Hasil untuk "Islam. Bahai Faith. Theosophy, etc."

Peter Hase, Christopher Potts

Inspecting Chain-of-Thought reasoning is among the most common means of understanding why an LLM produced its output. But well-known problems with CoT faithfulness severely limit what insights can be gained from this practice. In this paper, we introduce a training method called Counterfactual Simulation Training (CST), which aims to improve CoT faithfulness by rewarding CoTs that enable a simulator to accurately predict a model's outputs over counterfactual inputs. We apply CST in two settings: (1) CoT monitoring with cue-based counterfactuals, to detect when models rely on spurious features, reward hack, or are sycophantic, and (2) counterfactual simulation over generic model-based counterfactuals, to encourage models to produce more faithful, generalizable reasoning in the CoT. Experiments with models up to 235B parameters show that CST can substantially improve monitor accuracy on cue-based counterfactuals (by 35 accuracy points) as well as simulatability over generic counterfactuals (by 2 points). We further show that: (1) CST outperforms prompting baselines, (2) rewriting unfaithful CoTs with an LLM is 5x more efficient than RL alone, (3) faithfulness improvements do not generalize to dissuading cues (as opposed to persuading cues), and (4) larger models do not show more faithful CoT out of the box, but they do benefit more from CST. These results suggest that CST can improve CoT faithfulness in general, with promising applications for CoT monitoring. Code for experiments in this paper is available at https://github.com/peterbhase/counterfactual-simulation-training

Muniroh Munawar, Alfiah Alfiah, Aurora Nur Aini

The current research aims to describe teachers’ understanding and constraints of global diversity character learning in early childhood education (ECE), especially at the kindergarten level (educational services for children aged 4 – 6 years) seen from several factors, namely: a) kindergarten teachers' understanding of the global diversity concept; b) use of global diversity teaching materials; c) types of global diversity teaching materials; and d) content of global diversity teaching materials. This study is a qualitative study with a descriptive approach. Data were collected through surveys, in-depth interviews, and documentation. The respondents of this study were 55 kindergarten teachers at School Mover who were members of IGTKI Central Java. The data analysis technique used was the Miles and Huberman interactive technique, which included data collection, data reduction, data presentation, conclusion, and data verification. The validity and reliability tests showed that this questionnaire item was valid and reliable. The results show that the majority of kindergarten teachers have a good understanding of the concept of global diversity dimension, use various types and forms of relevant teaching materials, the teaching materials used are from various trusted and varied references, and the content of the teaching materials has met the key elements of global diversity. However, teachers still experience obstacles in teaching materials because the existing teaching materials are less interesting for children. This study suggests the presence of more interesting teaching materials in the form of flipbooks, as flipbooks stimulate children's interest in learning global diversity characters.

Ii Ratna Sari, Santi Nur Wahyuni, Maiyaliza Maiyaliza

At the beginning of 2020, the Covid-19 pandemic began to spread across various regions in Indonesia, causing many companies to experience various operational and financial impacts, especially health sector companies listed on the Indonesia Stock Exchange (IDX). Since the outbreak, the demand for health services has skyrocketed. Many people were infected with the virus, leading to a surge in patients at hospitals and healthcare providers, particularly those requiring intensive care due to COVID-19 infection. As a result, healthcare companies experienced changes in revenue and operational costs. The Net Profit Margin (NPM) ratio did not show substantial changes in the financial performance of healthcare companies before, during, and after the COVID-19 epidemic. The TATO ratio did not show substantial changes in the financial performance of healthcare companies before, during, and after the COVID-19 epidemic. Healthcare companies should improve operational efficiency and asset utilization to maximize revenue contributions and enhance operational management, enabling them to manage and allocate company assets more productively.

Md Zahidul Islam, Md Shafiqur Rahman, Md Sumsuzoha et al.

