OpenRaiser/Intern-Atlas huggingface dataset:
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) are revolutionizing medical Question-Answering (medQA) through extensive use of medical literature. However, their performance is often hampered by outdated training data and a lack of explainability, which limits clinical applicability. This study aimed to create and assess UroBot, a urology-specialized chatbot, by comparing it with state-of-the-art models and the performance of urologists on urological board questions, ensuring full clinician-verifiability. UroBot was developed using OpenAI's GPT-3.5, GPT-4, and GPT-4o models, employing retrieval-augmented generation (RAG) and the latest 2023 guidelines from the European Association of Urology (EAU). The evaluation included ten runs of 200 European Board of Urology (EBU) In-Service Assessment (ISA) questions, with performance assessed by the mean Rate of Correct Answers (RoCA). UroBot-4o achieved an average RoCA of 88.4%, surpassing GPT-4o by 10.8%, with a score of 77.6%. It was also clinician-verifiable and exhibited the highest run agreement as indicated by Fleiss' Kappa (k = 0.979). By comparison, the average performance of urologists on board questions, as reported in the literature, is 68.7%. UroBot's clinician-verifiable nature and superior accuracy compared to both existing models and urologists on board questions highlight its potential for clinical integration. The study also provides the necessary code and instructions for further development of UroBot.
Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters. However, recent studies have identified side effects, such as knowledge distortion and the deterioration of general abilities, that have emerged after editing. Despite these findings, evaluating the pitfalls of knowledge editing often relies on inconsistent metrics and benchmarks, lacking a uniform standard. In response, this survey presents a comprehensive study of these side effects, providing a unified perspective on the challenges of knowledge editing in LLMs by conducting experiments with consistent metrics and benchmarks. Additionally, we review related works and outline potential research directions to address these limitations. Our survey highlights the limitations of current knowledge editing methods, emphasizing the need for a deeper understanding of the inner knowledge structures of LLMs and improved knowledge editing methods. To foster future research, we have released the complementary materials publicly in https://github.com/MiuLab/EditLLM-Survey.
Asynchronous Byzantine Federated Learning
Venue: arXiv cs.LG 2024
Year: 2024

Federated learning (FL) enables a set of geographically distributed clients to collectively train a model through a server. Classically, the training process is synchronous, but can be made asynchronous to maintain its speed in presence of slow clients and in heterogeneous networks. The vast majority of Byzantine fault-tolerant FL systems however rely on a synchronous training process. Our solution is one of the first Byzantine-resilient and asynchronous FL algorithms that does not require an auxiliary server dataset and is not delayed by stragglers, which are shortcomings of previous works. Intuitively, the server in our solution waits to receive a minimum number of updates from clients on its latest model to safely update it, and is later able to safely leverage the updates that late clients might send. We compare the performance of our solution with state-of-the-art algorithms on both image and text datasets under gradient inversion, perturbation, and backdoor attacks. Our results indicate that our solution trains a model faster than previous synchronous FL solution, and maintains a higher accuracy, up to 1.54x and up to 1.75x for perturbation and gradient inversion attacks respectively, in the presence of Byzantine clients than previous asynchronous FL solutions.
Experimental comparison of 5G SDR platforms: srsRAN x OpenAirInterface
Venue: arXiv cs.NI 2024
Year: 2024

A Software-Defined Radio (SDR) platform is a communication system that implements as software functions that are typically implemented in dedicated hardware. One of its main advantages is the flexibility to test and deploy radio communication networks in a fast and cheap way. In the context of the Fifth Generation (5G) of wireless cellular networks, there are open source SDR platforms available online. Two of the most popular SDR platforms are srsRAN and OpenAirInterface. This paper presents these two platforms, the characteristics of the networks created by them, the possibilities of changes in their interfaces and configurations, and also their limits. Moreover, in this paper, we also evaluate and compare both platforms in an experimental setup deployed in a laboratory.
Near-Field Beam Tracking with Extremely Large Dynamic Metasurface Antennas
Venue: arXiv cs.IT 2024
Year: 2024

The interplay between large antenna apertures and high frequencies in future generations of wireless networks will give rise to near-field communications. In this paper, we focus on the hybrid analog and digital beamforming architecture of dynamic metasurface antennas, which constitutes a recent prominent enabler of extremely massive antenna architectures, and devise a near-field beam tracking framework that initiates near-field beam sweeping only when the base station estimates that its provided beamforming gain drops below a threshold from its theoretically optimum value. Novel analytical expressions for the correlation function between any two beam focusing vectors, the beamforming gain with respect to user coordinate mismatch, the direction of the user movement yielding the fastest beamforming gain deterioration, and the minimum user displacement for a certain performance loss are presented. We also design a non-uniform coordinate grid for effectively sampling the user area of interest at each position estimation slot. Our extensive simulation results validate our theoretical analysis and showcase the superiority of the proposed near-field beam tracking over benchmarks.
Going Bananas! - Unfolding Program Synthesis with Origami
Venue: arXiv cs.PL 2024
Year: 2024

