pwc-archive/papers-with-abstracts huggingface dataset:
Investigating the Impact of Semi-Supervised Methods with Data Augmentation on Offensive Language Detection in Romanian Language
Authors: Elena Beatrice Nicola, Dumitru Clementin Cercel, Florin Pop
Date: 2024-07-29 00:00:00
Tasks: Data Augmentation

Offensive language detection is a crucial task in today's digital landscape, where online platforms grapple with maintaining a respectful and inclusive environment. However, building robust offensive language detection models requires large amounts of labeled data, which can be expensive and time-consuming to obtain. Semi-supervised learning offers a feasible solution by utilizing labeled and unlabeled data to create more accurate and robust models. In this paper, we explore a few different semi-supervised methods, as well as data augmentation techniques. Concretely, we implemented eight semi-supervised methods and ran experiments for them using only the available data in the RO-Offense dataset and applying five augmentation techniques before feeding the data to the models. Experimental results demonstrate that some of them benefit more from augmentations than others.
Exploring Large Language Models to generate Easy to Read content
Authors: Paloma Martínez, Lourdes Moreno, Alberto Ramos
Date: 2024-07-29 00:00:00
Tasks: Text Simplification

Ensuring text accessibility and understandability are essential goals, particularly for individuals with cognitive impairments and intellectual disabilities, who encounter challenges in accessing information across various mediums such as web pages, newspapers, administrative tasks, or health documents. Initiatives like Easy to Read and Plain Language guidelines aim to simplify complex texts; however, standardizing these guidelines remains challenging and often involves manual processes. This work presents an exploratory investigation into leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches to systematically simplify Spanish texts into Easy to Read formats, with a focus on utilizing Large Language Models (LLMs) for simplifying texts, especially in generating Easy to Read content. The study contributes a parallel corpus of Spanish adapted for Easy To Read format, which serves as a valuable resource for training and testing text simplification systems. Additionally, several text simplification experiments using LLMs and the collected corpus are conducted, involving fine-tuning and testing a Llama2 model to generate Easy to Read content. A qualitative evaluation, guided by an expert in text adaptation for Easy to Read content, is carried out to assess the automatically simplified texts. This research contributes to advancing text accessibility for individuals with cognitive impairments, highlighting promising strategies for leveraging LLMs while responsibly managing energy usage.
Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews
Authors: Wen Wu, Chao Zhang, Philip C. Woodland
Date: 2024-07-29 00:00:00
Tasks: Diagnostic

Speech-based automatic detection of Alzheimer's disease (AD) and depression has attracted increased attention. Confidence estimation is crucial for a trust-worthy automatic diagnostic system which informs the clinician about the confidence of model predictions and helps reduce the risk of misdiagnosis. This paper investigates confidence estimation for automatic detection of AD and depression based on clinical interviews. A novel Bayesian approach is proposed which uses a dynamic Dirichlet prior distribution to model the second-order probability of the predictive distribution. Experimental results on the publicly available ADReSS and DAIC-WOZ datasets demonstrate that the proposed method outperforms a range of baselines for both classification accuracy and confidence estimation.
A Temporal Psycholinguistics Approach to Identity Resolution of Social Media Users
Authors: Md Touhidul Islam
Date: 2024-07-29 00:00:00
Tasks: Sentiment Analysis

In this thesis, we propose an approach to identity resolution across social media platforms using the topics, sentiments, and timings of the posts on the platforms. After collecting the public posts of around 5000 profiles from Disqus and Twitter, we analyze their posts to match their profiles across the two platforms. We pursue both temporal and non-temporal methods in our analysis. While neither approach proves definitively superior, the temporal approach generally performs better. We found that the temporal window size influences results more than the shifting amount. On the other hand, our sentiment analysis shows that the inclusion of sentiment makes little difference, probably due to flawed data extraction methods. We also experimented with a distance-based reward-and-punishment-focused scoring model, which achieved an accuracy of 24.198% and an average rank of 158.217 out of 2525 in our collected corpus. Future work includes refining sentiment analysis by evaluating sentiments per topic, extending temporal analysis with additional phases, and improving the scoring model through weight adjustments and modified rewards.
Preliminary WMT24 Ranking of General MT Systems and LLMs
Authors: Tom Kocmi, Eleftherios Avramidis, Rachel Bawden, Ondrej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Markus Freitag, Thamme Gowda, Roman Grundkiewicz, Barry Haddow, Marzena Karpinska, Philipp Koehn, Benjamin Marie, Kenton Murray, Masaaki Nagata, Martin Popel, Maja Popovic, Mariya Shmatova, Steinþór Steingrímsson, Vilém Zouhar
Date: 2024-07-29 00:00:00