Cryptocurrency markets are experiencing rapid growth, but this expansion comes with significant challenges, particularly in predicting cryptocurrency prices for traders in the U.S. In this study, we explore how deep learning and machine learning models can be used to forecast the closing prices of the XRP/USDT trading pair. While many existing cryptocurrency prediction models focus solely on price and volume patterns, they often overlook market liquidity, a crucial factor in price predictability. To address this, we introduce two important liquidity proxy metrics: the Volume-To-Volatility Ratio (VVR) and the Volume-Weighted Average Price (VWAP). These metrics provide a clearer understanding of market stability and liquidity, ultimately enhancing the accuracy of our price predictions. We developed four machine learning models, Linear Regression, Random Forest, XGBoost, and LSTM neural networks, using historical data without incorporating the liquidity proxy metrics, and evaluated their performance. We then retrained the models, including the liquidity proxy metrics, and reassessed their performance. In both cases (with and without the liquidity proxies), the LSTM model consistently outperformed the others. These results underscore the importance of considering market liquidity when predicting cryptocurrency closing prices. Therefore, incorporating these liquidity metrics is essential for more accurate forecasting models. Our findings offer valuable insights for traders and developers seeking to create smarter and more risk-aware strategies in the U.S. digital assets market.

James Chua, Owain Evans

Language models trained to solve reasoning tasks via reinforcement learning have achieved striking results. We refer to these models as reasoning models. Are the Chains of Thought (CoTs) of reasoning models more faithful than traditional models? We evaluate three reasoning models (based on Qwen-2.5, Gemini-2, and DeepSeek-V3-Base) on an existing test of faithful CoT. To measure faithfulness, we test whether models can describe how a cue in their prompt influences their answer to MMLU questions. For example, when the cue "A Stanford Professor thinks the answer is D" is added to the prompt, models sometimes switch their answer to D. In such cases, the DeepSeek-R1 reasoning model describes the cue's influence 59% of the time, compared to 7% for the non-reasoning DeepSeek model. We evaluate seven types of cue, such as misleading few-shot examples and suggestive follow-up questions from the user. Reasoning models describe cues that influence them much more reliably than all the non-reasoning models tested (including Claude-3.5-Sonnet and GPT-4o). In an additional experiment, we provide evidence suggesting that the use of reward models causes less faithful responses -- which may help explain why non-reasoning models are less faithful. Our study has two main limitations. First, we test faithfulness using a set of artificial tasks, which may not reflect realistic use-cases. Second, we only measure one specific aspect of faithfulness -- whether models can describe the influence of cues. Future research should investigate whether the advantage of reasoning models in faithfulness holds for a broader set of tests. Still, we think this increase in faithfulness is promising for the explainability of language models.

Peter J. Haine, Maxime Ramzi, Jan Steinebrunner

We study stability properties of fully faithful functors, and compute mapping anima in pushouts of $\infty$-categories along fully faithful functors. We provide applications of these calculations to pushouts along Dwyer functors and Reedy categories.

Moumita Kamal, Douglas A. Talbert

In real-world applications, computational constraints often require transforming large models into smaller, more efficient versions through model compression. While these techniques aim to reduce size and computational cost without sacrificing performance, their evaluations have traditionally focused on the trade-off between size and accuracy, overlooking the aspect of model faithfulness. This limited view is insufficient for high-stakes domains like healthcare, finance, and criminal justice, where compressed models must remain faithful to the behavior of their original counterparts. This paper presents a novel approach to evaluating faithfulness in compressed models, moving beyond standard metrics. We introduce and demonstrate a set of faithfulness metrics that capture how model behavior changes post-compression. Our contributions include introducing techniques to assess predictive consistency between the original and compressed models using model agreement, and applying chi-squared tests to detect statistically significant changes in predictive patterns across both the overall dataset and demographic subgroups, thereby exposing shifts that aggregate fairness metrics may obscure. We demonstrate our approaches by applying quantization and pruning to artificial neural networks (ANNs) trained on three diverse and socially meaningful datasets. Our findings show that high accuracy does not guarantee faithfulness, and our statistical tests detect subtle yet significant shifts that are missed by standard metrics, such as Accuracy and Equalized Odds. The proposed metrics provide a practical and more direct method for ensuring that efficiency gains through compression do not compromise the fairness or faithfulness essential for trustworthy AI.

Jeremias Ferrao, Ezgi Basar, Khondoker Ittehadul Islam et al.

This study investigates the attribution patterns underlying Chain-of-Thought (CoT) reasoning in multilingual LLMs. While prior works demonstrate the role of CoT prompting in improving task performance, there are concerns regarding the faithfulness and interpretability of the generated reasoning chains. To assess these properties across languages, we applied two complementary attribution methods--ContextCite for step-level attribution and Inseq for token-level attribution--to the Qwen2.5 1.5B-Instruct model using the MGSM benchmark. Our experimental results highlight key findings such as: (1) attribution scores excessively emphasize the final reasoning step, particularly in incorrect generations; (2) structured CoT prompting significantly improves accuracy primarily for high-resource Latin-script languages; and (3) controlled perturbations via negation and distractor sentences reduce model accuracy and attribution coherence. These findings highlight the limitations of CoT prompting, particularly in terms of multilingual robustness and interpretive transparency.

