Curse of Multilinguality in NLP

• The curse of multilinguality is a phenomenon in NLP where a fixed model capacity is split among many languages, leading to degraded performance per language.
• Architectural innovations like modular adapters and expert ensembles mitigate interference by allocating specialized capacity to language clusters.
• Empirical evidence highlights that balanced data curation and targeted training regimes are essential to preserve linguistic diversity and optimize cross-lingual transfer.

The curse of multilinguality refers to the central challenge in NLP and multilingual LLMing where supporting an increasing number of languages within a single model or system leads to degraded performance for individual languages, especially under fixed model capacity constraints. This phenomenon encompasses not only empirical trade-offs in representation capacity and data balancing but also manifests as negative cross-lingual interference, representational dilution, semantic misalignment, and a tendency toward linguistic homogenization. Addressing these limitations has prompted the development of architectural innovations, novel training paradigms, and evaluation protocols in response to both technical and linguistic requirements.

1. Theoretical Foundations and Capacity Dilution

The core of the curse of multilinguality lies in the allocation of a model’s finite representational capacity across a growing set of languages. The effective per-language capacity can be conceptualized as:

$C_\text{lang} = \frac{C_\text{model}}{L}$

where $C_\text{model}$ is the total capacity (e.g., parameter count) and $L$ is the number of supported languages (Gurgurov et al., 15 Jun 2024). As $L$ increases, $C_\text{lang}$ diminishes, resulting in reduced ability to model idiosyncratic linguistic and semantic phenomena. Empirical studies have demonstrated that while the introduction of some multilingual data can facilitate improvements in low-resource settings (analogous to a fractional effective increase in data quantity), excessive multilingual scaling leads to deterioration in both low- and high-resource languages once the model becomes overextended (Chang et al., 2023). The impact is particularly pronounced in smaller models, with high-resource languages experiencing losses equivalent to an 85% reduction in monolingual data size when subjected to massive multilingual regimes.

2. Architectural and Training Strategies for Mitigation

Several lines of architectural innovation have emerged to counteract the negative effects of sharing model parameters across languages:

• Modular and Expert-based Architectures: X-MOD and dynamic mixture-of-experts (DMoE) frameworks extend transformers by incorporating language-specific modules or expert groups at selected layers. In X-MOD, a bottleneck feed-forward layer is added per language at each layer, allowing for both parameter sharing and language-specific capacity (Pfeiffer et al., 2022, Gurgurov et al., 15 Jun 2024). DMoE dynamically identifies layers exhibiting high language-specific parameter deviation and replaces them with MoE layers, where each expert specializes in a cluster of similar languages and a router module assigns tokens accordingly (Li et al., 14 Jun 2025). This approach mitigates negative transfer and enables efficient adaptation to new languages via expert fine-tuning.
• Expert Ensembles and Branch-Train-Merge Paradigms: Cross-lingual expert LLMs (X-ELM) eschew uniform parameter sharing by training separate experts on language clusters determined by linguistic typology or data-driven clustering, later merging them as an ensemble with soft routing during inference (Blevins et al., 19 Jan 2024). This framework ensures that high-resource and typologically distant languages do not overwhelm low-resource ones, and allows for iterative addition of new languages without catastrophic forgetting.
• Adapter-based and Embedding-Augmentation Methods: Lightweight adapters or language-specific embedding layers inserted within shared architectures allow for efficient per-language specialization. New training regimes based on subword unit mapping, transliteration, and specialized tokenizers further aid the inclusion of low-resource or previously unseen languages (Gurgurov et al., 15 Jun 2024).

3. Cross-Lingual Knowledge Transfer and Language Selection

Empirical evidence demonstrates that careful design of multilingual models can exploit positive transfer, particularly within related language families. In neural machine translation (NMT), many-to-many and pivot-based systems for the Slavic language family showcase that cross-lingual benefits (including robust zero-shot transfer) are observable even without direct parallel data for every pair (Kot et al., 20 Feb 2025). Here, carefully constructed language tags (e.g., >>ces<< for Czech) and shared transformers, coupled with balanced tokenizers (e.g., via SentencePiece), maximize positive transfer.

Agglutinative languages, due to their rich morphological variability and free word order, also disproportionately improve cross-lingual transfer performance when included in training (Kim et al., 2022). This suggests that typologically diverse or morphologically rich languages introduce beneficial structural variance, facilitating more abstract, transferable semantic representations.

