Tootfinder

Culturally-Aware Conversations: A Framework & Benchmark for LLMs
Shreya Havaldar, Sunny Rai, Young-Min Cho, Lyle Ungar
https://arxiv.org/abs/2510.11563 https://

Culturally-Aware Conversations: A Framework & Benchmark for LLMs
Existing benchmarks that measure cultural adaptation in LLMs are misaligned with the actual challenges these models face when interacting with users from diverse cultural backgrounds. In this work, we introduce the first framework and benchmark designed to evaluate LLMs in realistic, multicultural conversational settings. Grounded in sociocultural theory, our framework formalizes how linguistic style - a key element of cultural communication - is shaped by situational, relational, and cultural …

A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
Olalekan K. Akindele, Bhupesh Kumar Mishra, Kenneth Y. Wertheim
https://arxiv.org/abs/2509.19209

A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
Large Language Models (LLMs) have significantly enhanced conversational Artificial Intelligence(AI) chatbots; however, domain-specific accuracy and the avoidance of factual inconsistencies remain pressing challenges, particularly for large datasets. Designing an effective chatbot with appropriate methods and evaluating its effectiveness is among the challenges in this domain. This study presents a Retrieval Augmented Generation (RAG) chatbot that harnesses a knowledge graph and vector search re…