Automatically creating a computer program using input-output examples can be a challenging task, especially when trying to synthesize computer programs that require loops or recursion. Even though the use of recursion can make the algorithmic description more succinct and declarative, this concept creates additional barriers to program synthesis algorithms such as the creation and the (tentative) evaluation of non-terminating programs. One reason is that the recursive function must define how to traverse (or generate) the data structure and, at the same time, how to process it. In functional programming, the concept of recursion schemes decouples these two tasks by putting a major focus on the latter. This can also help to avoid some of the pitfalls of recursive functions during program synthesis, as argued in a previous work where we introduced the Origami technique. In our previous paper, we showed how this technique was effective in finding solutions for programs that require folding lists. In this work, we incorporate other recursion schemes into Origami, such as accumulated folding, unfolding, and the combination of unfolding and folding. We evaluated Origami on the 29 problems of the standard General Program Synthesis Benchmark Suite 1, obtaining favorable results against other well-known algorithms. Overall, Origami achieves the best result in 25% more problems than its predecessor (HOTGP) and an even higher increase when compared to other approaches. Not only that, but it can also consistently find a solution to problems that many algorithms report a low success rate.
Beyond symmetrization: effective adjacency matrices and renormalization for (un)singed directed graphs
Venue: arXiv cs.SI 2024
Year: 2024

To address the peculiarities of directed and/or signed graphs, new Laplacian operators have emerged. For instance, in the case of directionality, we encounter the magnetic operator, dilation (which is underexplored), operators based on random walks, and so forth. The definition of these new operators leads to the need for new studies and concepts, and consequently, the development of new computational tools. But is this really necessary? In this work, we define the concept of effective adjacency matrices that arise from the definition of deformed Laplacian operators such as magnetic, dilation, and signal. These effective matrices allow mapping generic graphs to a family of unsigned, undirected graphs, enabling the application of the well-explored toolkit of measures, machine learning methods, and renormalization groups of undirected graphs. To explore the interplay between deformed operators and effective matrices, we show how the Hodge-Helmholtz decomposition can assist us in navigating this complexity.
Polynomial Bounds of CFLOBDDs against BDDs
Venue: arXiv cs.SC 2024
Year: 2024

Binary Decision Diagrams (BDDs) are widely used for the representation of Boolean functions. Context-Free-Language Ordered Decision Diagrams (CFLOBDDs) are a plug-compatible replacement for BDDs -- roughly, they are BDDs augmented with a certain form of procedure call. A natural question to ask is, ``For a given family of Boolean functions $F$, what is the relationship between the size of a BDD for $f \in F$ and the size of a CFLOBDD for $f$?'' Sistla et al. established that there are best-case families of functions, which demonstrate an inherently exponential separation between CFLOBDDs and BDDs. They showed that there are families of functions $\{ f_n \}$ for which, for all $n = 2^k$, the CFLOBDD for $f_n$ (using a particular variable order) is exponentially more succinct than any BDD for $f_n$ (i.e., using any variable order). However, they did not give a worst-case bound -- i.e., they left open the question, ``Is there a family of functions $\{ g_i \}$ for which the size of a CFLOBDD for $g_i$ must be substantially larger than a BDD for $g_i$?'' For instance, it could be that there is a family of functions for which the BDDs are exponentially more succinct than any corresponding CFLOBDDs. This paper studies such questions, and answers the second question posed above in the negative. In particular, we show that by using the same variable ordering in the CFLOBDD that is used in the BDD, the size of a CFLOBDD for any function $h$ cannot be far worse than the size of the BDD for $h$. The bound that relates their sizes is polynomial: If BDD $B$ for function $h$ is of size $|B|$ and uses variable ordering $\textit{Ord}$, then the size of the CFLOBDD $C$ for $h$ that also uses $\textit{Ord}$ is bounded by $O(|B|^3)$. The paper also shows that the bound is tight: there is a family of functions for which $|C|$ grows as $Ω(|B|^3)$.
How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance
Venue: arXiv cs.LG 2024
Year: 2024