This is the preliminary ranking of WMT24 General MT systems based on automatic metrics. The official ranking will be a human evaluation, which is superior to the automatic ranking and supersedes it. The purpose of this report is not to interpret any findings but only provide preliminary results to the participants of the General MT task that may be useful during the writing of the system submission.
Comparative Analysis of Encoder-Based NER and Large Language Models for Skill Extraction from Russian Job Vacancies
Authors: Nikita Matkin, Aleksei Smirnov, Mikhail Usanin, Egor Ivanov, Kirill Sobyanin, Sofiia Paklina, Petr Parshakov
Date: 2024-07-29 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

The labor market is undergoing rapid changes, with increasing demands on job seekers and a surge in job openings. Identifying essential skills and competencies from job descriptions is challenging due to varying employer requirements and the omission of key skills. This study addresses these challenges by comparing traditional Named Entity Recognition (NER) methods based on encoders with Large Language Models (LLMs) for extracting skills from Russian job vacancies. Using a labeled dataset of 4,000 job vacancies for training and 1,472 for testing, the performance of both approaches is evaluated. Results indicate that traditional NER models, especially DeepPavlov RuBERT NER tuned, outperform LLMs across various metrics including accuracy, precision, recall, and inference time. The findings suggest that traditional NER models provide more effective and efficient solutions for skill extraction, enhancing job requirement clarity and aiding job seekers in aligning their qualifications with employer expectations. This research contributes to the field of natural language processing (NLP) and its application in the labor market, particularly in non-English contexts.
Segmentation en phrases : ouvrez les guillemets sans perdre le fil
Authors: Sandrine Ollinger, Denis Maurel
Date: 2024-07-29 00:00:00
Tasks: Sentence, Sentence segmentation

This paper presents a graph cascade for sentence segmentation of XML documents. Our proposal offers sentences inside sentences for cases introduced by quotation marks and hyphens, and also pays particular attention to situations involving incises introduced by parentheses and lists introduced by colons. We present how the tool works and compare the results obtained with those available in 2019 on the same dataset, together with an evaluation of the system's performance on a test corpus
Cool-Fusion: Fuse Large Language Models without Training
Authors: Cong Liu, Xiaojun Quan, Yan Pan, Liang Lin, Weigang Wu, Xu Chen
Date: 2024-07-29 00:00:00
Tasks: Combinatorial Optimization, GSM8K, Text Generation

We focus on the problem of fusing two or more heterogeneous large language models (LLMs) to facilitate their complementary strengths. One of the challenges on model fusion is high computational load, i.e. to fine-tune or to align vocabularies via combinatorial optimization. To this end, we propose \emph{Cool-Fusion}, a simple yet effective approach that fuses the knowledge of heterogeneous source LLMs to leverage their complementary strengths. \emph{Cool-Fusion} is the first method that does not require any type of training like the ensemble approaches. But unlike ensemble methods, it is applicable to any set of source LLMs that have different vocabularies. The basic idea is to have each source LLM individually generate tokens until the tokens can be decoded into a text segment that ends at word boundaries common to all source LLMs. Then, the source LLMs jointly rerank the generated text segment and select the best one, which is the fused text generation in one step. Extensive experiments are conducted across a variety of benchmark datasets. On \emph{GSM8K}, \emph{Cool-Fusion} increases accuracy from three strong source LLMs by a significant 8\%-17.8\%.
Teaching LLMs at Charles University: Assignments and Activities
Authors: Jindřich Helcl, Zdeněk Kasner, Ondřej Dušek, Tomasz Limisiewicz, Dominik Macháček, Tomáš Musil, Jindřich Libovický
Date: 2024-07-29 00:00:00
Tasks: Machine Translation, Translation

This paper presents teaching materials, particularly assignments and ideas for classroom activities, from a new course on large language models (LLMs) taught at Charles University. The assignments include experiments with LLM inference for weather report generation and machine translation. The classroom activities include class quizzes, focused research on downstream tasks and datasets, and an interactive "best paper" session aimed at reading and comprehension of research papers.
Introducing a new hyper-parameter for RAG: Context Window Utilization
Authors: Kush Juvekar, Anupam Purwar
Date: 2024-07-29 00:00:00
Tasks: Answer Generation, RAG, Retrieval, Retrieval-augmented Generation

This paper introduces a new hyper-parameter for Retrieval-Augmented Generation (RAG) systems called Context Window Utilization. RAG systems enhance generative models by incorporating relevant information retrieved from external knowledge bases, improving the factual accuracy and contextual relevance of generated responses. The size of the text chunks retrieved and processed is a critical factor influencing RAG performance. This study aims to identify the optimal chunk size that maximizes answer generation quality. Through systematic experimentation, we analyze the effects of varying chunk sizes on the efficiency and effectiveness of RAG frameworks. Our findings reveal that an optimal chunk size balances the trade-off between providing sufficient context and minimizing irrelevant information. These insights are crucial for enhancing the design and implementation of RAG systems, underscoring the importance of selecting an appropriate chunk size to achieve superior performance.
Synthesizing Scientific Summaries: An Extractive and Abstractive Approach
Authors: Grishma Sharma, Aditi Paretkar, Deepak Sharma
Date: 2024-07-29 00:00:00