4. Interference, Biases, and Semantic Alignment

Despite the promise of parameter and data sharing, multilingual models often exhibit bias and interference:

• Grammatical Structure Transfer: Multilingual models, particularly those heavily pre-trained on English, exhibit “grammatical structure bias.” For example, mBERT consistently favors English-like explicit pronouns in Spanish and subject-verb-object ordering in Greek, even against the native grammar of those languages (Papadimitriou et al., 2022). This bias arises due to imbalanced training data, forced parameter sharing, and, at times, non-representative pretraining corpora (e.g., translationese instead of native text).
• Cultural Value Bleeding and Semantic Misalignment: Shared parameter spaces can lead to cultural and conceptual values bleeding across languages during both pretraining and fine-tuning (Choenni et al., 21 May 2024, Mizumoto et al., 1 Mar 2025). Fine-tuning on data from a particular language or domain can shift the encoded cultural profiles for all languages, as measured by vector-based alignment with survey-based “ground-truth” human values. Structural conflicts also appear regarding alignment with cross-linguistic consistency (CL-consistency) versus fidelity to folk judgments (Folk-consistency), raising complex philosophical, epistemic, and normative considerations for both model developers and users.
• Model Collapse and Loss of Linguistic Diversity: Prolonged training on model-generated outputs (self-consuming loops) induces model collapse, further amplifying dominant and high-probability forms while pruning rare and culturally distinctive forms from the LLM distribution (Vanmassenhove, 5 Jul 2025). This collapse threatens both grammatical precision and the expressive capacity of low-resource and minority languages.

5. Empirical Observations and Data Regime Effects

Large-scale multilingual modeling studies indicate that multilinguality is not universally beneficial and is highly sensitive to pretraining and fine-tuning regimes:

• Data Quantity vs. Multilinguality: Analyses reveal that improvements attributed to multilingual training frequently conflate the confounding effects of increased training data size as opposed to genuine language diversity effects. When the amount of fine-tuning data is controlled, monolingual and multilingual models often converge in performance, and the supposed advantages of multilinguality dissipate (Goworek et al., 30 May 2025).
• Role of Syntactic and Geographic Similarity: Targeted addition of syntactically related languages produces more substantial transfer and less negative interference than adding arbitrary or unrelated data (Chang et al., 2023).
• In-Context Learning (ICL) and Prompt Design: Recent findings indicate that using mixed high-resource language demonstrations in prompts produces greater improvements on low-resource languages, even when the context is irrelevant to the target task. This demonstrates that strategic exploitation of multilingual cues can “bless” rather than curse a model, contingent upon prompt design and task context (Tu et al., 17 Feb 2025).

6. Practical Implications and Applications

The curse of multilinguality has wide-reaching practical implications across NLP applications:

• Machine Translation: While universal multilingual NMT systems enable parameter reuse and economy, they risk global performance deficits unless substantial capacity scaling or modularization is adopted (Kocmi et al., 2022). Within language families, multilingual systems outperform bilingual baselines even in zero-shot translation scenarios if carefully designed (Kot et al., 20 Feb 2025).
• Code-Mixed and Societal NLP: Handling code-mixed text requires dedicated models and tailored tools due to increased linguistic variation, spelling inconsistencies, and lack of robust annotation and evaluation protocols (Srivastava et al., 2021). General-purpose multilingual models underperform on such mixed data, amplifying the need for context-sensitive system design.
• Low-Resource and Typologically Diverse Languages: The most stable performance improvements arise from carefully curated training distributions that reflect syntactic or morphological similarity, use of modularized model architectures, and creative data composition strategies (e.g., targeted oversampling, synthetic data generation) (Luukkonen et al., 2 Apr 2024).
• Fairness, Cultural Alignment, and Evaluation: Addressing the curse necessitates rethinking evaluation metrics beyond traditional BLEU or classification accuracy, as these miss subtle but critical shifts in language structure, cultural knowledge, and expressive diversity.

7. Future Directions and Ongoing Challenges

Mitigating the curse of multilinguality remains an open area of research. Promising directions include:

• Further Modularization and Specialization: Continued refinement of mixture-of-experts strategies, dynamic expert allocation, and modular adapters, with adaptive mechanisms for expansion and language-specific specialization (Li et al., 14 Jun 2025, Blevins et al., 19 Jan 2024).
• Data Curation and Evaluation Protocols: Development of balanced corpora, inclusion of low-frequency linguistic forms, and evaluation metrics attuned to diversity, bias, and cultural fidelity (Vanmassenhove, 5 Jul 2025).
• Language Family-aware and Typology-sensitive Modeling: Leveraging linguistic typology to guide architecture and data selection, enabling positive transfer without extensive negative interference (Kim et al., 2022, Kot et al., 20 Feb 2025).
• Cross-disciplinary Insights: Multidisciplinary approaches drawing from linguistics, philosophy, and cognitive science are critical for resolving normative and conceptual misalignments, particularly in light of cross-linguistic knowledge barriers and alignment conflicts (Mizumoto et al., 1 Mar 2025).
• Scalable and Incremental Learning: Asynchronous and scalable training protocols (e.g., Branch-Train-Merge) that allow for democratized model adaptation and equitable capacity distribution across languages (Blevins et al., 19 Jan 2024).

In summary, the curse of multilinguality arises from intrinsic trade-offs in shared representation, data balancing, and the management of linguistic and cultural diversity within LLMs. Addressing this phenomenon demands flexible model architectures, principled data strategies, and multidimensional evaluation to support scalable, fair, and expressive multilingual NLP systems.