pwc-archive/papers-with-abstracts huggingface dataset:
A review on the use of large language models as virtual tutors
Authors: Silvia García-Méndez, Francisco de Arriba-Pérez, María del Carmen Somoza-López
Date: 2024-05-20 00:00:00
Tasks: Question Generation, Question-Generation

Transformer architectures contribute to managing long-term dependencies for Natural Language Processing, representing one of the most recent changes in the field. These architectures are the basis of the innovative, cutting-edge Large Language Models (LLMs) that have produced a huge buzz in several fields and industrial sectors, among the ones education stands out. Accordingly, these generative Artificial Intelligence-based solutions have directed the change in techniques and the evolution in educational methods and contents, along with network infrastructure, towards high-quality learning. Given the popularity of LLMs, this review seeks to provide a comprehensive overview of those solutions designed specifically to generate and evaluate educational materials and which involve students and teachers in their design or experimental plan. To the best of our knowledge, this is the first review of educational applications (e.g., student assessment) of LLMs. As expected, the most common role of these systems is as virtual tutors for automatic question generation. Moreover, the most popular models are GTP-3 and BERT. However, due to the continuous launch of new generative models, new works are expected to be published shortly.
Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space
Authors: Qianmei Liu, Yufei Kuang, Jie Wang
Date: 2024-05-20 00:00:00
Tasks: Deep Reinforcement Learning, MuJoCo

Deep reinforcement learning (DRL) algorithms can suffer from modeling errors between the simulation and the real world. Many studies use adversarial learning to generate perturbation during training process to model the discrepancy and improve the robustness of DRL. However, most of these approaches use a fixed parameter to control the intensity of the adversarial perturbation, which can lead to a trade-off between average performance and robustness. In fact, finding the optimal parameter of the perturbation is challenging, as excessive perturbations may destabilize training and compromise agent performance, while insufficient perturbations may not impart enough information to enhance robustness. To keep the training stable while improving robustness, we propose a simple but effective method, namely, Adaptive Adversarial Perturbation (A2P), which can dynamically select appropriate adversarial perturbations for each sample. Specifically, we propose an adaptive adversarial coefficient framework to adjust the effect of the adversarial perturbation during training. By designing a metric for the current intensity of the perturbation, our method can calculate the suitable perturbation levels based on the current relative performance. The appealing feature of our method is that it is simple to deploy in real-world applications and does not require accessing the simulator in advance. The experiments in MuJoCo show that our method can improve the training stability and learn a robust policy when migrated to different test environments. The code is available at https://github.com/Lqm00/A2P-SAC.
SM-DTW: Stability Modulated Dynamic Time Warping for signature verification
Authors: Antonio Parziale, Moises Diaz, Miguel A. Ferrer, Angelo Marcelli
Date: 2024-05-20 00:00:00
Tasks: Dynamic Time Warping

Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject's signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the similarity between a questioned signature and the reference ones during signature verification. We then introduce the Stability Modulated Dynamic Time Warping algorithm for incorporating the stability regions, i.e. the most similar parts between two signatures, into the distance measure between a pair of signatures computed by the Dynamic Time Warping for signature verification. Experiments were conducted on two datasets largely adopted for performance evaluation. Experimental results show that the proposed algorithm improves the performance of the baseline system and compares favourably with other top performing signature verification systems.
Conditional Shift-Robust Conformal Prediction for Graph Neural Network
Authors: S. Akansha
Date: 2024-05-20 00:00:00
Tasks: Conformal Prediction, Graph Neural Network, Node Classification, Prediction, Uncertainty Quantification

Graph Neural Networks (GNNs) have emerged as potent tools for predicting outcomes in graph-structured data. Despite their efficacy, a significant drawback of GNNs lies in their limited ability to provide robust uncertainty estimates, posing challenges to their reliability in contexts where errors carry significant consequences. Moreover, GNNs typically excel in in-distribution settings, assuming that training and test data follow identical distributions a condition often unmet in real world graph data scenarios. In this article, we leverage conformal prediction, a widely recognized statistical technique for quantifying uncertainty by transforming predictive model outputs into prediction sets, to address uncertainty quantification in GNN predictions amidst conditional shift\footnote{Representing the change in conditional probability distribution \(P(label|input)\) from source domain to target domain.} in graph-based semi-supervised learning (SSL). Additionally, we propose a novel loss function aimed at refining model predictions by minimizing conditional shift in latent stages. Termed Conditional Shift Robust (CondSR) conformal prediction for GNNs, our approach CondSR is model-agnostic and adaptable to various classification models. We validate the effectiveness of our method on standard graph benchmark datasets, integrating it with state-of-the-art GNNs in node classification tasks. Comprehensive evaluations demonstrate that our approach consistently achieves any predefined target marginal coverage, enhances the accuracy of state of the art GNN models by up to 12\% under conditional shift, and reduces the prediction set size by up to 48\%. The code implementation is publicly available for further exploration and experimentation.
Recommender Algorithm for Supporting Self-Management of CVD Risk Factors in an Adult Population at Home
Authors: Tatiana V. Afanasieva, Pavel V. Platov, Anastasia I. Medvedeva
Date: 2024-05-20 00:00:00
Tasks: Language Modelling, Large Language Model, Management

