pwc-archive/papers-with-abstracts huggingface dataset:
Drug Repurposing for Parkinson's Disease Using Random Walk With Restart Algorithm and the Parkinson's Disease Ontology Database
Authors: Pratham Kankariya, Rachita Rode, Kevin Mudaliar, Prof. Pranali Hatode
Date: 2024-04-11 00:00:00

Parkinson's disease is a progressive and slowly developing neurodegenerative disease, characterized by dopaminergic neuron loss in the substantia nigra region of the brain. Despite extensive research by scientists, there is not yet a cure to this problem and the available therapies mainly help to reduce some of the Parkinson's symptoms. Drug repurposing (that is, the process of finding new uses for existing drugs) receives more appraisals as an efficient way that allows for reducing the time, resources, and risks associated with the development of new drugs. In this research, we design a novel computational platform that integrates gene expression data, biological networks, and the PDOD database to identify possible drug-repositioning agents for PD therapy. By using machine learning approaches like the RWR algorithm and PDOD scoring system we arrange drug-disease conversions and sort our potential sandboxes according to their possible efficacy. We propose gene expression analysis, network prioritization, and drug target data analysis to arrive at a comprehensive evaluation of drug repurposing chances. Our study results highlight such therapies as promising drug candidates to conduct further research on PD treatment. We also provide the rationale for promising drug repurposing ideas by using various sources of data and computational approaches.
$F_β$-plot -- a visual tool for evaluating imbalanced data classifiers
Authors: Szymon Wojciechowski, Michał Woźniak
Date: 2024-04-11 00:00:00
Tasks: Classification

One of the significant problems associated with imbalanced data classification is the lack of reliable metrics. This runs primarily from the fact that for most real-life (as well as commonly used benchmark) problems, we do not have information from the user on the actual form of the loss function that should be minimized. Although it is pretty common to have metrics indicating the classification quality within each class, for the end user, the analysis of several such metrics is then required, which in practice causes difficulty in interpreting the usefulness of a given classifier. Hence, many aggregate metrics have been proposed or adopted for the imbalanced data classification problem, but there is still no consensus on which should be used. An additional disadvantage is their ambiguity and systematic bias toward one class. Moreover, their use in analyzing experimental results in recognition of those classification models that perform well for the chosen aggregated metrics is burdened with the drawbacks mentioned above. Hence, the paper proposes a simple approach to analyzing the popular parametric metric $F_\beta$. We point out that it is possible to indicate for a given pool of analyzed classifiers when a given model should be preferred depending on user requirements.
Multi-scale Topology Optimization using Neural Networks
Authors: Hongrui Chen, Xingchen Liu, Levent Burak Kara
Date: 2024-04-11 00:00:00

A long-standing challenge is designing multi-scale structures with good connectivity between cells while optimizing each cell to reach close to the theoretical performance limit. We propose a new method for direct multi-scale topology optimization using neural networks. Our approach focuses on inverse homogenization that seamlessly maintains compatibility across neighboring microstructure cells. Our approach consists of a topology neural network that optimizes the microstructure shape and distribution across the design domain as a continuous field. Each microstructure cell is optimized based on a specified elasticity tensor that also accommodates in-plane rotations. The neural network takes as input the local coordinates within a cell to represent the density distribution within a cell, as well as the global coordinates of each cell to design spatially varying microstructure cells. As such, our approach models an n-dimensional multi-scale optimization problem as a 2n-dimensional inverse homogenization problem using neural networks. During the inverse homogenization of each unit cell, we extend the boundary of each cell by scaling the input coordinates such that the boundaries of neighboring cells are combined. Inverse homogenization on the combined cell improves connectivity. We demonstrate our method through the design and optimization of graded multi-scale structures.
CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes
Authors: Haokun Zhao, Haixia Han, Jie Shi, Chengyu Du, Jiaqing Liang, Yanghua Xiao
Date: 2024-04-11 00:00:00
Tasks: Continual Learning, Continual Pretraining, Language Modelling, Large Language Model, Specificity, World Knowledge

As world knowledge advances and new task schemas emerge, Continual Learning (CL) becomes essential for keeping Large Language Models (LLMs) current and addressing their shortcomings. This process typically involves continual instruction tuning (CIT) and continual pre-training (CPT) to enable these models to adapt to novel tasks and acquire critical knowledge. However, collecting sufficient CPT data and efficiently bridging knowledge gaps remain significant challenges. Inspired by the 'summarizing mistakes' strategy, we propose the Continue Evolving from Mistakes (CEM) method, a data-efficient approach aiming to collect CPT data and continually improve LLMs' performance through iterative evaluation and supplementation with mistake-relevant knowledge. To further optimize data usage and mitigate forgetting, we introduce a novel training paradigm that combines CIT and CPT. Experiments show that CEM substantially enhances multiple models' performance on both in-domain and out-of-domain QA tasks, achieving gains of up to 29.63%. Code and datasets are available on https://anonymous.4open.science/r/cem-BB25.
If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level
Authors: Matti Wiegmann, Jennifer Rakete, Magdalena Wolska, Benno Stein, Martin Potthast
Date: 2024-04-15 00:00:00

