Sandip soparkar wiki biography of rory

Abstract

Large language models (LLMs) have been shown to propagate and even amplify gender bias, in English and other languages, in specific or constrained contexts. However, no studies so far have focused on gender biases conveyed by LLMs’ responses to generic instructions, especially with regard to masculine generics (MG). MG are a linguistic feature found in many gender-marked languages, denoting the use of the masculine gender as a “default” or supposedly neutral gender to refer to mixed group of men and women, or of a person whose gender is irrelevant or unknown. Numerous psycholinguistics studies have shown that MG are not neutral and induce gender bias. This work aims to analyze the use of MG by both proprietary and local LLMs in responses to generic instructions a

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Towards Geo-Culturally Grounded LLM Generations

Piyawat Lertvittayakumjorn^†, David Kinney^⋆†‡,
Vinodkumar Prabhakaran^†, Donald Martin^†, Sunipa Dev<sup>†

†</sup>Google  ^‡Washington University in St. Louis
{piyawat,vinodkpg,dxm,sunipadev}@google.com, kinney@wustl.edu

Abstract

Generative large language models (LLMs) have been demonstrated to have gaps in diverse, cultural knowledge across the globe. We investigate the effect of retrieval augmented generation and search-grounding techniques on the ability of LLMs to display familiarity with a diverse range of national cultures. Specifically, we compare the performance of standard LLMs, LLMs augmented with retrievals from a bespoke knowledge base (i.e., KB grounding), and LLMs augmented with retrievals from a web search (i.e., search grounding) on a series of cultural familiarity benchmarks. We find that search grounding significantly improves the LLM performance on multiple-choice benchmarks that test propositional knowledge (