Hasil untuk "Greek philology and language"

Heinz-Jürgen Schulz-Koppe

Juliette Theißen

Heinz-Jürgen Schulz-Koppe

Maria Sandali

The Second Sophistic is defined as the period of letters "rebirth" and the "rewriting" of the Greek past. The emperors of the time, mainly the Flavians, contributed to this development by highlighting the art of rhetoric through the writing of works and the establishment of schools. The Romans were aware of Greek tradition and Greek models, which they promoted, wishing to promote their political ideology and propaganda. The rhetoricians of the time, the sophists, engaged with and imitated the earlier classical authors. The historical past was a topic of interest. The reworking of familiar historical and mythological themes was a preoccupation in the schools of rhetoric, the result of which were "declamations", i.e. discourses of embellished presentation of the past. One of the rewriting subjects of the Greeks' history was the Persian Wars. Greco-Persian battles were extremely popular, as Spawforth (1994) and Swain (1996) note, ideologically serving both the Greeks and the Romans. The encouragement of this practice was accompanied by the provision of privileges and facilities, as literature was placed at the service and constituted a form of legitimization of the emperor's and the imperial state's role in protecting both halves of the civilized world.

Søren Vejlgaard Holm, Lars Kai Hansen, Martin Carsten Nielsen

The language technology moonshot moment of Generative Large Language Models (GLLMs) was not limited to English: These models brought a surge of technological applications, investments, and hype to low-resource languages as well. However, the capabilities of these models in languages such as Danish were, until recently, difficult to verify beyond qualitative demonstrations due to a lack of applicable evaluation corpora. We present a GLLM benchmark to evaluate \emph{Danoliteracy}, a measure of Danish language and cultural competency across eight diverse scenarios such as Danish citizenship tests and abstractive social media question answering. This limited-size benchmark was found to produce a robust ranking that correlates to human feedback at $ρ\sim 0.8$ with GPT-4 and Claude Opus models achieving the highest rankings. Analyzing these model results across scenarios, we find one strong underlying factor explaining $95\%$ of scenario performance variance for GLLMs in Danish, suggesting a $g$ factor of model consistency in language adaptation.

Oline Ranum, Gomer Otterspeer, Jari I. Andersen et al.

In this work, we present an efficient approach for capturing sign language in 3D, introduce the 3D-LEX v1.0 dataset, and detail a method for semi-automatic annotation of phonetic properties. Our procedure integrates three motion capture techniques encompassing high-resolution 3D poses, 3D handshapes, and depth-aware facial features, and attains an average sampling rate of one sign every 10 seconds. This includes the time for presenting a sign example, performing and recording the sign, and archiving the capture. The 3D-LEX dataset includes 1,000 signs from American Sign Language and an additional 1,000 signs from the Sign Language of the Netherlands. We showcase the dataset utility by presenting a simple method for generating handshape annotations directly from 3D-LEX. We produce handshape labels for 1,000 signs from American Sign Language and evaluate the labels in a sign recognition task. The labels enhance gloss recognition accuracy by 5% over using no handshape annotations, and by 1% over expert annotations. Our motion capture data supports in-depth analysis of sign features and facilitates the generation of 2D projections from any viewpoint. The 3D-LEX collection has been aligned with existing sign language benchmarks and linguistic resources, to support studies in 3D-aware sign language processing.

Hamees Sayed, Advait Joglekar, Srinivasan Umesh

We develop a robust translation model for four low-resource Indic languages: Khasi, Mizo, Manipuri, and Assamese. Our approach includes a comprehensive pipeline from data collection and preprocessing to training and evaluation, leveraging data from WMT task datasets, BPCC, PMIndia, and OpenLanguageData. To address the scarcity of bilingual data, we use back-translation techniques on monolingual datasets for Mizo and Khasi, significantly expanding our training corpus. We fine-tune the pre-trained NLLB 3.3B model for Assamese, Mizo, and Manipuri, achieving improved performance over the baseline. For Khasi, which is not supported by the NLLB model, we introduce special tokens and train the model on our Khasi corpus. Our training involves masked language modelling, followed by fine-tuning for English-to-Indic and Indic-to-English translations.