Trigger warnings are labels that preface documents with sensitive content if this content could be perceived as harmful by certain groups of readers. Since warnings about a document intuitively need to be shown before reading it, authors usually assign trigger warnings at the document level. What parts of their writing prompted them to assign a warning, however, remains unclear. We investigate for the first time the feasibility of identifying the triggering passages of a document, both manually and computationally. We create a dataset of 4,135 English passages, each annotated with one of eight common trigger warnings. In a large-scale evaluation, we then systematically evaluate the effectiveness of fine-tuned and few-shot classifiers, and their generalizability. We find that trigger annotation belongs to the group of subjective annotation tasks in NLP, and that automatic trigger classification remains challenging but feasible.
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction
Authors: Zepeng Ding, Wenhao Huang, Jiaqing Liang, Deqing Yang, Yanghua Xiao
Date: 2024-04-15 00:00:00
Tasks: Few-Shot Learning

Relation triple extraction, which outputs a set of triples from long sentences, plays a vital role in knowledge acquisition. Large language models can accurately extract triples from simple sentences through few-shot learning or fine-tuning when given appropriate instructions. However, they often miss out when extracting from complex sentences. In this paper, we design an evaluation-filtering framework that integrates large language models with small models for relational triple extraction tasks. The framework includes an evaluation model that can extract related entity pairs with high precision. We propose a simple labeling principle and a deep neural network to build the model, embedding the outputs as prompts into the extraction process of the large model. We conduct extensive experiments to demonstrate that the proposed method can assist large language models in obtaining more accurate extraction results, especially from complex sentences containing multiple relational triples. Our evaluation model can also be embedded into traditional extraction models to enhance their extraction precision from complex sentences.
Bridging the Gap between Different Vocabularies for LLM Ensemble
Authors: Yangyifan Xu, Jinliang Lu, Jiajun Zhang
Date: 2024-04-15 00:00:00
Tasks: Arithmetic Reasoning, Data-to-Text Generation, Machine Translation, Text Generation

Ensembling different large language models (LLMs) to unleash their complementary potential and harness their individual strengths is highly valuable. Nevertheless, vocabulary discrepancies among various LLMs have constrained previous studies to either selecting or blending completely generated outputs. This limitation hinders the dynamic correction and enhancement of outputs during the generation process, resulting in a limited capacity for effective ensemble. To address this issue, we propose a novel method to Ensemble LLMs via Vocabulary Alignment (EVA). EVA bridges the lexical gap among various LLMs, enabling meticulous ensemble at each generation step. Specifically, we first learn mappings between the vocabularies of different LLMs with the assistance of overlapping tokens. Subsequently, these mappings are employed to project output distributions of LLMs into a unified space, facilitating a fine-grained ensemble. Finally, we design a filtering strategy to exclude models that generate unfaithful tokens. Experimental results on commonsense reasoning, arithmetic reasoning, machine translation, and data-to-text generation tasks demonstrate the superiority of our approach compared with individual LLMs and previous ensemble methods conducted on complete outputs. Further analyses confirm that our approach can leverage knowledge from different language models and yield consistent improvement.
Automatic Knowledge Graph Construction for Judicial Cases
Authors: Jie zhou, Xin Chen, Hang Zhang, Zhe Li
Date: 2024-04-15 00:00:00
Tasks: graph construction, Knowledge Graphs

In this paper, we explore the application of cognitive intelligence in legal knowledge, focusing on the development of judicial artificial intelligence. Utilizing natural language processing (NLP) as the core technology, we propose a method for the automatic construction of case knowledge graphs for judicial cases. Our approach centers on two fundamental NLP tasks: entity recognition and relationship extraction. We compare two pre-trained models for entity recognition to establish their efficacy. Additionally, we introduce a multi-task semantic relationship extraction model that incorporates translational embedding, leading to a nuanced contextualized case knowledge representation. Specifically, in a case study involving a "Motor Vehicle Traffic Accident Liability Dispute," our approach significantly outperforms the baseline model. The entity recognition F1 score improved by 0.36, while the relationship extraction F1 score increased by 2.37. Building on these results, we detail the automatic construction process of case knowledge graphs for judicial cases, enabling the assembly of knowledge graphs for hundreds of thousands of judgments. This framework provides robust semantic support for applications of judicial AI, including the precise categorization and recommendation of related cases.
A Large-Scale Evaluation of Speech Foundation Models
Authors: Shu-wen Yang, Heng-Jui Chang, Zili Huang, Andy T. Liu, Cheng-I Lai, Haibin Wu, Jiatong Shi, Xuankai Chang, Hsiang-Sheng Tsai, Wen-Chin Huang, Tzu-hsun Feng, Po-Han Chi, Yist Y. Lin, Yung-Sung Chuang, Tzu-Hsien Huang, Wei-Cheng Tseng, Kushal Lakhotia, Shang-Wen Li, Abdelrahman Mohamed, Shinji Watanabe, Hung-Yi Lee
Date: 2024-04-15 00:00:00
Tasks: Benchmarking

