<code>
def config(self):
    self.n_players = 3
    self.min_bet = 10
    self.max_bet = 1000
</code>
<utter>
Great! We've set up the number of players and the betting range. Now, what about the suits and card value rank in the deck?
</utter>

user:
I want to use 4 suits: H, D, C, S. The card ranks should be from 2 to 12 and an additional rank 1.

assistant:
<script>
script['Config']['Suit'] = ['H', 'D', 'C', 'S']
script['Config']['Card value rank'] = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1]
</script>
<code>
def config(self):
    self.n_players = 3
    self.min_bet = 10
    self.max_bet = 1000
    self.suit = ['H', 'D', 'C', 'S']
    self.value = [str(v) for v in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1]]
</code>
<utter>
Perfect! The suits and card value rank have been configured. Do you need to rank the suits or should we ignore suit ranking?
</utter>

user:
No need to rank the suits.

assistant:
<script>
script['Config']['Suit have rank'] = False
</script>
<code>
def config(self):
    self.n_players = 3
    self.min_bet = 10
    self.max_bet = 1000
    self.suit = ['H', 'D', 'C', 'S']
    self.suit_have_rank = False
    self.value = [str(v) for v in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1]]
</code>
<utter>
Got it! Suits won't have any inherent ranking. What about the card combinations rank? You can freely arrange the following card combinations: High Card, Pair, Two Pair, Three of a Kind, Full House, Four of a Kind, Straight, Flush, Straight Flush.
</utter>

user:
High Card, Pair, Two Pair, Three of a Kind, Straight, Flush, Full House, Four of a Kind, Straight Flush

assistant:
<script>
script['Config']['Card combinations rank'] = ['High Card', 'Pair', 'Two Pair', 'Three of a Kind', 'Straight', 'Flush', 'Full House', 'Four of a Kind', 'Straight Flush']
</script>
<code>
def config(self):
    self.n_players = 3
    self.min_bet = 10
    self.max_bet = 1000
    self.suit = ['H', 'D', 'C', 'S']
    self.suit_have_rank = False
    self.value = [str(v) for v in [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1]]
    self.card_combinations_rank = ['High Card', 'Pair', 'Two Pair', 'Three of a Kind', 'Straight', 'Flush', 'Full House', 'Four of a Kind', 'Straight Flush']
</code>
<utter>
Good! The configuration is complete. Now, let's move on to the phase. Please provide the Phase details.
</utter>

user:
The phase start: Configure the game and prepare the deck and chips for all players.

assistant:
<script>
script['Phase']['start'] = 'Configure the game, prepare the deck and chips for all players.'
</script>
<code>
def start(self):
    self.deck = []
    self.community = []
    for v in self.value:
        for s in self.suit:
            self.deck += [''.join([s,v])]

    for i in range(self.n_players):
        self.players[f'p{i+1}'].hole = []
        self.players[f'p{i+1}'].fold = False
</code>
<utter>
Great. The phase 'start' has been added. Are there any other phases?
</utter>

user:
Good. There is a phase called 'shuffle', which shuffles the deck.

assistant:
<script>
script['Phase']['shuffle'] = 'Shuffle the deck.'
</script>
<code>
def shuffle(self):
    shuffle(self.deck)
</code>
<utter>
Get! The phase 'shuffle' has been added. Are there any other phases?
</utter>

user:
I'd like to add a Phase 'blind' where we randomly select two players as the big blind and small blind. The small blind and big blind both place the minimum bet.

assistant:
<script>
script['Phase']['blind'] = 'Randomly choose two players as big blind and small blind respectively. Place minimum bet for small blind and minimum bet for big blind.'
</script>
<code>
def blind(self):
    def bet(player_id, amount):
        self.players[player_id].bet += amount
        self.players[player_id].remain -= amount

    small_blind, big_blind = random_choice(self.player_ids, 2)
    bet(small_blind, self.min_bet)
    bet(big_blind, self.min_bet)
</code>
<utter>
Alright, I've added the blind phase. Are there any other phases?
</utter>

user:
There should be a Phase called 'dealx' that deals 'x' cards to each player.

assistant:
<script>
script['Phase']['dealx'] = 'Deal x cards to each player.'
</script>
<code>
def dealx(self, x):
    for i in range(x):
        for p in self.players:
            self.players[p].hole += [self.deck.pop()]
</code>
<utter>
OK. The phase 'dealx' has been added. Are there any other phases?
</utter>

user:
switch: Query each player to discard a number of cards and draw the same number of new cards from the deck.

assistant:
<script>
script['Phase']['switch'] = 'Query each player to discard a number of cards and draw the same number of new cards from the deck.'
</script>
<code>
def switch(self, switch_indices, player_id):
    for i in switch_indices:
        self.players[player_id].hole[i] = self.deck.pop()
</code>
<utter>
Good! The phase 'dealx' has been added. Are there any other phases?
</utter>

user:
bet: Query each player to bet until all unfolded players have placed the highest bet or there is only one unfolded player.

