IndicIFEval: A Benchmark for Verifiable Instruction-Following Evaluation in 14 Indic Languages

• URL: http://arxiv.org/abs/2602.22125v1
• Date: Wed, 25 Feb 2026 17:12:37 GMT
• Title: IndicIFEval: A Benchmark for Verifiable Instruction-Following Evaluation in 14 Indic Languages
• Abstract summary: IndicIFEval is a benchmark evaluating constrained generation of LLMs across 14 Indic languages.<n>It comprises around 800 human-verified examples per language spread across two complementary subsets.<n>We conduct a comprehensive evaluation of major open-weight and proprietary models spanning both reasoning and non-reasoning models.
• Score: 27.702988457222116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instruction-following benchmarks remain predominantly English-centric, leaving a critical evaluation gap for the hundreds of millions of Indic language speakers. We introduce IndicIFEval, a benchmark evaluating constrained generation of LLMs across 14 Indic languages using automatically verifiable, rule-based instructions. It comprises around 800 human-verified examples per language spread across two complementary subsets: IndicIFEval-Ground, translated prompts from IFEval (Zhou et al., 2023) carefully localized for Indic contexts, and IndicIFEval-Ground, synthetically generated instructions grounded in native Indic content. We conduct a comprehensive evaluation of major open-weight and proprietary models spanning both reasoning and non-reasoning models. While models maintain strong adherence to formatting constraints, they struggle significantly with lexical and cross-lingual tasks -- and despite progress in high-resource languages, instruction-following across the broader Indic family lags significantly behind English. We release IndicIFEval and its evaluation scripts to support progress on multilingual constrained generation (http://github.com/ai4bharat/IndicIFEval).

Related papers

• On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generation [79.18001266925245]
  LingT2I is a benchmark covering 10 widely used languages with 33K prompts.<n>We conduct a comprehensive cross-lingual analysis, uncovering linguistic inequality and language-dependent trade-offs.
  arXiv  Detail & Related papers  (2026-08-11T14:49:23Z)
• HITSZ's End-To-End Speech Translation Systems Combining Sequence-to-Sequence Auto Speech Recognition Model and Indic Large Language Model for IWSLT 2025 in Indic Track [22.93226769885803]
  We propose an end-to-end system integrating the pre-trained Whisper automated speech recognition (ASR) model with Krutrim, an Indic-specialized large language model (LLM)<n> Experimental results demonstrate that our end-to-end system achieved average BLEU scores of $28.88$ for English-to-Indic directions and $27.86$ for Indic-to-English directions.
  arXiv  Detail & Related papers  (2025-07-25T18:32:14Z)
• XIFBench: Evaluating Large Language Models on Multilingual Instruction Following [59.549015333755186]
  Large Language Models (LLMs) have demonstrated remarkable instruction-following capabilities across various applications.<n>Existing evaluations lack fine-grained constraint analysis across diverse linguistic contexts.<n>We introduce XIFBench, a comprehensive benchmark for evaluating multilingual instruction-following abilities of LLMs.
  arXiv  Detail & Related papers  (2025-03-10T17:07:52Z)
• Analysis of Indic Language Capabilities in LLMs [0.3599866690398789]
  This report evaluates the performance of text-in text-out Large Language Models (LLMs) to understand and generate Indic languages.<n> Hindi is the most widely represented language in models.<n>While model performance roughly correlates with number of speakers for the top five languages, the assessment after that varies.
  arXiv  Detail & Related papers  (2025-01-23T18:49:33Z)
• IndicSentEval: How Effectively do Multilingual Transformer Models encode Linguistic Properties for Indic Languages? [16.53746282142566]
  We investigate encoding capability and robustness for 8 linguistic properties across 13 different perturbations in 6 Indic languages.<n>Our probing analysis of surface, syntactic, and semantic properties reveals that while almost all multilingual models demonstrate consistent encoding performance for English.<n>Indic-specific multilingual models capture linguistic properties in Indic languages better than universal models.
  arXiv  Detail & Related papers  (2024-10-03T15:50:08Z)
• Navigating Text-to-Image Generative Bias across Indic Languages [53.92640848303192]
  This research investigates biases in text-to-image (TTI) models for the Indic languages widely spoken across India. It evaluates and compares the generative performance and cultural relevance of leading TTI models in these languages against their performance in English.
  arXiv  Detail & Related papers  (2024-08-01T04:56:13Z)
• Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages [19.91781398526369]
  We aim to improve the NLU capabilities of Indic languages by making contributions along 3 important axes. Specifically, we curate the largest monolingual corpora, IndicCorp, with 20.9B tokens covering 24 languages from 4 language families. We create a human-supervised benchmark, IndicXTREME, consisting of nine diverse NLU tasks covering 20 languages. Across languages and tasks, IndicXTREME contains a total of 105 evaluation sets, of which 52 are new contributions to the literature.
  arXiv  Detail & Related papers  (2022-12-11T04:45:50Z)
• CLSE: Corpus of Linguistically Significant Entities [58.29901964387952]
  We release a Corpus of Linguistically Significant Entities (CLSE) annotated by experts. CLSE covers 74 different semantic types to support various applications from airline ticketing to video games. We create a linguistically representative NLG evaluation benchmark in three languages: French, Marathi, and Russian.
  arXiv  Detail & Related papers  (2022-11-04T12:56:12Z)
• Multilingual Word Sense Disambiguation with Unified Sense Representation [55.3061179361177]
  We propose building knowledge and supervised-based Multilingual Word Sense Disambiguation (MWSD) systems. We build unified sense representations for multiple languages and address the annotation scarcity problem for MWSD by transferring annotations from rich-sourced languages to poorer ones. Evaluations of SemEval-13 and SemEval-15 datasets demonstrate the effectiveness of our methodology.
  arXiv  Detail & Related papers  (2022-10-14T01:24:03Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• Vy\=akarana: A Colorless Green Benchmark for Syntactic Evaluation in Indic Languages [0.0]
  Indic languages have rich morphosyntax, grammatical genders, free linear word-order, and highly inflectional morphology. We introduce Vy=akarana: a benchmark of gender-balanced Colorless Green sentences in Indic languages for syntactic evaluation of multilingual language models. We use the datasets from the evaluation tasks to probe five multilingual language models of varying architectures for syntax in Indic languages.
  arXiv  Detail & Related papers  (2021-03-01T09:07:58Z)
• Word Sense Disambiguation for 158 Languages using Word Embeddings Only [80.79437083582643]
  Disambiguation of word senses in context is easy for humans, but a major challenge for automatic approaches. We present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings.
  arXiv  Detail & Related papers  (2020-03-14T14:50:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.