Brendon Boldt, David Mortensen

In this paper, we introduce a benchmark for evaluating the overall quality of emergent languages using data-driven methods. Specifically, we interpret the notion of the "quality" of an emergent language as its similarity to human language within a deep learning framework. We measure this by using the emergent language as pretraining data for a downstream NLP tasks in human language -- the better the downstream performance, the better the emergent language. We implement this benchmark as an easy-to-use Python package that only requires a text file of utterances from the emergent language to be evaluated. Finally, we empirically test the benchmark's validity using human, synthetic, and emergent language baselines.

Ortimini, Pietro

La stele con bassorilievo, rinvenuta a Cihanköy, a nord-ovest del lago di İznik, e conservata presso il Museo Archeologico di Istanbul dal 1901, riporta un’iscrizione doppia composta da due epitafi in distici elegiaci dedicati all’ufficiale (hegemon) Menas di Bitinia, caduto in una battaglia svoltasi verosimilmente nella piana di Curupedio, nei pressi del fiume Frigio in Lidia. Sull’identificazione della battaglia, si è ipotizzato la battaglia di Curupedio tra Lisimaco e Seleuco I (281 a.C.), la battaglia di Magnesia tra Roma e Antioco III (190/189 a.C.), i conflitti tra il regno di Bitinia e quello di Pergamo (208‑183 a.C.; 156‑154 a.C.).

Alex Mei, Sharon Levy, William Yang Wang

As large language models are integrated into society, robustness toward a suite of prompts is increasingly important to maintain reliability in a high-variance environment.Robustness evaluations must comprehensively encapsulate the various settings in which a user may invoke an intelligent system. This paper proposes ASSERT, Automated Safety Scenario Red Teaming, consisting of three methods -- semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection. For robust safety evaluation, we apply these methods in the critical domain of AI safety to algorithmically generate a test suite of prompts covering diverse robustness settings -- semantic equivalence, related scenarios, and adversarial. We partition our prompts into four safety domains for a fine-grained analysis of how the domain affects model performance. Despite dedicated safeguards in existing state-of-the-art models, we find statistically significant performance differences of up to 11% in absolute classification accuracy among semantically related scenarios and error rates of up to 19% absolute error in zero-shot adversarial settings, raising concerns for users' physical safety.

Max J. van Duijn, Bram M. A. van Dijk, Tom Kouwenhoven et al.

To what degree should we ascribe cognitive capacities to Large Language Models (LLMs), such as the ability to reason about intentions and beliefs known as Theory of Mind (ToM)? Here we add to this emerging debate by (i) testing 11 base- and instruction-tuned LLMs on capabilities relevant to ToM beyond the dominant false-belief paradigm, including non-literal language usage and recursive intentionality; (ii) using newly rewritten versions of standardized tests to gauge LLMs' robustness; (iii) prompting and scoring for open besides closed questions; and (iv) benchmarking LLM performance against that of children aged 7-10 on the same tasks. We find that instruction-tuned LLMs from the GPT family outperform other models, and often also children. Base-LLMs are mostly unable to solve ToM tasks, even with specialized prompting. We suggest that the interlinked evolution and development of language and ToM may help explain what instruction-tuning adds: rewarding cooperative communication that takes into account interlocutor and context. We conclude by arguing for a nuanced perspective on ToM in LLMs.