assistant:
<script>
script['Phase']['bet'] = 'Query each player to bet until all unfolded players have placed the highest bet or there is only one unfolded player.'
</script>
<code>
def bet_done(self, wait_to_bet):
    all_bet = [self.players[p].bet for p in self.get_unfold_players()]
    if not wait_to_bet and all([b==all_bet[0] for b in all_bet]):
        return True
    return False
</code>
<utter>
OK. The phase 'bet' has been added. Are there any other phases?
</utter>

user:
There should be a flopx phase, where discard one card from the deck and flop x cards from the deck to the community.

assistant:
<script>
script['Phase']['flopX'] = 'Discard one card from the deck. Flop x cards from the deck to the community.'
</script>
<code>
def flopx(self, x):
    self.deck.pop()
    for i in range(x):
        self.community += [self.deck.pop()]
</code>
<utter>
OK. The phase 'flopX' has been added. Are there any other phases?
</utter>

user:
No, that's all phases for now.

assistant:
<script>
</script>
<code>
</code>
<utter>
Great! The Phase is complete. Now, let's set up the flow for the game with these phases and the 'show' and 'prize' additionally at the end.
</utter>

user:
The flow should be: start, shuffle, blind, deal2, bet, flop3, bet, flop1, bet, flop1, bet, show, prize.

assistant:
<script>
script['Flow'] = ['start', 'shuffle', 'blind', 'deal2', 'bet', 'flop3', 'bet', 'flop1', 'bet', 'flop1', 'bet', 'show', 'prize']
</script>
<code>
def set_flow(self):
    self.flow = ['start', 'shuffle', 'blind', 'deal2', 'bet', 'flop3', 'bet', 'flop1', 'bet', 'flop1', 'bet', 'show', 'prize']
</code>
<utter>
Excellent! The flow is set up, everything is ready. You have successfully created a new game!
</utter>

Reviewer paper summaries: This paper introduces Chat Game Engine (ChatGE) for creating custom games by natural language interaction with LLMs. ChatGE requires users to specify game requirements step by step, with the model generating both a script and corresponding code snippets at each turn. The authors propose a data synthesis pipeline to automatically produce script–code pairs from a few manually crafted examples, and a three-stage progressive training method (Base, Core, Alignment) to smoothly adapt a generic dialogue-based model into ChatGE. Authors validate the system on poker-based card games, evaluating both interaction quality and code correctness.

This paper proposes a game-generation pipeline which allows users to generate their custom games by interacting with an LLM game engine ChatGE. The authors trained ChatGE with synthesized data and showed that it can be used in generating Poker games.

This paper suggests a solution for developing game engines using LLMs. The authors made an LLM to generate script, code, and utterances that can develop a game environment given as a user's description. Also, the paper demonstrates a way to develop a kind of Poker game using their system.

The contribution of this paper:

- Suggesting the concept of using LLMs for game engine development and propose a working model for that.
- Demonstrating the effectiveness of their methods through various ways.
- Demonstration of the development procedure.
Summary of strengths: 1. Interesting framework to help people with few coding experience to develop games.
2. Robust eval and analysis on poker game, including human study.

1. The proposed pipeline for game design can be useful in accelerating game development.

2. There are solid experiment results to show the effectiveness of the method given the constraint that the game is a Poker game.

3. The paper is well structured and written.

- Suggesting the concept of using LLMs for game engine development.
- They demonstrated the development procedure with a poker game.
- They showed high performance with sufficient ablation studies to assess the effect of their synthetic dataset, system usability, etc.
Summary of weaknesses: 1. Only one game: Only show results for poker-based card games; as the author states: " it is still challenging to generalize ChatGE to all games or all game engine". 
2. Look like huge efforts for generalizing to another game, no guidelines for how to do it. If it is too challenging, then this paper/pipeline will only be used as "Poker game Development as Human-LLM Interaction.
3. The validation protocol is only designed for poker game, any guidelines for other games?

1. The major concern is to what extend can this method the generalize. For new games in topics other than Poker games, new training data may be needed for model finetuning. This makes the proposed approach difficult to scale and generalize.

2. Though the system is designed for human-in-the-loop game design, only an LLM agent is used to mimic human interactions. However, for game design, human users may have different levels of knowledge and may interact quite differently with the system. An expert coder may point out code errors directly, while a user who knows nothing about coding and game design may not be able to describe his/her needs clearly. It is worth studying how real humans interacts with this system.

- As a proof of concept, it is questionable whether the demonstration could be applicable to games other than poker-related ones. (I know this one is already included in the limitation section.)
Comments, suggestions and typos: 1. What is the guideline for people to reuse the code for other games?

At the end of Line 414 a space seems to be missing after "quality."

Thanks for addressing my concerns in the previous round. The paper successfully addresses all of my concerns, either in new experimental settings or updated descriptions of limitations.
Overall assessment scores: 3

4

3.5