The impact of real world events on fictional media is particularly apparent in the American cartoon series The Simpsons. While there are often very direct pop culture references evident in the dialogue and visual gags of the show, subtle changes in tone or sentiment may not be so obvious. Our aim was to use Natural Language Processing to attempt to search for changes in word frequency, topic, and sentiment before and after the September 11 terrorist attacks in New York. No clear trend change was seen, there was a slight decrease in the average sentiment over time around the relevant period between 2000 and 2002, but the scripts still maintained an overall positive value, indicating that the comedic nature of The Simpsons did not wane particularly significantly. The exploration of other social issues and even specific character statistics is needed to bolster the findings here.
Illusion or Algorithm? Investigating Memorization, Emergence, and Symbolic Processing in In-Context Learning
Technical University of Darmstadt（德累斯顿技术大学）；UKP Lab（UKP实验室）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；The University of Bath（巴斯大学）
Investigating how well contextual features are captured by bi-directional recurrent neural network models
Adobe Research, Big Data Experience Lab（Adobe 研究院大数据体验实验室）；National Center for Text Mining, The University of Manchester（曼彻斯特大学国家文本挖掘中心）；IIT Guwahati（印度理工学院古瓦哈提分校）
The Less the Merrier? Investigating Language Representation in Multilingual Models
University of California Berkeley（加州大学伯克利分校）；University of Colorado, Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；Instituto Politécnico Nacional（墨西哥国立理工学院）；Lelapa AI（莱拉帕人工智能公司）
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups
National University of Science and Technology POLITEHNICA Bucharest（布加勒斯特理工国立大学）；National Institute for Research & Development in Informatics - ICI Bucharest（布加勒斯特国家信息学研究与发展研究所）；Academy of Romanian Scientists（罗马尼亚科学院）；Paris 1 Panthéon-Sorbonne University（巴黎第一大学先贤祠-索邦大学）；University of Bucharest（布加勒斯特大学）
University of Pretoria（比勒陀利亚大学）；CSIR（科学与工业研究理事会）；University of Zululand（祖鲁兰大学）；University of Cape Town（开普敦大学）；University of Limpopo（林波波大学）；North-West University（西北大学）；University of the Witwatersrand（威特沃特斯兰德大学）
The recent advances in Natural Language Processing have been a boon for well-represented languages in terms of available curated data and research resources. One of the challenges for low-resourced languages is clear guidelines on the collection, curation and preparation of datasets for different use-cases. In this work, we take on the task of creation of two datasets that are focused on news headlines (i.e short text) for Setswana and Sepedi and creation of a news topic classification task. We document our work and also present baselines for classification. We investigate an approach on data …
University of Mannheim（曼海姆大学）；Carnegie Mellon University（卡内基梅隆大学）；Bocconi University（博科尼大学）；University of Hamburg（汉堡大学）
Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area in natural language processing (NLP), there is a surprising lack of research on the interplay between the two fields. This lacuna is highly problematic, since there is increasing evidence that an exclusive focus on fairness can actually hinder environmental sustainability, and vice versa. In this work, we shed light on this crucial intersection in NLP by (1) investigating the efficiency of current fairness approache…
Lelapa AI（莱拉帕人工智能公司）；Luleå University of Technology（吕勒奥理工大学）；Lanfrica（兰弗里卡）；Instituto Politécnico Nacional（墨西哥国立理工学院）；USC Information Sciences Institute（南加州大学信息科学研究所）；University of California, Davis（加州大学戴维斯分校）；New Mexico State University（新墨西哥州立大学）；Technical University of Munich（慕尼黑工业大学）；University of Porto（波尔图大学）；ML Collective（机器学习集体）；University of Toronto（多伦多大学）；University of Dayton（代顿大学）；McGill University（麦吉尔大学）；Mila Quebec AI Institute（米拉魁北克人工智能研究所）
Many natural language processing (NLP) tasks make use of massively pre-trained language models, which are computationally expensive. However, access to high computational resources added to the issue of data scarcity of African languages constitutes a real barrier to research experiments on these languages. In this work, we explore the applicability of low-compute approaches such as language adapters in the context of this low-resource double-bind. We intend to answer the following question: do language adapters allow those who are doubly bound by data and compute to practically build useful m…
University of Pennsylvania（宾夕法尼亚大学）；Tencent AI Lab（腾讯人工智能实验室）；Columbia University（哥伦比亚大学）；AWS AI Labs（亚马逊云服务人工智能实验室）
Double-blind peer review mechanism has become the skeleton of academic research across multiple disciplines including computer science, yet several studies have questioned the quality of peer reviews and raised concerns on potential biases in the process. In this paper, we conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs). We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date by aggregating data from OpenReview…
Recently, a number of articles have argued that deep learning models such as GPT could also capture key aspects of language processing in the human mind and brain. However, I will argue that these models are not suitable as neural models of human language. Firstly, because they fail on fundamental boundary conditions, such as the amount of learning they require. This would in fact imply that the mechanisms of GPT and brain language processing are fundamentally different. Secondly, because they do not possess the logistics of access needed for compositional and productive human language process…
Investigating Task Arithmetic for Zero-Shot Information Retrieval
Department of Informatics, Systems and Communication - DISCo, University of Milano-Bicocca（信息学、系统与通信系 - DISCo，米兰-比科卡大学）；DAUIN Dipartimento di Automatica e Informatica, Politecnico di Torino（DAUIN 自动控制与信息系，托斯卡纳理工学院）
Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study
King Mongkut's University of Technology Thonburi（国王科技大学）
Active learning has been shown to be an effective way to alleviate some of the effort required in utilising large collections of unlabelled data for machine learning tasks without needing to fully label them. The representation mechanism used to represent text documents when performing active learning, however, has a significant influence on how effective the process will be. While simple vector representations such as bag-of-words and embedding-based representations based on techniques such as word2vec have been shown to be an effective way to represent documents during active learning, the e…
