Recent advances in Large Language Models (LLMs) have expanded their role in human interaction, yet curiosity -- a central driver of inquiry -- remains underexplored in these systems, particularly across cultural contexts. In this work, we investigate cultural variation in curiosity using Yahoo! Answers, a real-world multi-country dataset spanning diverse topics. We introduce CUEST (CUriosity Evaluation across SocieTies), an evaluation framework that measures human-model alignment in curiosity through linguistic (style), topic preference (content) analysis and grounding insights in social science constructs. Across open- and closed-source models, we find that LLMs flatten cross-cultural diversity, aligning more closely with how curiosity is expressed in Western countries. We then explore fine-tuning strategies to induce curiosity in LLMs, narrowing the human-model alignment gap by up to 50%. Finally, we demonstrate the practical value of curiosity for LLM adaptability across cultures, showing its importance for future NLP research.

As large language models (LLMs) impact a growing number of complex domains, it is becoming increasingly important to have fair, accurate, and rigorous evaluation benchmarks. Evaluating the reasoning skills required for business and financial NLP stands out as a particularly difficult challenge. We introduce BizBench, a new benchmark for evaluating models' ability to reason about realistic financial problems. BizBench comprises 8 quantitative reasoning tasks. Notably, BizBench targets the complex task of question-answering (QA) for structured and unstructured financial data via program synthesis (i.e., code generation). We introduce three diverse financially-themed code-generation tasks from newly collected and augmented QA data. Additionally, we isolate distinct financial reasoning capabilities required to solve these QA tasks: reading comprehension of financial text and tables, which is required to extract correct intermediate values; and understanding domain knowledge (e.g., financial formulas) needed to calculate complex solutions. Collectively, these tasks evaluate a model's financial background knowledge, ability to extract numeric entities from financial documents, and capacity to solve problems with code. We conduct an in-depth evaluation of open-source and commercial LLMs, illustrating that BizBench is a challenging benchmark for quantitative reasoning in the finance and business domain.

Accepted: January 16, 2018 Online Published: January 29, 2018 URL: https://doi.org/10.5539/ass.v14n2p145 Abstract This study examines the two important aspect of latest technology issues in Islamic finance that related to artificial intelligence (AI) and smart contract. AI refers to the ability of machines to understand, think, and learn in a similar way to human beings, indicating the possibility of using computers to simulate human intelligence. Smart contract is a computer code running on top of a block-chain containing a set of rules under which the parties to that smart contract agree to interact with each other. The main objectives of this article are to evaluate the operations of AI and smart contract, to make comparison between the operations of AI and smart contract. This article concludes that AI and smart contract will have a huge impact in future for Islamic Finance industry. Keywords: Artificial intelligence (AI), smart contract, digital banking, Islamic Finance 1. Preliminary Artificial Intelligence (AI) is the intelligence machines that have the ability to think. At this point, Artificial Intelligence (AI) offers rapid advancement in technology that mimic human intelligence. It's believed that intelligence machines associated with human thinking activities such as decision making and problem solving learning. Professor J. McCarthy (1955) established the concept of artificial intelligence (AI) during the first artificial intelligence conference at Dartmouth conferences in year 1956. This evolution confirm by Bogue (2014) who described Artificial intelligence (AI) as an intelligent agent system that takes actions in maximize the chances of success in a particular task. Pan (2016) revealed that AI becomes extremely critical when it applies to the technology. According to research report on artificial intelligence, this market is expected to be worth $16.06 billion by 2022. This market is expected to grow at 62.9% compound annual growth rate (CAGR) from 2016 to 2022 (Research and Markets, 2017). In 25th April 2016 to 27th May 2016, a special report under the subheading "Outlook on Artificial Intelligence in the Enterprise 2016" have been produce by Narrative Science in collaboration with National Business Research Institute. This report deployed an online survey with a total of 235 respondents.