ConlangCrafter: Automated Constructed Language Generator

• ConlangCrafter is a modular, LLM-driven pipeline that creates internally coherent and typologically diverse conlangs without requiring linguistic expertise.
• It employs sequential processes including language sketch bootstrapping, constructive translation, and iterative self-refinement to maintain consistency and creativity.
• By integrating randomness injection with a critic–editor loop, the system balances linguistic diversity and logical rigor for applications in art, worldbuilding, and computational linguistics.

ConlangCrafter is a LLM–driven multi-stage pipeline for automatic constructed language (conlang) generation, designed to produce internally coherent and typologically diverse artificial languages without requiring linguistic expertise. It takes advantage of recent advances in foundation models by decomposing the creative process into modular linguistic layers and employs both randomness and iterative self-refinement to maintain diversity and logical rigor. Below, the architecture, core mechanisms, evaluation, and implications are summarized as established in the associated research (Alper et al., 8 Aug 2025).

1. Multi-Hop Pipeline Architecture

ConlangCrafter’s workflow is characterized by a stateful, multi-hop pipeline that mirrors key components of linguistic description:

• Stage A: Bootstrap Language Sketch
  • The process begins by constructing a “language sketch” S, an evolving document that incrementally specifies the phonology, morphology, syntax, and lexicon.
  • This sketch is composed modularly, with each layer generated and refined before proceeding to the next.
• Stage B: Constructive Translation
  • Once S is established, the system translates new text inputs into the conlang.
  • During translation, if the language sketch lacks required definitions (e.g., missing words or grammatical structures), the system extends S in situ to preserve consistency.

A diagram provided in the original paper details the pipeline: phonology → grammar → lexicon generation → constructive translation (all conditioned on the evolving sketch S).

2. LLM Utilization and Meta-Linguistic Reasoning

At the core of ConlangCrafter is an LLM (denoted M) acting as both generator and critic:

• Each pipeline stage prompts M to execute specific linguistic tasks:
  • Phonology: Generation of plausible inventories and phonotactic rules.
  • Morphology/Syntax: Formulation of inflectional/derivational paradigms and sentence-level rules.
  • Lexicon: Creation of word forms constrained by previously established phonological principles.
  • Translation: Conditioning output on S, and expanding S if underspecification is detected.
• The system draws on M’s meta-linguistic capabilities, instructing it to reason about typological variation, structural dependencies, and cross-linguistic principles.

The process is stateful: every addition to S is made visible to later steps via context conditioning, ensuring cross-stage coherence.

3. Diversity Induction and Self-Refinement

To avoid degeneracy (e.g., repeatedly producing similar language prototypes) and to correct logical errors, two mechanisms are integrated:

• Randomness Injection
  • At key decision points (e.g., phonology, morphological typology), M is prompted to generate a checklist of 10 features, each with 5 typological options. An external RNG selects one value per feature.
  • This methodically forces the system to explore a wide typological search space and prevents convergence on default profile languages.
• Self-Refinement Feedback Loop
  • Once a language sketch is produced, a “critic” (also M) reviews its output for internal inconsistency and logical ambiguity.
  • The critic assigns a consistency rating, and an “editor” (again, M) iterates refinements until a predefined threshold (typically ≥9 on a 1–10 scale) is met.
  • This iterative loop significantly improves internal logical coherence among the different linguistic layers.

Empirical results from the paper show that the initial randomness may lower consistency (rate), which is largely recovered through the self-refinement process.

4. Evaluation Metrics: Diversity and Consistency

The system is quantitatively evaluated on two principal metrics:

• Typological Diversity
  • Each generated conlang Lᵢ is encoded as a one-hot vector xᵢ over k = 16 fundamental features (World Atlas of Language Structures).
  • The average normalized Hamming distance D_mean is calculated:

  $$D_{\mathrm{mean}} = \frac{2}{N(N-1)} \sum_{1 \leq i < j \leq N} \frac{\mathrm{Ham}(\mathbf{x}_i, \mathbf{x}_j)}{k}$$ - A t-SNE visualization demonstrates wide dispersion of generated languages in feature space, outperforming baseline methods in typological coverage.

• Internal Consistency
  • Language Consistency Rate:

  $$\text{Consistency Rate}_{\text{language}} = \frac{N_{c,s}}{N_{t,s}}$$ - Translation Consistency Rate:

  $$\text{Consistency Rate}_{\text{trans}} = \frac{N_{c,t}}{N_{t,t}}$$ - The rates are computed over randomly sampled sentences/translations, each judged for adherence to grammar, lexicon, and phonology within S.

Experimental tables indicate that while randomness increases typological diversity, it may initially depress consistency—a deficit restored by the critic–editor cycle.

5. Constructivist Lexicon and Translation Dynamics

During translation, ConlangCrafter’s generative methodology is “constructive”:

• If a target text requires a grammatical structure or lexical item not yet specified in S, the system incrementally augments S in real-time rather than defaulting to borrowing or leaving gaps.
• This ensures that translations remain maximally consistent with the internal rules, even as the conlang evolves.
• Phonological rules from prior pipeline stages are strictly enforced during lexicon expansion.

A use case involves translating diverse input sentences while dynamically extending the lexicon and grammar to support all semantic and grammatical requirements, conditioning each output on the established sketch S.

6. Applications and Broader Implications

ConlangCrafter supports multiple domains:

• Artistic and Literary Creation: The system enables the procedural generation of “languages” for games, novels, worldbuilding, or philo-artistic experimentation, conditioned on typological constraints or thematic requirements.
• Linguistic Modeling: It provides a testbed for computational exploration of language typology, diversity, and the effects of modular creative processes.
• International Communication and Low-Resource Language Support: The methodology is readily extendable to support hypothetical auxiliary languages or rapid prototyping for linguistically underserved settings.
• Computational Creativity Research: The integration of randomness, self-refinement, and forced typological variation advances the frontier of machine-aided creative generation in linguistics, distinct from unstructured text simulation.

7. Methodological Innovations and Limitations

ConlangCrafter’s distinctive features include:

Mechanism	Role in the System	Benefits/Tradeoffs
Multi-Hop Pipeline	Modular decomposition, staged synthesis	Increased coherence, iterative expansion
LLM Critic-Editor	Self-refinement, ambiguity correction	Restores consistency, boosts logical rigor
Typological RNG	Random feature assignment	Maximizes diversity, mitigates convergence
Constructive Translation	Real-time sketch expansion	Ensures semantic adequacy

While the system demonstrates strong diversity and internal coherence by objective metrics, possible limitations pertain to coverage of rare or highly marked typological profiles (as randomness is still restricted to the model’s priors) and dependency on LLM’s baseline linguistic granularity.

Conclusion

ConlangCrafter is a state-of-the-art system for automated constructed language generation, combining modular LLM-driven synthesis with systematic randomness and critic-based self-refinement. Its evaluation on diversity and consistency metrics validates both typological breadth and logical rigor, situating it as a key methodological contribution to computational creativity, world-building, and linguistic simulation (Alper et al., 8 Aug 2025).