Translation is a fundamentally value-laden process that requires the translator to make decisions and judgments that have ethical implications. However, even though large language models (LLMs) are increasingly used for translation tasks, LLMs have not been systematically examined for their default ethical tendencies or their abilities to employ and prioritize specified ethical approaches in conflicted translation situations. To address this gap, we present ETHICA-MT, a framework for examining ethical reasoning and implementation in LLM-based machine translation. Drawing on diverse ethical approaches from the translation studies literature, we formalize a conceptual framework and construct a multilingual benchmark, ETHICA-MT BENCH, that covers six languages and highlights ethical conflicts arising from competing ethical approaches in a variety of translation scenarios. Our empirical study shows that current models predominantly default to an ethical stance favoring ‘faithful representation’ to the source text, and vary in their ability to implement specified ethics at the expense of others. Finally, we highlight the basic challenges of automatically and manually evaluating the models’ ethical stances.

The pervasive influence of social biases in language data has sparked the need for benchmark datasets that capture and evaluate these biases in Large Language Models (LLMs). Existing efforts predominantly focus on English language and the Western context, leaving a void for a reliable dataset that encapsulates India’s unique socio-cultural nuances. To bridge this gap, we introduce IndiBias, a comprehensive benchmarking dataset designed specifically for evaluating social biases in the Indian context. We filter and translate the existing CrowS-Pairs dataset to create a benchmark dataset suited to the Indian context in Hindi language. Additionally, we leverage LLMs including ChatGPT and InstructGPT to augment our dataset with diverse societal biases and stereotypes prevalent in India. The included bias dimensions encompass gender, religion, caste, age, region, physical appearance, and occupation. We also build a resource to address intersectional biases along three intersectional dimensions. Our dataset contains 800 sentence pairs and 300 tuples for bias measurement across different demographics. The dataset is available in English and Hindi, providing a size comparable to existing benchmark datasets. Furthermore, using IndiBias we compare ten different language models on multiple bias measurement metrics. We observed that the language models exhibit more bias across a majority of the intersectional groups. All the scripts utilized and datasets created in this study are publicly available.