Hasil untuk "Greek language and literature. Latin language and literature"

John Colley

This monograph reassesses how the Quattrocento rebirth of Greek scholarship influenced fifteenth- and sixteenth-century English literature. It begins with the first signs of humanist Greek in England in and around Duke Humfrey’s circle, at a time when no English writer could claim significant Greek literacy. It ends on the cusp of Elizabethan literary culture, when English writers much more frequently translated Ancient Greek into both Latin and the vernacular. This period witnessed a surge in the translation of Greek. It also witnessed changing beliefs about how and why Greek should be translated at all, especially under the growing pressures of the Reformation. Building on scholarship in the fields of classical reception scholarship, translation studies, and intellectual history, this book argues that attending to the period’s ideas about Greek translation fundamentally alters our perception of Tudor humanism and the classical tradition more widely. In linking biblical and patristic translation with the translation of works by pagan authors, the book shows that Renaissance humanism was less secular and more wide-ranging in its goals and interest than the standard scholarly narrative has claimed. By showing continuities between late medieval and early modern literature, it further revises arguments for the novelty of the sixteenth-century humanists. The book ultimately argues that fifteenth- and sixteenth-century English writers experienced a contradictory relationship to Greek. Desire for the language and what it stood for was tempered by the realities of its mediated transmission. Desire for Greek was also undercut by the sectarian divisions that the language came to reflect and magnify.

Manon Ertola Urtubey

Reseña de Éticas estoicas de J. M. Zamora Calvo por M. Ertola Urtubey. 

Alberto Regagliolo

Teaching classical culture to children can be done through literature and Phaedrus' fables. There are several books on the market that can be used to introduce Phaedrus' fables to children. However, in order to be suitable, the books should follow some requirements of appropriateness related to the use of the language and the values to be shared, among others. In this study, through the analysis of 12 Italian books on Phaedrus' fables for children, it will be analysed how the death of an animal is described through the use of verbs and structures. The research aims at making observations on how some books for children represent cruelty and the adoption of certain linguistic structures. The analysis shows, in the first place, that the authors never eliminate the death/killing of the animal; secondly, the verbs and expressions used are varied but, in most cases, cruel, and direct without making the death softer.

Nina Čengić

Aysenur Kocak, Shuo Yang, Bardh Prenkaj et al.

Pre-trained language models have achieved remarkable success across diverse applications but remain susceptible to spurious, concept-driven correlations that impair robustness and fairness. In this work, we introduce CURE, a novel and lightweight framework that systematically disentangles and suppresses conceptual shortcuts while preserving essential content information. Our method first extracts concept-irrelevant representations via a dedicated content extractor reinforced by a reversal network, ensuring minimal loss of task-relevant information. A subsequent controllable debiasing module employs contrastive learning to finely adjust the influence of residual conceptual cues, enabling the model to either diminish harmful biases or harness beneficial correlations as appropriate for the target task. Evaluated on the IMDB and Yelp datasets using three pre-trained architectures, CURE achieves an absolute improvement of +10 points in F1 score on IMDB and +2 points on Yelp, while introducing minimal computational overhead. Our approach establishes a flexible, unsupervised blueprint for combating conceptual biases, paving the way for more reliable and fair language understanding systems.

Anandita Garg, Uma Gaba, Deepan Muthirayan et al.

The advent of Large Language Models (LLMs) has raised concerns about their enormous carbon footprint, starting with energy-intensive training and continuing through repeated inference. This study investigates the potential of using fine-tuned Small Language Models (SLMs) as a sustainable alternative for predefined tasks. Here, we present a comparative analysis of the performance-emissions trade-off between LLMs and fine-tuned SLMs across selected tasks under Natural Language Processing, Reasoning and Programming. Our results show that in four out of the six selected tasks, SLMs maintained comparable performances for a significant reduction in carbon emissions during inference. Our findings demonstrate the viability of smaller models in mitigating the environmental impact of resource-heavy LLMs, thus advancing towards sustainable, green AI.

Jakub Piskorski, Michał Marcińczuk, Roman Yangarber

This paper presents a corpus manually annotated with named entities for six Slavic languages - Bulgarian, Czech, Polish, Slovenian, Russian, and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017-2023 as a part of the Workshops on Slavic Natural Language Processing. The corpus consists of 5 017 documents on seven topics. The documents are annotated with five classes of named entities. Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits - single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture with the pre-trained multilingual models - XLM-RoBERTa-large for named entity mention recognition and categorization, and mT5-large for named entity lemmatization and linking.

Yuanwei Wu, Yue Huang, Yixin Liu et al.

