相关论文: IMLJD: A Computational Dataset for Indian Matrimon…
Domestic violence is commonly viewed as a gendered issue that primarily affects women, which tends to leave male victims largely overlooked. This study presents a novel, data-driven analysis of male domestic violence (MDV) in Bangladesh,…
The conflict between Israel and Palestinians significantly escalated after the October 7, 2023 Hamas attack, capturing global attention. To understand the public discourse on this conflict, we present a meticulously compiled…
Large language models (LLMs) are entering legal workflows, yet we lack a jurisdiction-specific framework to assess their baseline competence therein. We use India's public legal examinations as a transparent proxy. Our multi-year benchmark…
The study carries out predictive modeling based on publicly available COVID-19 data for the duration 01 April to 20 June 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat, and Rajasthan…
In formal procedure of civil cases, the textual materials provided by different parties describe the development process of the cases. It is a difficult but necessary task to extract the key information for the cases from these textual…
Protection of human rights is one of the most important problems of our world. In this paper, our aim is to provide a dataset which covers one of the most significant human rights contradiction in recent months affected the whole world,…
We present LEGAL-UQA, the first Urdu legal question-answering dataset derived from Pakistan's constitution. This parallel English-Urdu dataset includes 619 question-answer pairs, each with corresponding legal article contexts, addressing…
Online web communities often face bans for violating platform policies, encouraging their migration to alternative platforms. This migration, however, can result in increased toxicity and unforeseen consequences on the new platform. In…
Most legal text in the Indian judiciary is written in complex English due to historical reasons. However, only a small fraction of the Indian population is comfortable in reading English. Hence legal text needs to be made available in…
Data on marriage flows are not available in most developing countries, making marriage market imbalance difficult to measure. Existing measures use crude fertility rates and do not account for early-life mortality, overstating the number of…
The rapid advancement of artificial intelligence in legal natural language processing demands scalable methods for evaluating text extraction from judicial decisions. This study evaluates 16 unsupervised metrics, including novel…
Mathematical ability is among the most important determinants of prospering in the labour market. Using multiple representative datasets with learning outcomes of over 2 million children from rural India in the age group 8 to 16 years, the…
Automated Fact-Checking has largely focused on verifying general knowledge against static corpora, overlooking high-stakes domains like law where truth is evolving and technically complex. We introduce CaseFacts, a benchmark for verifying…
We present the Massive Legal Embedding Benchmark (MLEB), the largest, most diverse, and most comprehensive open-source benchmark for legal information retrieval to date. MLEB consists of ten expert-annotated datasets spanning multiple…
We investigate the impact of judges' gender on the outcome of domestic violence cases. Using data from S\~ao Paulo, Brazil, between 2011 and 2019, we find that a domestic violence case assigned to a female judge is 28% (9.7 p.p.) more…
In a legal system, judgment consistency is regarded as one of the most important manifestations of fairness. However, due to the complexity of factual elements that impact sentencing in real-world scenarios, few works have been done on…
This paper examines monthly matching efficiency in the Japanese marriage market using novel data from IBJ, the country's largest structured matching platform. Unlike administrative or dating app data, IBJ provides full search, dating, and…
The adoption of Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks. We identify two fundamental types of inconsistencies: (1) Score-Comparison…
Evaluating Large Language Models (LLMs) for mental health support is challenging due to the emotionally and cognitively complex nature of therapeutic dialogue. Existing benchmarks are limited in scale, reliability, often relying on…
Vision-Language Models (VLMs) are known to inherit and amplify societal biases from their web-scale training data with Indian being particularly misrepresented. Existing fairness-aware datasets have significantly improved demographic…