Bhaasha, Bhāṣā, Zaban: A Survey for Low-Resourced Languages in S outh A sia – Current Stage and Challenges (2025)

Abstract: Rapid developments of large language models have revolutionized many NLP tasks for English data. Unfortunately, the models and their evaluations for low-resource languages are being overlooked, especially for languages in South Asia. Although there are more than 650 languages in South Asia, many of them either have very limited computational resources or are missing from existing language models.Thus, a concrete question to be answered is: _Can we assess the current stage and challenges to inform our NLP community and facilitate model developments for South Asian languages?_ In this survey, we have comprehensively examined current efforts and challenges of NLP models for South Asian languages by retrieving studies since 2020, with a focus on transformer-based models, such as BERT, T5, & GPT. We present advances and gaps across 3 essential aspects: data, models, & tasks, such as available data sources, fine-tuning strategies, & domain applications. Our findings highlight substantial issues, including missing data in critical domains (e.g., health), code-mixing, and lack of standardized evaluation benchmarks. Our survey aims to raise awareness within the NLP community for more targeted data curation, unify benchmarks tailored to cultural and linguistic nuances of South Asia, and encourage an equitable representation of South Asian languages. The complete list of resources is available at: [https://github.com/trust-nlp/LM4SouthAsia-Survey](https://github.com/trust-nlp/LM4SouthAsia-Survey).

## introduction south asia is one of the most linguistically diverse regions, encompassing indo-aryan, dravidian, iranian, and tibeto-burman languages, along with numerous isolates @xcite . however, the regional languages are often missing from training corpora or present in imbalanced quantities @xcite , and many of them are not supported by current large language models (llms) @xcite . there are multiple factors behind this disparity, and it's crucial to identify and address them for better representation of south asian languages. the definition of "low-resource" varies based on data availability and digital presence @xcite . we consider a language "low-resource" if it lacks computational data and standardized evaluation benchmarks for nlp tasks. crucially, this framing moves beyond definitions based solely on speaker population, since even widely spoken languages like hindi and bengali remain underresourced in terms of benchmark coverage and model support. while low-resource languages have been studied for various regions @xcite @xcite , there is no comprehensive study on the current status of south asian nlp, which will be fulfilled by this survey outlined in ## data and resources a large text corpus is essential to enable language models to understand complex and heterogeneous semantics and structures of south asian languages. over 650 languages are spoken in the region, yet computational resources remain scarce and highly skewed toward a few languages @xcite @xcite @xcite . for example, most language resources consist of small text samples, with a major focus on languages like hindi and urdu @xcite @xcite . however, existing studies may merely address the questions that will be answered in our study: 1) what are the available corpora for the low-resourced languages in south asia? 2) what nlp tasks are in the corpora? and 3) what domains are the corpora? to answer those questions, we summarize data distributions by language families in figure 1 and statistics in ## model advances we examine recent model advances of south asian languages in ## trends and challenges building on the contributions reviewed in the previous sections, we now synthesize emerging patterns and persisting challenges. data scarcity and quality issues for lowresource languages affect model generalizability and applicability @xcite . existing resources, especially small datasets, are often domain-specific (e.g., government or political) due to limited digital content and copyright restrictions, and may potentially introduce cultural or political biases in downstream applications @xcite @xcite . the lack of gold-annotated resources complicates tasks, such as co-reference resolution @xcite , and the rapidly evolving online discourse hurts model long-term sustainability @xcite . non-standardized transliteration and representation of south asian languages introduce biases as annotators often rely on phonetic judgment @xcite . @xcite noted inconsistencies in language identification and translation quality due to style and dialect differ-ences within translations and translated text, which are common as for missing human re-verification @xcite . also, datasets translated from english to a south asian language can be culturally misaligned @xcite . for culturally nuanced languages @xcite , the requirement for proficient annotators restricts the scalability of data collection efforts. biases from human annotators' varying interpretation and background can harm sensitive tasks like hate speech detection @xcite . further, certain data exhibit class imbalances, leading to bias toward majority classes; solutions such as cost-sensitive learning and oversampling have been proposed @xcite but not examined. languages exhibiting diglossia need additional efforts as literary text cannot be used for tasks in all settings @xcite . limited computing resources further restrict improvements in the curation of high-quality datasets @xcite . transliteration and tokenization inconsistencies reduce generalizability of multilingual models on code-mixed languages, such as hinglish, tanglish, and romanized bengali @xcite . models often learn script-dependent embeddings, which limits cross-script generalization @xcite . for example, transliteration ambiguity can easily affect speech-text alignment in asr models @xcite . existing tokenization strategies such as byte-pair encoding (bpe) @xcite and word-piece @xcite frequently fragment morphologically rich words in dravidian and indo-aryan languages, leading to over-segmentation and loss of meaning @xcite . similarly, agglutinative languages like tamil and manipuri form complex word structures that are inconsistently tokenized, affecting syntactic parsing and nmt @xcite . for extremely lowresource languages, pretrained tokenizers @xcite fail to adapt effectively as they