The growing prominence of large language models (LLMs) necessitates the
exploration of their capabilities beyond English. This research investigates
the Telugu language proficiency of ChatGPT and Gemini, two leading LLMs.
Through a designed set of 20 questions encompassing greetings, grammar,
vocabulary, common phrases, task completion, and situational reasoning, the
study delves into their strengths and weaknesses in handling Telugu. The
analysis aims to identify the LLM that demonstrates a deeper understanding of
Telugu grammatical structures, possesses a broader vocabulary, and exhibits
superior performance in tasks like writing and reasoning. By comparing their
ability to comprehend and use everyday Telugu expressions, the research sheds
light on their suitability for real-world language interaction. Furthermore,
the evaluation of adaptability and reasoning capabilities provides insights
into how each LLM leverages Telugu to respond to dynamic situations. This
comparative analysis contributes to the ongoing discussion on multilingual
capabilities in AI and paves the way for future research in developing LLMs
that can seamlessly integrate with Telugu-speaking communities.

通过比较大型语言模型在特鲁古语中的能力，研究旨在找出能够更深入理解特鲁古语法结构、具备更广泛词汇量、在写作和推理等任务中表现出色的语言模型，以及其适用于真实语言交互的能力，并评估其适应性和推理能力，为多语言 AI 能力的讨论提供见解，为开发能够无缝融入特鲁古语社区的大型语言模型铺平道路。

大型语言模型对泰卢固语熟练度的评估：ChatGPT 与 Gemini 的比较分析

Evaluating Telugu Proficiency in Large Language Models_ A Comparative  Analysis of ChatGPT and Gemini

Devising player-specific strategies in cricket necessitates a meticulous
understanding of each player's unique strengths and weaknesses. Nevertheless,
the absence of a definitive computational approach to extract such insights
from cricket players poses a significant challenge. This paper seeks to address
this gap by establishing computational models designed to extract the rules
governing player strengths and weaknesses, thereby facilitating the development
of tailored strategies for individual players. The complexity of this endeavor
lies in several key areas: the selection of a suitable dataset, the precise
definition of strength and weakness rules, the identification of an appropriate
learning algorithm, and the validation of the derived rules. To tackle these
challenges, we propose the utilization of unstructured data, specifically
cricket text commentary, as a valuable resource for constructing comprehensive
strength and weakness rules for cricket players. We also introduce
computationally feasible definitions for the construction of these rules, and
present a dimensionality reduction technique for the rule-building process. In
order to showcase the practicality of this approach, we conduct an in-depth
analysis of cricket player strengths and weaknesses using a vast corpus of more
than one million text commentaries. Furthermore, we validate the constructed
rules through two distinct methodologies: intrinsic and extrinsic. The outcomes
of this research are made openly accessible, including the collected data,
source code, and results for over 250 cricket players, which can be accessed at
this https URL

利用非结构化的板球文字评论作为构建全面的板球运动员强项和弱项规则的宝贵资源，本研究建立了计算模型来提取控制板球运动员强项和弱项的规则，为个别选手的策略开发提供便利。

板球运动员个人资料建模：使用文本解说数据揭示优势和劣势

Cricket Player Profiling: Unraveling Strengths and Weaknesses Using Text  Commentary Data

We examine how much of the contemporary progress in artificial intelligence
(and, specifically, in natural language processing), can be, more or less
directly, traced back to the seminal work and ideas of the Austrian-British
philosopher Ludwig Wittgenstein, with particular focus on his late views.
Discussing Wittgenstein's original theses will give us the chance to survey the
state of artificial intelligence, and comment on both its strengths and
weaknesses. A similar text appeared first in Spanish as a chapter of CENTENARIO
DEL SILENCIO (2021), a book celebrating 100 years since the publication of the
Tractatus.

本文探讨了当代人工智能（特别是自然语言处理）进展在多大程度上直接追溯到奥地利 - 英国哲学家路德维希・维特根斯坦的开创性工作和思想，着重关注他的后期观点，并讨论了人工智能的优缺点。

维特根斯坦对人工智能的影响

Witgenstein's influence on artificial intelligence

Relation classification models are conventionally evaluated using only a
single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze
weighting schemes, such as micro and macro, for imbalanced datasets. We
introduce a framework for weighting schemes, where existing schemes are
extremes, and two new intermediate schemes. We show that reporting results of
different weighting schemes better highlights strengths and weaknesses of a
model.

本文分析了单一度量方法，探讨了权重方案，提出了新的方案，并表明不同权重方案的结果表明模型的优缺点。