GPT-4V has attracted considerable attention due to its extraordinary capacity for integrating and processing multimodal information. At the same time, its ability of face recognition raises new safety concerns of privacy leakage. Despite researchers' efforts in safety alignment through RLHF or preprocessing filters, vulnerabilities might still be exploited. In our study, we introduce AutoJailbreak, an innovative automatic jailbreak technique inspired by prompt optimization. We leverage Large Language Models (LLMs) for red-teaming to refine the jailbreak prompt and employ weak-to-strong in-context learning prompts to boost efficiency. Furthermore, we present an effective search method that incorporates early stopping to minimize optimization time and token expenditure. Our experiments demonstrate that AutoJailbreak significantly surpasses conventional methods, achieving an Attack Success Rate (ASR) exceeding 95.3\%. This research sheds light on strengthening GPT-4V security, underscoring the potential for LLMs to be exploited in compromising GPT-4V integrity.

Xuemei Tang, Xufeng Duan, Zhenguang G. Cai

Large language models (LLMs) have emerged as a potential solution to automate the complex processes involved in writing literature reviews, such as literature collection, organization, and summarization. However, it is yet unclear how good LLMs are at automating comprehensive and reliable literature reviews. This study introduces a framework to automatically evaluate the performance of LLMs in three key tasks of literature writing: reference generation, literature summary, and literature review composition. We introduce multidimensional evaluation metrics that assess the hallucination rates in generated references and measure the semantic coverage and factual consistency of the literature summaries and compositions against human-written counterparts. The experimental results reveal that even the most advanced models still generate hallucinated references, despite recent progress. Moreover, we observe that the performance of different models varies across disciplines when it comes to writing literature reviews. These findings highlight the need for further research and development to improve the reliability of LLMs in automating academic literature reviews.

Xiutian Zhao, Ke Wang, Wei Peng

Despite large language models' (LLMs) recent advancements, their bias and hallucination issues persist, and their ability to offer consistent preferential rankings remains underexplored. This study investigates the capacity of LLMs to provide consistent ordinal preferences, a crucial aspect in scenarios with dense decision space or lacking absolute answers. We introduce a formalization of consistency based on order theory, outlining criteria such as transitivity, asymmetry, reversibility, and independence from irrelevant alternatives. Our diagnostic experiments on selected state-of-the-art LLMs reveal their inability to meet these criteria, indicating a strong positional bias and poor transitivity, with preferences easily swayed by irrelevant alternatives. These findings highlight a significant inconsistency in LLM-generated preferential rankings, underscoring the need for further research to address these limitations.

Jannik Peters, Constantin Waubert de Puiseau, Hasan Tercan et al.

The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learning. Although the concept of studying language emergence is not new, early approaches were primarily concerned with explaining human language formation, with little consideration given to its potential utility for artificial agents. In contrast, studies based on reinforcement learning aim to develop communicative capabilities in agents that are comparable to or even superior to human language. Thus, they extend beyond the learned statistical representations that are common in natural language processing research. This gives rise to a number of fundamental questions, from the prerequisites for language emergence to the criteria for measuring its success. This paper addresses these questions by providing a comprehensive review of 181 scientific publications on emergent language in artificial intelligence. Its objective is to serve as a reference for researchers interested in or proficient in the field. Consequently, the main contributions are the definition and overview of the prevailing terminology, the analysis of existing evaluation methods and metrics, and the description of the identified research gaps.

Lefteris Loukas, Nikolaos Smyrnioudis, Chrysa Dikonomaki et al.

We present GR-NLP-TOOLKIT, an open-source natural language processing (NLP) toolkit developed specifically for modern Greek. The toolkit provides state-of-the-art performance in five core NLP tasks, namely part-of-speech tagging, morphological tagging, dependency parsing, named entity recognition, and Greeklishto-Greek transliteration. The toolkit is based on pre-trained Transformers, it is freely available, and can be easily installed in Python (pip install gr-nlp-toolkit). It is also accessible through a demonstration platform on HuggingFace, along with a publicly available API for non-commercial use. We discuss the functionality provided for each task, the underlying methods, experiments against comparable open-source toolkits, and future possible enhancements. The toolkit is available at: https://github.com/nlpaueb/gr-nlp-toolkit

Urjitkumar Patel, Fang-Chun Yeh, Chinmay Gondhalekar et al.

In the rapidly evolving financial sector, the accurate and timely interpretation of market news is essential for stakeholders needing to navigate unpredictable events. This paper introduces FANAL (Financial Activity News Alerting Language Modeling Framework), a specialized BERT-based framework engineered for real-time financial event detection and analysis, categorizing news into twelve distinct financial categories. FANAL leverages silver-labeled data processed through XGBoost and employs advanced fine-tuning techniques, alongside ORBERT (Odds Ratio BERT), a novel variant of BERT fine-tuned with ORPO (Odds Ratio Preference Optimization) for superior class-wise probability calibration and alignment with financial event relevance. We evaluate FANAL's performance against leading large language models, including GPT-4o, Llama-3.1 8B, and Phi-3, demonstrating its superior accuracy and cost efficiency. This framework sets a new standard for financial intelligence and responsiveness, significantly outstripping existing models in both performance and affordability.