fragment words into multiple sub-word tokens, sometimes even individual characters, introducing noise to tasks like pos tagging. morphological segmentation is particularly challenging for dravidian languages as words are formed by adding multiple suffixes @xcite . hindi, assamese, and bengali exhibit different, complex inflectional systems com-plicating parsing @xcite . most indo-aryan languages rely on dependent vowel signs (matras) and nasalization markers, where bert tokenizers often split them incorrectly @xcite and cause ambiguities @xcite . for instance, the word " " (flower) can be incorrectly tokenized as " " (fruit). assamese possesses unique sound patterns and alveolar stops, showing the tokenization complexity @xcite . besides structural differences, administrative vocabulary include persian-origin words like "farman" (order), alongside english-origin terms @xcite . code mixing, diglossia, and ambiguity are highly domain-dependent issues and can integrate english letters, words, or phrases, such as hinglish/tanglish @xcite . diglossia shows substantial differences in speaking and writing. for example, literary tamil retains its formal vocabulary, but spoken tamil incorporates loanwords and phonetic simplifications @xcite . additionally, polysemy and contextual ambiguities can fail many models on tasks like ner @xcite . for example, indic languages do not typically capitalize proper nouns, making it difficult to distinguish named entities from common words @xcite ; "hindustan" ( ) can refer to a location, a person, or an organization @xcite . many languages are grammatically gendered, even inanimate objects being referred to with gendered pronouns @xcite . dialect variations and continua are common issues in south asian corpus development as most studies consider a single standard variety. recent efforts have started addressing this by creating dialect-specific resources @xcite @xcite . for example, @xcite curated hindialect, a folk-song corpus covering 26 hindi-related dialects; and vacaspati @xcite compiles 115m bengali literature sentences sampled across west bengal and bangladesh to capture regional lexical differences. several studies incorporated dialectal cues into models: ax-omiyaberta @xcite includes phonological signals via an attention network; alam and anastasopoulos (2025) utilized lora @xcite to achieve dialectal normalization and translation across south asian dialects with limited supervision. however, existing studies show that performance is lower on underrepresented dialects compared to common varieties, which reflects biases in data coverage. annotation and orthography for dialectal text are inconsistent--many informal dialects lack standardization and the boundary between "dialect" and "standard" is often arbitrary @xcite . data frequently conflate dialectal variants with the standard language, while current benchmarks rarely consider these variants. most multilingual benchmarks only cover a few dominant languages, so dialectal evaluations are missing. chipsal and recent shared tasks (e.g., nlu of devanagari script languages) have started to address this by building annotated dialectal corpora @xcite . together, these findings show that dialect-specific corpora and evaluation benchmarks are essential to avoid biasing models toward standard varieties. llm alignment and reasoning tasks current llm benchmarks have limited coverage of south asian languages. for example, the mmlu-prox covers 13 languages (e.g., hindi, bengali) but omits many others such as tamil, marathi, and kannada @xcite . even broader tests like global-mmlu span multiple languages ( e.g., hindi, telugu, nepali, etc.) @xcite ), yet these datasets were generated by translating english questions. this leads to cultural mismatch. many mmlu @xcite questions (e.g., us history, law) are western-specific and thus irrelevant in south asia; and the translation introduces artifacts that distort evaluation @xcite . @xcite show that hindi, the most spoken language in the region, is only represented in 5 multilingual reasoning corpora. recent work on cultural and value alignment (culturellm) fine-tunes llms on global survey data; however, such efforts test broad value judgments rather than deep reasoning in vernacular settings @xcite . for example, chiu et al. (2025) covers bangladesh, india, nepal, and pakistan, but the corpus only focuses on trivia/ etiquette and not cultural knowledge in the lowresourced languages spoken in the regions. in practice, south asian languages are severely underrepresented in reasoning and alignment tasks with cultural considerations. standard evaluation benchmarks exist, but gaps have remained in evaluating multilingual models of south asian language options, distributional balances, and nlp task diversities. fine-tuned multilingual models often overfit high-resource regional languages (e.g., hindi), leading to degraded performance on lower-resource languages @xcite . catastrophic forgetting happens when adapting models to new languages or tasks, such as in lora & adapter-based fine-tuning @xcite . phonetic variation across dialects within the same language family (e.g., bengali & assamese) results in inconsistencies in phonemebased word embeddings @xcite . tibeto-burman and austroasiatic evaluation data are almost non-existent and most studies for very lowresourced languages use manually curated datasets @xcite . model evaluation from our collected studies generally rely on english-origin benchmarks in ## conclusion in this study, we provide comprehensive synthesis and analysis of recent nlp advances on lowresourced languages in south asia. our work examines persisting challenges at every stage of resource development--uneven representation in multilingual corpora, model availability, multilingual tuning, and evaluation benchmarks. while a few languages have received more attention, challenges remain in collecting and processing data and adapting models to specific orthographies. moreover, existing evaluation metrics fall short due to a lack of script-and task-specific benchmarks, as well as overlooked sociocultural biases. we present model tuning guidelines that reflect current limitations of south asian nlp, calling for south asian-specific frameworks and script-aware model adaptation. we include our future envisions in appendix a.2. we expect this study can encourage broader participation in advancing further research of low-resource languages in south asia.