The availability of a vast array of research papers in any area of study, necessitates the need of automated summarisation systems that can present the key research conducted and their corresponding findings. Scientific paper summarisation is a challenging task for various reasons including token length limits in modern transformer models and corresponding memory and compute requirements for long text. A significant amount of work has been conducted in this area, with approaches that modify the attention mechanisms of existing transformer models and others that utilise discourse information to capture long range dependencies in research papers. In this paper, we propose a hybrid methodology for research paper summarisation which incorporates an extractive and abstractive approach. We use the extractive approach to capture the key findings of research, and pair it with the introduction of the paper which captures the motivation for research. We use two models based on unsupervised learning for the extraction stage and two transformer language models, resulting in four combinations for our hybrid approach. The performances of the models are evaluated on three metrics and we present our findings in this paper. We find that using certain combinations of hyper parameters, it is possible for automated summarisation systems to exceed the abstractiveness of summaries written by humans. Finally, we state our future scope of research in extending this methodology to summarisation of generalised long documents.
Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios
Authors: Camilla Bignotti, Carolina Camassa
Date: 2024-07-29 00:00:00
Tasks: Decision Making

In this paper, we conduct an empirical analysis of how large language models (LLMs), specifically GPT-4, interpret constitutional principles in complex decision-making scenarios. We examine rulings from the Italian Constitutional Court on bioethics issues that involve trade-offs between competing values and compare model-generated legal arguments on these issues to those presented by the State, the Court, and the applicants. Our results indicate that GPT-4 consistently aligns more closely with progressive interpretations of the Constitution, often overlooking competing values and mirroring the applicants' views rather than the more conservative perspectives of the State or the Court's moderate positions. Our experiments reveal a distinct tendency of GPT-4 to favor progressive legal interpretations, underscoring the influence of underlying data biases. We thus underscore the importance of testing alignment in real-world scenarios and considering the implications of deploying LLMs in decision-making processes.
Do Text-to-Vis Benchmarks Test Real Use of Visualisations?
Authors: Hy Nguyen, Xuefei He, Andrew Reeson, Cecile Paris, Josiah Poon, Jonathan K. Kummerfeld
Date: 2024-07-29 00:00:00

Large language models are able to generate code for visualisations in response to simple user requests. This is a useful application and an appealing one for NLP research because plots of data provide grounding for language. However, there are relatively few benchmarks, and those that exist may not be representative of what users do in practice. This paper investigates whether benchmarks reflect real-world use through an empirical study comparing benchmark datasets with code from public repositories. Our findings reveal a substantial gap, with evaluations not testing the same distribution of chart types, attributes, and actions as real-world examples. One dataset is representative, but requires extensive modification to become a practical end-to-end benchmark. This shows that new benchmarks are needed to support the development of systems that truly address users' visualisation needs. These observations will guide future data creation, highlighting which features hold genuine significance for users.
Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classification
Authors: Wei Peng, Lei Cui, Wei Cai, Zhenquan Ding, Zhiyu Hao, Xiaochun Yun
Date: 2024-07-29 00:00:00
Tasks: Classification, Traffic Classification

Encrypted traffic classification is the task of identifying the application or service associated with encrypted network traffic. One effective approach for this task is to use deep learning methods to encode the raw traffic bytes directly and automatically extract features for classification (byte-based models). However, current byte-based models input raw traffic bytes, whether plaintext or encrypted text, for automated feature extraction, neglecting the distinct impacts of plaintext and encrypted text on downstream tasks. Additionally, these models primarily focus on improving classification accuracy, with little emphasis on the efficiency of models. In this paper, for the first time, we analyze the impact of plaintext and encrypted text on the model's effectiveness and efficiency. Based on our observations and findings, we propose a two-phase approach to balance the trade-off between plaintext and encrypted text in traffic classification. Specifically, Stage one is to Determine whether the Plain text is enough to be accurately Classified (DPC) using the proposed DPC Selector. This stage quickly identifies samples that can be classified using plaintext, leveraging explicit byte features in plaintext to enhance model's efficiency. Stage two aims to adaptively make a classification with the result from stage one. This stage incorporates encrypted text information for samples that cannot be classified using plaintext alone, ensuring the model's effectiveness on traffic classification tasks. Experiments on two datasets demonstrate that our proposed model achieves state-of-the-art results in both effectiveness and efficiency.
SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages
Authors: Wenxuan Zhang, Hou Pong Chan, Yiran Zhao, Mahani Aljunied, Jianyu Wang, Chaoqun Liu, Yue Deng, Zhiqiang Hu, Weiwen Xu, Yew Ken Chia, Xin Li, Lidong Bing
Date: 2024-07-29 00:00:00
Tasks: Diversity, Instruction Following, Mathematical Reasoning, World Knowledge