The foundation model paradigm leverages a shared foundation model to achieve state-of-the-art (SOTA) performance for various tasks, requiring minimal downstream-specific modeling and data annotation. This approach has proven crucial in the field of Natural Language Processing (NLP). However, the speech processing community lacks a similar setup to explore the paradigm systematically. In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech. We propose a unified multi-tasking framework to address speech processing tasks in SUPERB using a frozen foundation model followed by task-specialized, lightweight prediction heads. Combining our results with community submissions, we verify that the foundation model paradigm is promising for speech, and our multi-tasking framework is simple yet effective, as the best-performing foundation model shows competitive generalizability across most SUPERB tasks. For reproducibility and extensibility, we have developed a long-term maintained platform that enables deterministic benchmarking, allows for result sharing via an online leaderboard, and promotes collaboration through a community-driven benchmark database to support new development cycles. Finally, we conduct a series of analyses to offer an in-depth understanding of SUPERB and speech foundation models, including information flows across tasks inside the models, the correctness of the weighted-sum benchmarking protocol and the statistical significance and robustness of the benchmark.
Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields
Authors: Ryan Cotterell, Kevin Duh
Date: 2024-04-14 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Low Resource Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Transfer Learning

Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world's languages, it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low resource languages jointly. Learning character representations for multiple related languages allows transfer among the languages, improving F1 by up to 9.8 points over a loglinear CRF baseline.
Deceptive Patterns of Intelligent and Interactive Writing Assistants
Authors: Karim Benharrak, Tim Zindulka, Daniel Buschek
Date: 2024-04-14 00:00:00
Tasks: Chatbot

Large Language Models have become an integral part of new intelligent and interactive writing assistants. Many are offered commercially with a chatbot-like UI, such as ChatGPT, and provide little information about their inner workings. This makes this new type of widespread system a potential target for deceptive design patterns. For example, such assistants might exploit hidden costs by providing guidance up until a certain point before asking for a fee to see the rest. As another example, they might sneak unwanted content/edits into longer generated or revised text pieces (e.g. to influence the expressed opinion). With these and other examples, we conceptually transfer several deceptive patterns from the literature to the new context of AI writing assistants. Our goal is to raise awareness and encourage future research into how the UI and interaction design of such systems can impact people and their writing.
The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation
Authors: Zoey Liu, Bonnie J. Dorr
Date: 2024-04-14 00:00:00
Tasks: Diversity

Recent work to enhance data partitioning strategies for more realistic model evaluation face challenges in providing a clear optimal choice. This study addresses these challenges, focusing on morphological segmentation and synthesizing limitations related to language diversity, adoption of multiple datasets and splits, and detailed model comparisons. Our study leverages data from 19 languages, including ten indigenous or endangered languages across 10 language families with diverse morphological systems (polysynthetic, fusional, and agglutinative) and different degrees of data availability. We conduct large-scale experimentation with varying sized combinations of training and evaluation sets as well as new test data. Our results show that, when faced with new test data: (1) models trained from random splits are able to achieve higher numerical scores; (2) model rankings derived from random splits tend to generalize more consistently.
Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models
Authors: Souvik Das, Lifeng Jin, Linfeng Song, Haitao Mi, Baolin Peng, Dong Yu
Date: 2024-04-14 00:00:00
Tasks: Hallucination

Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination -- generating content ungrounded in the realities of training data. Recent work has focused on decoding techniques to improve factuality during inference by leveraging LLMs' hierarchical representation of factual knowledge, manipulating the predicted distributions at inference time. Current state-of-the-art approaches refine decoding by contrasting early-exit distributions from a lower layer with the final layer to exploit information related to factuality within the model forward procedure. However, such methods often assume the final layer is the most reliable and the lower layer selection process depends on it. In this work, we first propose extrapolation of critical token probabilities beyond the last layer for more accurate contrasting. We additionally employ layer-wise entropy-guided lower layer selection, decoupling the selection process from the final layer. Experiments demonstrate strong performance - surpassing state-of-the-art on multiple different datasets by large margins. Analyses show different kinds of prompts respond to different selection strategies.
Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments
Authors: Carlos Carrasco-Farre
Date: 2024-04-14 00:00:00
Tasks: Persuasion Strategies