Chronic cough disorders are widespread and challenging to assess because they rely on subjective patient questionnaires about cough frequency. Wearable devices running Machine Learning (ML) algorithms are promising for quantifying daily coughs, providing clinicians with objective metrics to track symptoms and evaluate treatments. However, there is a mismatch between state-of-the-art metrics for cough counting algorithms and the information relevant to clinicians. Most works focus on distinguishing cough from non-cough samples, which does not directly provide clinically relevant outcomes such as the number of cough events or their temporal patterns. In addition, typical metrics such as specificity and accuracy can be biased by class imbalance. We propose using event-based evaluation metrics aligned with clinical guidelines on significant cough counting endpoints. We use an ML classifier to illustrate the shortcomings of traditional sample-based accuracy measurements, highlighting their variance due to dataset class imbalance and sample window length. We also present an open-source event-based evaluation framework to test algorithm performance in identifying cough events and rejecting false positives. We provide examples and best practice guidelines in event-based cough counting as a necessary first step to assess algorithm performance with clinical relevance.
Detecting Deceptive Dark Patterns in E-commerce Platforms
Venue: arXiv cs.IR 2024
Year: 2024

Dark patterns are deceptive user interfaces employed by e-commerce websites to manipulate user's behavior in a way that benefits the website, often unethically. This study investigates the detection of such dark patterns. Existing solutions include UIGuard, which uses computer vision and natural language processing, and approaches that categorize dark patterns based on detectability or utilize machine learning models trained on datasets. We propose combining web scraping techniques with fine-tuned BERT language models and generative capabilities to identify dark patterns, including outliers. The approach scrapes textual content, feeds it into the BERT model for detection, and leverages BERT's bidirectional analysis and generation abilities. The study builds upon research on automatically detecting and explaining dark patterns, aiming to raise awareness and protect consumers.
FinEmbedDiff: A Cost-Effective Approach of Classifying Financial Documents with Vector Sampling using Multi-modal Embedding Models
Venue: arXiv cs.IR 2024
Year: 2024

Accurate classification of multi-modal financial documents, containing text, tables, charts, and images, is crucial but challenging. Traditional text-based approaches often fail to capture the complex multi-modal nature of these documents. We propose FinEmbedDiff, a cost-effective vector sampling method that leverages pre-trained multi-modal embedding models to classify financial documents. Our approach generates multi-modal embedding vectors for documents, and compares new documents with pre-computed class embeddings using vector similarity measures. Evaluated on a large dataset, FinEmbedDiff achieves competitive classification accuracy compared to state-of-the-art baselines while significantly reducing computational costs. The method exhibits strong generalization capabilities, making it a practical and scalable solution for real-world financial applications.
An Analysis under a Unified Fomulation of Learning Algorithms with Output Constraints
Venue: arXiv cs.LG 2024
Year: 2024

Neural networks (NN) perform well in diverse tasks, but sometimes produce nonsensical results to humans. Most NN models "solely" learn from (input, output) pairs, occasionally conflicting with human knowledge. Many studies indicate injecting human knowledge by reducing output constraints during training can improve model performance and reduce constraint violations. While there have been several attempts to compare different existing algorithms under the same programming framework, nonetheless, there has been no previous work that categorizes learning algorithms with output constraints in a unified manner. Our contributions are as follows: (1) We categorize the previous studies based on three axes: type of constraint loss used (e.g. probabilistic soft logic, REINFORCE), exploration strategy of constraint-violating examples, and integration mechanism of learning signals from main task and constraint. (2) We propose new algorithms to integrate the information of main task and constraint injection, inspired by continual-learning algorithms. (3) Furthermore, we propose the $Hβ$-score as a metric for considering the main task metric and constraint violation simultaneously. To provide a thorough analysis, we examine all the algorithms on three NLP tasks: natural language inference (NLI), synthetic transduction examples (STE), and semantic role labeling (SRL). We explore and reveal the key factors of various algorithms associated with achieving high $Hβ$-scores.
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification
Venue: arXiv cs.LG 2024
Year: 2024

While distributed training is often viewed as a solution to optimizing linear models on increasingly large datasets, inter-machine communication costs of popular distributed approaches can dominate as data dimensionality increases. Recent work on non-interactive algorithms shows that approximate solutions for linear models can be obtained efficiently with only a single round of communication among machines. However, this approximation often degenerates as the number of machines increases. In this paper, building on the recent optimal weighted average method, we introduce a new technique, ACOWA, that allows an extra round of communication to achieve noticeably better approximation quality with minor runtime increases. Results show that for sparse distributed logistic regression, ACOWA obtains solutions that are more faithful to the empirical risk minimizer and attain substantially higher accuracy than other distributed algorithms.
TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications
Venue: arXiv cs.NI 2024
Year: 2024