Large Language Models (LLMs) have shown remarkable abilities across various tasks, yet their development has predominantly centered on high-resource languages like English and Chinese, leaving low-resource languages underserved. To address this disparity, we present SeaLLMs 3, the latest iteration of the SeaLLMs model family, tailored for Southeast Asian languages. This region, characterized by its rich linguistic diversity, has lacked adequate language technology support. SeaLLMs 3 aims to bridge this gap by covering a comprehensive range of languages spoken in this region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese. Leveraging efficient language enhancement techniques and a specially constructed instruction tuning dataset, SeaLLMs 3 significantly reduces training costs while maintaining high performance and versatility. Our model excels in tasks such as world knowledge, mathematical reasoning, translation, and instruction following, achieving state-of-the-art performance among similarly sized models. Additionally, we prioritized safety and reliability by addressing both general and culture-specific considerations and incorporated mechanisms to reduce hallucinations. This work underscores the importance of inclusive AI, showing that advanced LLM capabilities can benefit underserved linguistic and cultural communities.
Overview of PerpectiveArg2024: The First Shared Task on Perspective Argument Retrieval
Authors: Neele Falk, Andreas Waldis, Iryna Gurevych
Date: 2024-07-29 00:00:00
Tasks: Argument Retrieval, Retrieval

Argument retrieval is the task of finding relevant arguments for a given query. While existing approaches rely solely on the semantic alignment of queries and arguments, this first shared task on perspective argument retrieval incorporates perspectives during retrieval, accounting for latent influences in argumentation. We present a novel multilingual dataset covering demographic and socio-cultural (socio) variables, such as age, gender, and political attitude, representing minority and majority groups in society. We distinguish between three scenarios to explore how retrieval systems consider explicitly (in both query and corpus) and implicitly (only in query) formulated perspectives. This paper provides an overview of this shared task and summarizes the results of the six submitted systems. We find substantial challenges in incorporating perspectivism, especially when aiming for personalization based solely on the text of arguments without explicitly providing socio profiles. Moreover, retrieval systems tend to be biased towards the majority group but partially mitigate bias for the female gender. While we bootstrap perspective argument retrieval, further research is essential to optimize retrieval systems to facilitate personalization and reduce polarization.
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
Authors: Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, Min Zhang
Date: 2024-07-29 00:00:00
Tasks: Contrastive Learning, Reranking, Retrieval, Text Retrieval, XLM-R

We present systematic efforts in building long-context multilingual text representation model (TRM) and reranker from scratch for text retrieval. We first introduce a text encoder (base size) enhanced with RoPE and unpadding, pre-trained in a native 8192-token context (longer than 512 of previous multilingual encoders). Then we construct a hybrid TRM and a cross-encoder reranker by contrastive learning. Evaluations show that our text encoder outperforms the same-sized previous state-of-the-art XLM-R. Meanwhile, our TRM and reranker match the performance of large-sized state-of-the-art BGE-M3 models and achieve better results on long-context retrieval benchmarks. Further analysis demonstrate that our proposed models exhibit higher efficiency during both training and inference. We believe their efficiency and effectiveness could benefit various researches and industrial applications.
From Pre-training Corpora to Large Language Models: What Factors Influence LLM Performance in Causal Discovery Tasks?
Authors: Tao Feng, Lizhen Qu, Niket Tandon, Zhuang Li, Xiaoxi Kang, Gholamreza Haffari
Date: 2024-07-29 00:00:00
Tasks: Causal Discovery

Recent advances in artificial intelligence have seen Large Language Models (LLMs) demonstrate notable proficiency in causal discovery tasks. This study explores the factors influencing the performance of LLMs in causal discovery tasks. Utilizing open-source LLMs, we examine how the frequency of causal relations within their pre-training corpora affects their ability to accurately respond to causal discovery queries. Our findings reveal that a higher frequency of causal mentions correlates with better model performance, suggesting that extensive exposure to causal information during training enhances the models' causal discovery capabilities. Additionally, we investigate the impact of context on the validity of causal relations. Our results indicate that LLMs might exhibit divergent predictions for identical causal relations when presented in different contexts. This paper provides the first comprehensive analysis of how different factors contribute to LLM performance in causal discovery tasks.