Recent research has revealed undesirable biases in NLP data and models. However, these efforts focus of social disparities in West, and are not directly portable to other geo-cultural contexts. In this paper, we focus on NLP fairness in the context of India. We start with a brief account of prominent axes of social disparities in India. We build resources for fairness evaluation in the Indian context and use them to demonstrate prediction biases along some of the axes. We then delve deeper into social stereotypes for Region and Religion, demonstrating its prevalence in corpora and models. Finally, we outline a holistic research agenda to re-contextualize NLP fairness research for the Indian context, accounting for Indian societal context, bridging technological gaps in NLP capabilities and resources, and adapting to Indian cultural values. While we focus on 'India', this framework can be generalized to other geo-cultural contexts.
Background: Solanum nigrum (S.nigrum) a medicinal herb is widely used in the Indian system of medicine for treatment of various ailments. The methanolic extract of S.nigrum berries had shown cardio protective and antioxidant effect. However, so far aqueous extract of S.nigrum is not scientifically evaluated for its cardio protective potential. Hence the present study was designed to find out cardio protective role of S.nigrum against doxorubicin induced cardiotoxicity. Methods: Seventy two rats were randomized into four major groups (n=6). group I received 2 ml/100 g/day normal saline p.o daily, group II received 2 ml/100 g/day of normal saline p.o daily, group III received carvedilol 30 mg/kg/day p.o daily and group IV received S.nigrum 1 g/kg/day p.o daily for test durations of 20, 30 and 40 days respectively. Doxorubicin 20 mg/kg i.p single dose was given to induce cardiotoxicity in rats of group II, III and IV respectively on last day of each experiment. Animals were sacrificed 48 hours after doxorubicin administration. Cardiac serum markers creatinine phosphokinase MB, lactate dehydrogenase, serum glutamate oxaloacetate transaminase and serum glutamate pyruvate transaminase were analysed biochemically. Histopathological changes were studied under light microscope. Results: All cardiac serum marker levels were found significantly (p<0.001) increased in doxorubicin group while S.nigrum pretreated group displayed significant (p<0.001) reduction in rise of these parameters in a time dependent manner indicating cardio protection. Histological observations further correlated the cardio protective effect of S.nigrum. Conclusions: The present study concluded that aqueous extract of S.nigrum possess cardio protective potential against doxorubicin induced cardiotoxicity.
Background: Renal diseases are common now days because of multiple nephrotoxic drugs use like aminoglycosides, analgesic etc. Many diseases like Diabetes and Hypertension also contributing to renal diseases. One of the mechanisms for nephrotoxicity is production of free radicals. The phytochemicals obtained from some plants are claimed to be useful in prevention of nephrotoxicity. One of the good sources of these phytochemicals is leaves of Aegle marmelos (Bael) which has antioxidant property that can be useful in nephroprotection. Hence this study was designed to investigate the nephroprotective as well as nephrocurative potential of Aegle marmelos.Methods: Study was done on albino rats at LLRM Medical College as per CPCSEA guidelines after obtaining permission from IAEC. Nephrotoxicity was induced using injection gentamicin(40mg/kg). The nephroprotective and nephrocurative effect was quantified using serum markers (BUN, Serum creatinine) and histopathological changes. Statistical analysis was done using ANOVA followed by post hoc dunnet’s test.Results: When compared with gentamicin induced nephrotoxicity, rats those who received aqueous extract of Aegle marmelos leaves showed significant (p<.001) reduction in nephrotoxicity.Conclusions: It can be concluded from this study that leaves of Aegle marmelos possess siginificant nephroprotective activity.
Deep Contextual Language Models (LMs) like ELMO, BERT, and their successors dominate the landscape of Natural Language Processing due to their ability to scale across multiple tasks rapidly by pre-training a single model, followed by task-specific fine-tuning. Furthermore, multilingual versions of such models like XLM-R and mBERT have given promising results in zero-shot cross-lingual transfer, potentially enabling NLP applications in many under-served and under-resourced languages. Due to this initial success, pre-trained models are being used as 'Universal Language Models' as the starting point across diverse tasks, domains, and languages. This work explores the notion of 'Universality' by identifying seven dimensions across which a universal model should be able to scale, that is, perform equally well or reasonably well, to be useful across diverse settings. We outline the current theoretical and empirical results that support model performance across these dimensions, along with extensions that may help address some of their current limitations. Through this survey, we lay the foundation for understanding the capabilities and limitations of massive contextual language models and help discern research gaps and directions for future work to make these LMs inclusive and fair to diverse applications, users, and linguistic phenomena.1 Throughout the rest of the paper -"these models", "LMs", "general domain LMs", "contextual LMs", "universal LMs" and all such terms refers to models including but not limited to ELMo, BERT, RoBERTa, GPT their variants, successors and multilingual versions
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