Discourse-level translation remains a major challenge for machine translation (MT) systems, particularly for translation from Indian languages to English. This difficulty arises due to factors such as rich morphology, diverse syntactic structures, unmarked gender distinctions in pronouns, and the limited availability of discourse-aware training data. Existing evaluation benchmarks primarily focus on sentence-level translation quality and fail to capture important discourse phenomena such as pronoun resolution and lexical cohesion. To address this gap, we introduce IndicDISCO-MT, a parallel benchmark dataset covering translations from eight Indian languages such as Bengali, Gujarati, Hindi, Marathi, Kannada, Tamil, Telugu and Urdu to English. On top of this dataset, we are first to introduce DiscoAlign, a human annotated word to word alignment benchmark dataset, that captures correspondences between source and target words across languages. In addition, we propose two evaluation benchmarks, ProAlign and LexiAlign, designed to specifically assess the ability of large language models (LLMs) and MT systems to handle personal pronouns and lexical cohesion. Our evaluation of recent LLMs and MT systems on these benchmarks shows that although models achieve high overall translation quality, they still struggle to accurately preserve discourse-level phenomena. The proposed benchmarks provide a systematic framework for evaluating discourse aware translation and can facilitate the development of MT systems that generate more coherent and contextually consistent translations.

Despite negation being one of the core element in any language, it remains a challenging phenomenon for modern Large Language Models(LLMs). Recently, there have been growing efforts to evaluate how models handle negation. However, the existing probing datasets are mostly English-centric. To facilitate evaluation for Indian Languages especially Telugu, which has complex morphological features, we present NEGTEG benchmark. This benchmark is a test suite that contains 5 tasks: Negation Detection, Negation Translation, Paraphrase Detection, Sentiment Analysis and Polarity Flipping. The test suite is designed based on strong linguistic analysis and includes annotations of different negation types. This helps us evaluate how models perform across various forms of negation. We use the benchmark to probe the negation handling capabilities of multilingual language models at different levels and our evaluation reveals that most of the models struggle significantly with Telugu negation across all tasks.