Understanding telecom standards involves sorting through numerous technical documents, such as those produced by the 3rd Generation Partnership Project (3GPP), which is time-consuming and labor-intensive. While large language models (LLMs) can assist with the extensive 3GPP knowledge base, an inclusive dataset is crucial for their effective pre-training and fine-tuning. In this paper, we introduce \textit{TSpec-LLM}, an open-source comprehensive dataset covering all 3GPP documents from Release 8 to Release 19 (1999--2023). To evaluate its efficacy, we first select a representative sample of 3GPP documents, create corresponding technical questions, and assess the baseline performance of various LLMs. We then incorporate a retrieval-augmented generation (RAG) framework to enhance LLM capabilities by retrieving relevant context from the \textit{TSpec-LLM} dataset. Our evaluation shows that using a naive-RAG framework on \textit{TSpec-LLM} improves the accuracy of GPT-3.5, Gemini 1.0 Pro, and GPT-4 from 44\%, 46\%, and 51\% to 71\%, 75\%, and 72\%, respectively.
LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback
Venue: arXiv cs.CL 2024
Year: 2024

To democratize large language models (LLMs) to most natural languages, it is imperative to make these models capable of understanding and generating texts in many languages, in particular low-resource ones. While recent multilingual LLMs demonstrate remarkable performance in such capabilities, these LLMs still support a limited number of human languages due to the lack of training data for low-resource languages. Moreover, these LLMs are not yet aligned with human preference for downstream tasks, which is crucial for the success of LLMs in English. In this paper, we introduce xLLaMA-100 and xBLOOM-100 (collectively xLLMs-100), which scale the multilingual capabilities of LLaMA and BLOOM to 100 languages. To do so, we construct two datasets: a multilingual instruction dataset including 100 languages, which represents the largest language coverage to date, and a cross-lingual human feedback dataset encompassing 30 languages. We perform multilingual instruction tuning on the constructed instruction data and further align the LLMs with human feedback using the DPO algorithm on our cross-lingual human feedback dataset. We evaluate the multilingual understanding and generating capabilities of xLLMs-100 on five multilingual benchmarks. Experimental results show that xLLMs-100 consistently outperforms its peers across the benchmarks by considerable margins, defining a new state-of-the-art multilingual LLM that supports 100 languages.
In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs
Venue: arXiv cs.LG 2024
Year: 2024

Large language models manifest the ability of few-shot adaptation to a sequence of provided examples. This behavior, known as in-context learning, allows for performing nontrivial machine learning tasks during inference only. In this work, we address the question: can we leverage in-context learning to predict out-of-distribution materials properties? However, this would not be possible for structure property prediction tasks unless an effective method is found to pass atomic-level geometric features to the transformer model. To address this problem, we employ a compound model in which GPT-2 acts on the output of geometry-aware graph neural networks to adapt in-context information. To demonstrate our model's capabilities, we partition the QM9 dataset into sequences of molecules that share a common substructure and use them for in-context learning. This approach significantly improves the performance of the model on out-of-distribution examples, surpassing the one of general graph neural network models.
An Open Multilingual System for Scoring Readability of Wikipedia
Venue: arXiv cs.CL 2024
Year: 2024

With over 60M articles, Wikipedia has become the largest platform for open and freely accessible knowledge. While it has more than 15B monthly visits, its content is believed to be inaccessible to many readers due to the lack of readability of its text. However, previous investigations of the readability of Wikipedia have been restricted to English only, and there are currently no systems supporting the automatic readability assessment of the 300+ languages in Wikipedia. To bridge this gap, we develop a multilingual model to score the readability of Wikipedia articles. To train and evaluate this model, we create a novel multilingual dataset spanning 14 languages, by matching articles from Wikipedia to simplified Wikipedia and online children encyclopedias. We show that our model performs well in a zero-shot scenario, yielding a ranking accuracy of more than 80% across 14 languages and improving upon previous benchmarks. These results demonstrate the applicability of the model at scale for languages in which there is no ground-truth data available for model fine-tuning. Furthermore, we provide the first overview on the state of readability in Wikipedia beyond English.
Fruit Classification System with Deep Learning and Neural Architecture Search
Venue: arXiv cs.CV 2024
Year: 2024

The fruit identification process involves analyzing and categorizing different types of fruits based on their visual characteristics. This activity can be achieved using a range of methodologies, encompassing manual examination, conventional computer vision methodologies, and more sophisticated methodologies employing machine learning and deep learning. Our study identified a total of 15 distinct categories of fruit, consisting of class Avocado, Banana, Cherry, Apple Braeburn, Apple golden 1, Apricot, Grape, Kiwi, Mango, Orange, Papaya, Peach, Pineapple, Pomegranate and Strawberry. Neural Architecture Search (NAS) is a technological advancement employed within the realm of deep learning and artificial intelligence, to automate conceptualizing and refining neural network topologies. NAS aims to identify neural network structures that are highly suitable for tasks, such as the detection of fruits. Our suggested model with 99.98% mAP increased the detection performance of the preceding research study that used Fruit datasets. In addition, after the completion of the study, a comparative analysis was carried out to assess the findings in conjunction with those of another research that is connected to the topic. When compared to the findings of earlier studies, the detector that was proposed exhibited higher performance in terms of both its accuracy and its precision.