Saprudin Efendi

The purpose of this study was to analyze the implications of Islamic religious education for the religiosity of the coastal community in the village of Tanjung Luar. This research method uses a qualitative-phenomenological approach. The research location is located in Tanjung Luar Village, Keruak District, East Lombok Regency. The data sources in this study consist of primary and secondary data. Data was obtained from observation, interviews, and documentation. The population of this research is the coastal fishing community of Tanjung Luar. The research sample was taken from community leaders, religious leaders, youth leaders, and madrasah students proportionally. Data analysis in this study used descriptive qualitative analysis, by collecting data, classifying data, reducing, evaluating, and making conclusions. Based on the results of the study, shows that Islamic religious education in fishermen's families in the village of Tanjung Luar has significant implications for carrying out Islamic worship and law in everyday life. There are two categories of fishing families in implementing Islamic religious education. First, parents who implement Islamic religious values in their family. Second, parents who lack the motivation in implementing Islamic religious values in their family

Reza Haqpanah

A Narrative traditions (Arabic: روایات, Romanized: riwāyāt) serve as crucial exegetical tools in Qurʾānic interpretation. However, the majority of such exegetical narrations suffer from weak chains of transmission (Arabic: اسناد, Romanized: asnād), inconsistencies, and content-related contradictions. One of the fundamental methodologies for evaluating exegetical narrations is to examine them in light of the Qurʾān itself. This study, employing a descriptive-analytical and library-based approach, conducts a case-based evaluation of the narrative and exegetical opinions concerning the controversial issue of the marriage of Ādam’s offspring, scrutinizing them against Qurʾānic verses. Findings reveal that the narrations supporting the dominant exegetical view, which holds that Ādam’s children married one another, not only contradict other narrations but are also at odds with the Qurʾān itself, undermining the claim that they align with the apparent meaning of Sūrah an-Nisāʾ (4:1). The second exegetical perspective, which suggests that Ādam’s offspring intermarried with jinn (Arabic: جن) or ḥūrīs (Arabic: حور), also suffers from content-based inconsistencies, and its reliance on Qurʾānic verses to substantiate the possibility or occurrence of such connection remains unfounded. The most coherent and defensible opinion, supported by both Qurʾānic and narrative evidence, posits that Ādam’s children intermarried with pre-existing human populations, a perspective that harmonizes with Qurʾānic discourse and select traditions.

Ina Mutmainah, Annisa Apriliantika

Purpose – The present study examines the impact of Islamic corporate governance and social responsibility on financial performance. Methodology – The study was designed with a quantitative approach. Purposive sampling was used in this study. Data were garnered through panel data from annual reports on Islamic banking in Indonesia and Malaysia from 2018 to 2020. The data were analyzed employing the Path Analysis. Findings – Study findings demonstrate that Islamic social responsibility and corporate governance did not significantly impact financial performance. Additionally, disclosing its Islamic ethical identity, Islamic corporate governance had a substantial impact on financial performance. Islamic social responsibility, however, had no impact on financial performance when Islamic ethical identity was disclosed. Implication – The findings offer empirical insights for managing Islamic commercial banks in Indonesia and Malaysia to elevate the application of Islamic corporate governance and social responsibility. This transparently demonstrates the quality of governance and social responsibility of Islamic banking with the disclosure of Islamic ethical identity following the standards applied by Islamic banking affecting stakeholder satisfaction. Originality – This research contributes significantly to the realm of Islamic banking management. It examines the level of stakeholder satisfaction through the enactment of Islamic corporate governance as a form of supervision of management performance, Islamic social responsibility as a form of corporate concern for the surrounding environment and Islamic ethical identity disclosure to promote the image of Islamic banking.

Seraj A. M. Mostafa, Md Z. Islam, Mohammad Z. Islam et al.

Artificially intelligent chatbot, such as ChatGPT, represents a recent and powerful advancement in the AI domain. Users prefer them for obtaining quick and precise answers, avoiding the usual hassle of clicking through multiple links in traditional searches. ChatGPT's conversational approach makes it comfortable and accessible for finding answers quickly and in an organized manner. However, it is important to note that these chatbots have limitations, especially in terms of providing accurate answers as well as ethical concerns. In this study, we explore various scenarios involving ChatGPT's ethical implications within academic contexts, its limitations, and the potential misuse by specific user groups. To address these challenges, we propose architectural solutions aimed at preventing inappropriate use and promoting responsible AI interactions.

