1,721,019 research outputs found
Towards Distribution-shift Robust Text Classification of Emotional Content
Full text linkSupervised models based on Transformers have been shown to achieve impressive performances in many natural language processing tasks. However, besides requiring a large amount of costly manually annotated data, supervised models tend to adapt to the characteristics of the training dataset, which are usually created ad-hoc and whose data distribution often differs from the one in real applications, showing significant performance degradation in real-world scenarios. We perform an extensive assessment of the out-of-distribution performances of supervised models for classification in the emotion and hate-speech detection tasks and show that NLI-based zero-shot models often outperform them, making task-specific annotation useless when the characteristics of final-user data are not known in advance. To benefit from both supervised and zero-shot approaches, we propose to fine-tune an NLI-based model on the task-specific dataset. The resulting model often outperforms all available supervised models both in distribution and out of distribution, with only a few thousand training samples
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Eliciting metaknowledge in Large Language Models
No full textThe introduction of Large Language Models (LLMs) able to exhibit a number of linguistic and extra-linguistic capabilities has represented, in the last years, one of the main frontiers in Artificial Intelligence (AI) research. Researcher from various disciplines debate about whether or not, among the capabilities of LLMs, there is the one of using knowledge about knowledge-usually considered one of the antechambers of meta-cognition in cognitive agents-about a particular task in order to improve or self-correct previous errors. In this work we propose a novel fine-tuning approach for LLMs, named EXAR, based on a multi-stage process leveraging past predictions from an early version of the same, and aimed at injecting metacognitive features for the task of Question-Answering. The conducted experiments on LLAMA-2-7B-CHAT showed promising improvements on the quality of the outcomes, due to the fact that the LLM acquired the ability to detect its own wrong predictions forcing itself to repeat submissions, thorough a prompt designed to fix inadmissible predictions, whenever detected. Such detection is achieved by enquiring the same LLM acting as meta-validator, through another prompt specifically designed for such purpose
Large Language Models meet moral values: A comprehensive assessment of moral abilities
Full text linkAutomatic moral classification in textual data is crucial for various fields including Natural Language Processing (NLP), social sciences, and ethical AI development. Despite advancements in supervised models, their performance often suffers when faced with real-world scenarios due to overfitting to specific data distributions. To address these limitations, we propose leveraging state-of-the-art Large Language Models (LLMs) trained on extensive common-sense data for unsupervised moral classification. We introduce an innovative evaluation framework that directly compares model outputs with human annotations, ensuring an assessment of model performance. Our methodology explores the effectiveness of different LLM sizes and prompt designs in moral value detection tasks, considering both multi-label and binary classification scenarios. We present experimental results using the Moral Foundation Reddit Corpus (MFRC) and discuss implications for future research in ethical AI development and human–computer interaction. Experimental results demonstrate that GPT-4 achieves superior performance, followed by GPT-3.5, Llama-70B, Mixtral-8x7B, Mistral-7B and Llama-7B. Additionally, the study reveals significant variations in model performance across different moral domains, particularly between everyday morality and political contexts. Our work provides meaningful insights into the use of zero-shot and few-shot models for moral value detection and discusses the potential and limitations of current technology in this task
A Large Visual Question Answering Dataset for Cultural Heritage
No full textVisual Question Answering (VQA) is gaining momentum for its ability of bridging Computer Vision and Natural Language Processing. VQA approaches mainly rely on Machine Learning algorithms that need to be trained on large annotated datasets. Once trained, a machine learning model is barely portable on a different domain. This calls for agile methodologies for building large annotated datasets from existing resources. The cultural heritage domain represents both a natural application of this task and an extensive source of data for training and validating VQA models. To this end, by using data and models from ArCo, the knowledge graph of the Italian cultural heritage, we generated a large dataset for VQA in Italian and English. We describe the results and the lessons learned by our semi-automatic process for the dataset generation and discuss the employed tools for data extraction and transformation
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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