Alice Borgna, AB

Recensione

Simone Mollea, SM

In Off. 2, 51, Cicerone dichiara che, in nome del concetto di humanitas, vanno difesi anche imputati colpevoli. Retrospettivamente, constatiamo come egli avesse fatto esplicito appello all’humanitas dei giudici in cinque orazioni (Pro Cluentio, Pro Balbo, Pro Archia, Pro Sulla e Pro Caelio) per cui la critica concorda sul fatto che gli elementi concreti per le argomentazioni difensive fossero pochi e l'innocenza degli imputati tutt'altro che scontata. Pur non volendo giungere alla conclusione che, nel 44 a.C., Cicerone in qualche modo ammetta che il ricorso esplicito all'argomento humanitas in alcuni processi di anni precedenti fosse conseguenza della colpevolezza degli imputati, questo contributo intende mostrare come e perché per tutte le suddette orazioni l’humanitas costituisca un collante o, al contrario, un elemento di separazione tra i giudici e altre parti coinvolte nel processo. Emergerà quindi che l'humanitas risulta, in definitiva, un'arma retoricamente molto efficace, che distrae i giudici dal nocciolo della questione, lusingandoli con accostamenti illustri (a Pompeo, ad un grande? poeta, a Cicerone stesso) e/o separandoli nettamente da reietti (Oppianico, Sassia) o dagli accusatori (Manlio Torquato, Erennio).

Sabine Hommen

Petros Marazopoulos

The aim of this article is to examine the Greek and international aesthetic reaction to the phenomenon of the economic crisis. By examining Greek and international literary texts that depict Greece during the era of austerity, I attempt to explain how crisis is perceived in the literary field. Thus, I aspire to analyse the way in which this literature negotiates the terms economy, crisis, Europe, power and past. At the same time, I discuss contemporary literary images of the Greek Other; through a comparative study of Greek and international relevant texts, I aim to highlight the political and ideological rhetoric of the texts under examination, as well as the perception of crisis as a global issue, rather than a 'Greek adventure'. In that sense, the authors under examination do not simply dramatise the traumatic events of the recession, but they also suggest a broader definition of crisis, as a global phenomenon, discussing aspects of the contemporary European South and its balance with the European North.

Hyuhng Joon Kim, Hyunsoo Cho, Junyeob Kim et al.

Large-scale pre-trained language models (PLMs) are well-known for being capable of solving a task simply by conditioning a few input-label pairs dubbed demonstrations on a prompt without being explicitly tuned for the desired downstream task. Such a process (i.e., in-context learning), however, naturally leads to high reliance on the demonstrations which are usually selected from external datasets. In this paper, we propose self-generated in-context learning (SG-ICL), which generates demonstrations for in-context learning from PLM itself to minimize the reliance on the external demonstration. We conduct experiments on four different text classification tasks and show SG-ICL significantly outperforms zero-shot learning and is generally worth approximately 0.6 gold training samples. Moreover, our generated demonstrations show more consistent performance with low variance compared to randomly selected demonstrations from the training dataset.

Michael Ahn, Anthony Brohan, Noah Brown et al.

Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at https://say-can.github.io/.

Monica Agrawal, Stefan Hegselmann, Hunter Lang et al.

A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the general domain and a lack of public clinical corpora and annotations. In this work, we show that large language models, such as InstructGPT, perform well at zero- and few-shot information extraction from clinical text despite not being trained specifically for the clinical domain. Whereas text classification and generation performance have already been studied extensively in such models, here we additionally demonstrate how to leverage them to tackle a diverse set of NLP tasks which require more structured outputs, including span identification, token-level sequence classification, and relation extraction. Further, due to the dearth of available data to evaluate these systems, we introduce new datasets for benchmarking few-shot clinical information extraction based on a manual re-annotation of the CASI dataset for new tasks. On the clinical extraction tasks we studied, the GPT-3 systems significantly outperform existing zero- and few-shot baselines.

Ludwika Gulka-Höll

Marijke Crab, MC

The article introduces a postdoctoral research project entitled Cicero, Man of Letters. The Reception of Cicero’s Epistles in the Renaissance. Starting from a study of all Cicero letters editions printed in the fifteenth and sixteenth centuries, and the paratexts contained therein, this project seeks to establish not only which letters were published, when and where, by whom, for whom, in which language and why; but it also explores how these letters were read and interpreted in this period, and which image of Cicero they spread. In the present contribution, I describe how I went about collecting, organising and interpreting the source materials, with special attention to the methods followed, the digital resources used and the planned digital output, before presenting some intermediate results of my study of the Cicero letters editions printed up to 1550. Throughout, I highlight not only the prospects but also the limitations and possible pitfalls of these new technologies for studying old books.