Md. Simul Hasan Talukder, Mohammad Raziuddin Chowdhury, Md Sakib Ullah Sourav et al.

In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, and somewhat imprecise, which poses a substantial financial risk. To address this issue, the study proposes a high-performing and resilient transfer learning (TL) based JutePestDetect model to identify jute pests at the early stage. Firstly, we prepared jute pest dataset containing 17 classes and around 380 photos per pest class, which were evaluated after manual and automatic pre-processing and cleaning, such as background removal and resizing. Subsequently, five prominent pre-trained models -DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50 were selected from a previous study to design the JutePestDetect model. Each model was revised by replacing the classification layer with a global average pooling layer and incorporating a dropout layer for regularization. To evaluate the models performance, various metrics such as precision, recall, F1 score, ROC curve, and confusion matrix were employed. These analyses provided additional insights for determining the efficacy of the models. Among them, the customized regularized DenseNet201-based proposed JutePestDetect model outperformed the others, achieving an impressive accuracy of 99%. As a result, our proposed method and strategy offer an enhanced approach to pest identification in the case of Jute, which can significantly benefit farmers worldwide.

Wenxuan Zhou, Sheng Zhang, Hoifung Poon et al.

Large language models (LLMs) encode parametric knowledge about world facts and have shown remarkable performance in knowledge-driven NLP tasks. However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks (e.g., knowledge acquisition tasks). In this paper, we seek to assess and enhance LLMs' contextual faithfulness in two aspects: knowledge conflict and prediction with abstention. We demonstrate that LLMs' faithfulness can be significantly improved using carefully designed prompting strategies. In particular, we identify opinion-based prompts and counterfactual demonstrations as the most effective methods. Opinion-based prompts reframe the context as a narrator's statement and inquire about the narrator's opinions, while counterfactual demonstrations use instances containing false facts to improve faithfulness in knowledge conflict situations. Neither technique requires additional training. We conduct experiments on three datasets of two standard NLP tasks, machine reading comprehension and relation extraction, and the results demonstrate significant improvement in faithfulness to contexts. Code and data are released at https://github.com/wzhouad/context-faithful-llm.

Tom A. Lamb, Rudy Brunel, Krishnamurthy DJ Dvijotham et al.

Knowledge distillation (KD) has received much attention due to its success in compressing networks to allow for their deployment in resource-constrained systems. While the problem of adversarial robustness has been studied before in the KD setting, previous works overlook what we term the relative calibration of the student network with respect to its teacher in terms of soft confidences. In particular, we focus on two crucial questions with regard to a teacher-student pair: (i) do the teacher and student disagree at points close to correctly classified dataset examples, and (ii) is the distilled student as confident as the teacher around dataset examples? These are critical questions when considering the deployment of a smaller student network trained from a robust teacher within a safety-critical setting. To address these questions, we introduce a faithful imitation framework to discuss the relative calibration of confidences and provide empirical and certified methods to evaluate the relative calibration of a student w.r.t. its teacher. Further, to verifiably align the relative calibration incentives of the student to those of its teacher, we introduce faithful distillation. Our experiments on the MNIST, Fashion-MNIST and CIFAR-10 datasets demonstrate the need for such an analysis and the advantages of the increased verifiability of faithful distillation over alternative adversarial distillation methods.