Eleanor Dickey

Why, when, and how did speakers of ancient Greek borrow words from Latin? Which words did they borrow? Who used Latin loanwords, and how? Who avoided them, and why? How many words were borrowed, and what kind of word? How long did the loanwords survive? Until now, attempts to answer such questions have been based on incomplete and often misleading evidence, but this study offers the first comprehensive collection of evidence from papyri, inscriptions, and literature from the fifth century BC to the sixth century AD. That collection – included in the book as a lexicon of Latin loanwords – is examined using insights from linguistic work on modern languages to provide new answers that often differ strikingly from earlier ones. The analysis is accessibly presented, and the lexicon offers a firm foundation for future work in this area.

Nancy Tyagi, Aidin Shiri, Surjodeep Sarkar et al.

Foundational Language Models (FLMs) have advanced natural language processing (NLP) research. Current researchers are developing larger FLMs (e.g., XLNet, T5) to enable contextualized language representation, classification, and generation. While developing larger FLMs has been of significant advantage, it is also a liability concerning hallucination and predictive uncertainty. Fundamentally, larger FLMs are built on the same foundations as smaller FLMs (e.g., BERT); hence, one must recognize the potential of smaller FLMs which can be realized through an ensemble. In the current research, we perform a reality check on FLMs and their ensemble on benchmark and real-world datasets. We hypothesize that the ensembling of FLMs can influence the individualistic attention of FLMs and unravel the strength of coordination and cooperation of different FLMs. We utilize BERT and define three other ensemble techniques: {Shallow, Semi, and Deep}, wherein the Deep-Ensemble introduces a knowledge-guided reinforcement learning approach. We discovered that the suggested Deep-Ensemble BERT outperforms its large variation i.e. BERTlarge, by a factor of many times using datasets that show the usefulness of NLP in sensitive fields, such as mental health.

Guijin Son, Hanearl Jung, Moonjeong Hahm et al.

Large Language Models (LLMs), consisting of 100 billion or more parameters, have demonstrated remarkable ability in complex multi-step reasoning tasks. However, the application of such generic advancements has been limited to a few fields, such as clinical or legal, with the field of financial reasoning remaining largely unexplored. To the best of our knowledge, the ability of LLMs to solve financial reasoning problems has never been dealt with, and whether it can be performed at any scale remains unknown. To address this knowledge gap, this research presents a comprehensive investigation into the potential application of LLMs in the financial domain. The investigation includes a detailed exploration of a range of subjects, including task formulation, synthetic data generation, prompting methods, and evaluation capability. Furthermore, the study benchmarks various GPT variants with parameter scales ranging from 2.8B to 13B, with and without instruction tuning, on diverse dataset sizes. By analyzing the results, we reveal that the ability to generate coherent financial reasoning first emerges at 6B parameters, and continues to improve with better instruction-tuning or larger datasets. Additionally, the study provides a publicly accessible dataset named sFIOG (Synthetic-Financial Investment Opinion Generation), consisting of 11,802 synthetic investment thesis samples, to support further research in the field of financial reasoning. Overall, this research seeks to contribute to the understanding of the efficacy of language models in the field of finance, with a particular emphasis on their ability to engage in sophisticated reasoning and analysis within the context of investment decision-making.

Iker García-Ferrero, Begoña Altuna, Javier Álvez et al.

Although large language models (LLMs) have apparently acquired a certain level of grammatical knowledge and the ability to make generalizations, they fail to interpret negation, a crucial step in Natural Language Processing. We try to clarify the reasons for the sub-optimal performance of LLMs understanding negation. We introduce a large semi-automatically generated dataset of circa 400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms. We have used our dataset with the largest available open LLMs in a zero-shot approach to grasp their generalization and inference capability and we have also fine-tuned some of the models to assess whether the understanding of negation can be trained. Our findings show that, while LLMs are proficient at classifying affirmative sentences, they struggle with negative sentences and lack a deep understanding of negation, often relying on superficial cues. Although fine-tuning the models on negative sentences improves their performance, the lack of generalization in handling negation is persistent, highlighting the ongoing challenges of LLMs regarding negation understanding and generalization. The dataset and code are publicly available.

Oskar van der Wal, Jaap Jumelet, Katrin Schulz et al.

Detecting and mitigating harmful biases in modern language models are widely recognized as crucial, open problems. In this paper, we take a step back and investigate how language models come to be biased in the first place. We use a relatively small language model, using the LSTM architecture trained on an English Wikipedia corpus. With full access to the data and to the model parameters as they change during every step while training, we can map in detail how the representation of gender develops, what patterns in the dataset drive this, and how the model's internal state relates to the bias in a downstream task (semantic textual similarity). We find that the representation of gender is dynamic and identify different phases during training. Furthermore, we show that gender information is represented increasingly locally in the input embeddings of the model and that, as a consequence, debiasing these can be effective in reducing the downstream bias. Monitoring the training dynamics, allows us to detect an asymmetry in how the female and male gender are represented in the input embeddings. This is important, as it may cause naive mitigation strategies to introduce new undesirable biases. We discuss the relevance of the findings for mitigation strategies more generally and the prospects of generalizing our methods to larger language models, the Transformer architecture, other languages and other undesirable biases.