Large Language Models (LLMs) are already as persuasive as humans. However, we know very little about how they do it. This paper investigates the persuasion strategies of LLMs, comparing them with human-generated arguments. Using a dataset of 1,251 participants in an experiment, we analyze the persuasion strategies of LLM-generated and human-generated arguments using measures of cognitive effort (lexical and grammatical complexity) and moral-emotional language (sentiment and moral analysis). The study reveals that LLMs produce arguments that require higher cognitive effort, exhibiting more complex grammatical and lexical structures than human counterparts. Additionally, LLMs demonstrate a significant propensity to engage more deeply with moral language, utilizing both positive and negative moral foundations more frequently than humans. In contrast with previous research, no significant difference was found in the emotional content produced by LLMs and humans. These findings contribute to the discourse on AI and persuasion, highlighting the dual potential of LLMs to both enhance and undermine informational integrity through communication strategies for digital persuasion.
Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora
Authors: Dror K. Markus, Effi Levi, Tamir Sheafer, Shaul R. Shenhav
Date: 2024-04-14 00:00:00
Tasks: Anomaly Detection, Articles, Unsupervised Anomaly Detection

Media Storms, dramatic outbursts of attention to a story, are central components of media dynamics and the attention landscape. Despite their significance, there has been little systematic and empirical research on this concept due to issues of measurement and operationalization. We introduce an iterative human-in-the-loop method to identify media storms in a large-scale corpus of news articles. The text is first transformed into signals of dispersion based on several textual characteristics. In each iteration, we apply unsupervised anomaly detection to these signals; each anomaly is then validated by an expert to confirm the presence of a storm, and those results are then used to tune the anomaly detection in the next iteration. We demonstrate the applicability of this method in two scenarios: first, supplementing an initial list of media storms within a specific time frame; and second, detecting media storms in new time periods. We make available a media storm dataset compiled using both scenarios. Both the method and dataset offer the basis for comprehensive empirical research into the concept of media storms, including characterizing them and predicting their outbursts and durations, in mainstream media or social media platforms.
Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT
Authors: Tuan Bui, Oanh Tran, Phuong Nguyen, Bao Ho, Long Nguyen, Thang Bui, Tho Quan
Date: 2024-04-14 00:00:00
Tasks: graph construction, Knowledge Graphs, Question Answering, RAG, Retrieval-augmented Generation

In today's rapidly evolving landscape of Artificial Intelligence, large language models (LLMs) have emerged as a vibrant research topic. LLMs find applications in various fields and contribute significantly. Despite their powerful language capabilities, similar to pre-trained language models (PLMs), LLMs still face challenges in remembering events, incorporating new information, and addressing domain-specific issues or hallucinations. To overcome these limitations, researchers have proposed Retrieval-Augmented Generation (RAG) techniques, some others have proposed the integration of LLMs with Knowledge Graphs (KGs) to provide factual context, thereby improving performance and delivering more accurate feedback to user queries. Education plays a crucial role in human development and progress. With the technology transformation, traditional education is being replaced by digital or blended education. Therefore, educational data in the digital environment is increasing day by day. Data in higher education institutions are diverse, comprising various sources such as unstructured/structured text, relational databases, web/app-based API access, etc. Constructing a Knowledge Graph from these cross-data sources is not a simple task. This article proposes a method for automatically constructing a Knowledge Graph from multiple data sources and discusses some initial applications (experimental trials) of KG in conjunction with LLMs for question-answering tasks.
JaFIn: Japanese Financial Instruction Dataset
Authors: Kota Tanabe, Masahiro Suzuki, Hiroki Sakaji, Itsuki Noda
Date: 2024-04-14 00:00:00
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Large Language Model

We construct an instruction dataset for the large language model (LLM) in the Japanese finance domain. Domain adaptation of language models, including LLMs, is receiving more attention as language models become more popular. This study demonstrates the effectiveness of domain adaptation through instruction tuning. To achieve this, we propose an instruction tuning data in Japanese called JaFIn, the Japanese Financial Instruction Dataset. JaFIn is manually constructed based on multiple data sources, including Japanese government websites, which provide extensive financial knowledge. We then utilize JaFIn to apply instruction tuning for several LLMs, demonstrating that our models specialized in finance have better domain adaptability than the original models. The financial-specialized LLMs created were evaluated using a quantitative Japanese financial benchmark and qualitative response comparisons, showing improved performance over the originals.