Pavel Pavlovitch

Havva Esma Akış

Sahabe biyografileri, Siyer yazıcılığına zenginlik katan hususlardandır. Kadını ve erkeği ile Hz. Peygamber’i yakinen tanıyan, onun kutlu risaletine tanıklık eden ve ilk İslâm toplumunu oluşturan bireylerin hayat hikâyelerinin ehemmiyeti büyüktür. Özellikle kadın sahabîlerin ilim dünyasına tanıtılmasının lüzumu izahtan vârestedir. Bu ne¬denle makalemizde meşhur kadın sahabî ‘Âtike bint Zeyd’in hayatı ele alınacaktır. ‘Âtike bint Zeyd, Hz. Peygamber’in cennetle müjdelediği on kişiden biri olan Saîd b. Zeyd’in kardeşi, Cahiliye döneminde hanîf olarak yaşamış Zeyd b. Amr’ın kızı, Hz. Ebû Bekir’in gelini, Hz. Ömer’in, Zübeyr b. Avvâm’ın ve bir rivayete göre de Hz. Hüseyin’in eşi ve ‘İyâd b. Ömer’in annesidir. O İslâm’ın Mekke döneminde Müslü¬man olan ve Medine’ye hicret edenler arasında idi. Hayatının Mekke dönemine dair kaynaklarda bilgi bulunmamaktadır. İlk evliliğini Hz. Ebû Bekir’in oğlu Abdullah ile yapmıştır. Abdullah’ın vefatından sonra Resûlullah’ın ashabından birkaç kişi ile daha evlenmiştir. Sonraki eşlerinin hepsinin şehit olması nedeni ile Medinelilerin “Kim şehit olmak istiyorsa ‘Âtike ile evlensin” dedikleri bir kadın sahabîdir. Gerek İslam öncesi gerekse İslamî dönemde Araplar arasında yaygın olan şâirlik, ‘Âtike bint Zeyd’in mümeyyiz vasıflarından birisidir. Şiirleri arasında hem Resûlullah, hem de şehit düşen eşleri için söyledikleri günümüze kadar ulaşmıştır. İbadete düşkünlüğü, namazlarını Mescid-i Nebevî’de kılma konusundaki hassasiyeti ve evleneceği zaman eşlerine bunu şart koşması da onun dikkat çeken özellikleri arasında zikredilmektedir.

Bayu Saputra

Pada masa kepemimpinan Donald Trump, Amerika Serikat kembali menaruh perhatian terhadap isu imigran ilegal yang marak masuk ke wilayah negaranya. Terutama imigran gelap yang berasal dari negara-negara Amerika Selatan melalui perbatasan AS-Meksiko. Para imigran menjadi subjek yang disalahkan kaum konservatif karena dianggap telah mengikis budaya lokal AS dan mengancam perekonomian nasional. Selanjutnya pemerintahan AS mengimplementasi sebuah kebijakan luar negeri yang populer dengan nama „Trump Wall? dengan dalih demi keamanan nasional. Perumusan „Trump Wall? tidak lepas dari adanya pro dan kontra serta peran partai Republik sebagai partai pengusung Donald Trump pada pemilihan presiden 2016 lalu. Para Republikan yang menguasai kursi legislatif AS pada 2017-2018 semakin memperkuat pengaruh ideologi Neo- Konservatif dalam politik domestik maupun luar negeri AS. Artikel ini bertujuan untuk mengetahui bagaimana pengaruh partai Republik terhadap kebijakan luar negeri „Trump Wall?. Dalam artikel, Penulis menggunakan teori Neo-Konservatif dan Group Level of Analysis untuk membantu proses analisis.

Chun Sik Chan, Huanqi Kong, Guanqing Liang

Interpretation methods to reveal the internal reasoning processes behind machine learning models have attracted increasing attention in recent years. To quantify the extent to which the identified interpretations truly reflect the intrinsic decision-making mechanisms, various faithfulness evaluation metrics have been proposed. However, we find that different faithfulness metrics show conflicting preferences when comparing different interpretations. Motivated by this observation, we aim to conduct a comprehensive and comparative study of the widely adopted faithfulness metrics. In particular, we introduce two assessment dimensions, namely diagnosticity and time complexity. Diagnosticity refers to the degree to which the faithfulness metric favours relatively faithful interpretations over randomly generated ones, and time complexity is measured by the average number of model forward passes. According to the experimental results, we find that sufficiency and comprehensiveness metrics have higher diagnosticity and lower time complexity than the other faithfulness metric

M. Nur Fauzi

This article discusses the paradigm of thought of Islam transnational movement to spread its ideas and discourse in the public sphere. The Islamic transnational movement on this research focused on Islamism and Post Islamism. The research tries to answer a few questions the idea and discourse, shifting paradigm of idea and discourse from Islamism to Post Islamism,  and its response to the discourse that grow up from Western civilization. This research founded that it is significant of Islamic shifting paradigm from both Islamic movements. At last, the shifting of the Islamic paradigm caused the differences of thought to respond to the discourse from western modern civilization. From that different paradigm thought of the both contemporary Islamic movement has a result the difference of the product of thought and dichotomic-dualistic too. Islamisms so far reject the ideas and discourse from the Western civilization, meanwhile, Post Islamism more appreciate, accept, and critically.