One of the new trends in the development of recommendation algorithms is the dissemination of their capabilities to support the population in managing their health. This article focuses on the problem of improving the effectiveness of cardiovascular diseases (CVD) prevention, since CVD is the leading cause of death worldwide. To address this issue, a knowledge-based recommendation algorithm was proposed to support self-management of CVD risk factors in adults at home. The proposed algorithm is based on the original multidimensional recommendation model and on a new user profile model, which includes predictive assessments of CVD health in addition to its current ones as outlined in official guidelines. The main feature of the proposed algorithm is the combination of rule-based logic with the capabilities of a large language model in generating human-like text for explanatory component of multidimensional recommendation. The verification and evaluation of the proposed algorithm showed the usefulness of the proposed recommendation algorithm for supporting adults in self-management of their CVD risk factors at home. As follows from the comparison with similar knowledge-based recommendation algorithms, the proposed algorithm evaluates a larger number of CVD risk factors and has a greater information and semantic capacity of the generated recommendations.
Chasing COMET: Leveraging Minimum Bayes Risk Decoding for Self-Improving Machine Translation
Authors: Kamil Guttmann, Mikołaj Pokrywka, Adrian Charkiewicz, Artur Nowakowski
Date: 2024-05-20 00:00:00
Tasks: Domain Adaptation, Machine Translation, Reranking, Translation

This paper explores Minimum Bayes Risk (MBR) decoding for self-improvement in machine translation (MT), particularly for domain adaptation and low-resource languages. We implement the self-improvement process by fine-tuning the model on its MBR-decoded forward translations. By employing COMET as the MBR utility metric, we aim to achieve the reranking of translations that better aligns with human preferences. The paper explores the iterative application of this approach and the potential need for language-specific MBR utility metrics. The results demonstrate significant enhancements in translation quality for all examined language pairs, including successful application to domain-adapted models and generalisation to low-resource settings. This highlights the potential of COMET-guided MBR for efficient MT self-improvement in various scenarios.
"Set It Up!": Functional Object Arrangement with Compositional Generative Models
Authors: Yiqing Xu, Jiayuan Mao, Yilun Du, Tomas Lozáno-Pérez, Leslie Pack Kaelbling, David Hsu
Date: 2024-05-20 00:00:00
Tasks: Object

This paper studies the challenge of developing robots capable of understanding under-specified instructions for creating functional object arrangements, such as "set up a dining table for two"; previous arrangement approaches have focused on much more explicit instructions, such as "put object A on the table." We introduce a framework, SetItUp, for learning to interpret under-specified instructions. SetItUp takes a small number of training examples and a human-crafted program sketch to uncover arrangement rules for specific scene types. By leveraging an intermediate graph-like representation of abstract spatial relationships among objects, SetItUp decomposes the arrangement problem into two subproblems: i) learning the arrangement patterns from limited data and ii) grounding these abstract relationships into object poses. SetItUp leverages large language models (LLMs) to propose the abstract spatial relationships among objects in novel scenes as the constraints to be satisfied; then, it composes a library of diffusion models associated with these abstract relationships to find object poses that satisfy the constraints. We validate our framework on a dataset comprising study desks, dining tables, and coffee tables, with the results showing superior performance in generating physically plausible, functional, and aesthetically pleasing object arrangements compared to existing models.
On Efficient and Statistical Quality Estimation for Data Annotation
Authors: Jan-Christoph Klie, Juan Haladjian, Marc Kirchner, Rahul Nair
Date: 2024-05-20 00:00:00

Annotated datasets are an essential ingredient to train, evaluate, compare and productionalize supervised machine learning models. It is therefore imperative that annotations are of high quality. For their creation, good quality management and thereby reliable quality estimates are needed. Then, if quality is insufficient during the annotation process, rectifying measures can be taken to improve it. Quality estimation is often performed by having experts manually label instances as correct or incorrect. But checking all annotated instances tends to be expensive. Therefore, in practice, usually only subsets are inspected; sizes are chosen mostly without justification or regard to statistical power and more often than not, are relatively small. Basing estimates on small sample sizes, however, can lead to imprecise values for the error rate. Using unnecessarily large sample sizes costs money that could be better spent, for instance on more annotations. Therefore, we first describe in detail how to use confidence intervals for finding the minimal sample size needed to estimate the annotation error rate. Then, we propose applying acceptance sampling as an alternative to error rate estimation We show that acceptance sampling can reduce the required sample sizes up to 50% while providing the same statistical guarantees.
Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning
Authors: Junghun Kim, Ka Hyun Park, Hoyoung Yoon, U Kang
Date: 2024-05-20 00:00:00
Tasks: Link Prediction

Given an edge-incomplete graph, how can we accurately find the missing links? The link prediction in edge-incomplete graphs aims to discover the missing relations between entities when their relationships are represented as a graph. Edge-incomplete graphs are prevalent in real-world due to practical limitations, such as not checking all users when adding friends in a social network. Addressing the problem is crucial for various tasks, including recommending friends in social networks and finding references in citation networks. However, previous approaches rely heavily on the given edge-incomplete (observed) graph, making it challenging to consider the missing (unobserved) links during training. In this paper, we propose PULL (PU-Learning-based Link predictor), an accurate link prediction method based on the positive-unlabeled (PU) learning. PULL treats the observed edges in the training graph as positive examples, and the unconnected node pairs as unlabeled ones. PULL effectively prevents the link predictor from overfitting to the observed graph by proposing latent variables for every edge, and leveraging the expected graph structure with respect to the variables. Extensive experiments on five real-world datasets show that PULL consistently outperforms the baselines for predicting links in edge-incomplete graphs.
Unveiling and Manipulating Prompt Influence in Large Language Models
Authors: Zijian Feng, Hanzhang Zhou, Zixiao Zhu, Junlang Qian, Kezhi Mao
Date: 2024-05-20 00:00:00
Tasks: Language Modelling, Text Generation

Prompts play a crucial role in guiding the responses of Large Language Models (LLMs). However, the intricate role of individual tokens in prompts, known as input saliency, in shaping the responses remains largely underexplored. Existing saliency methods either misalign with LLM generation objectives or rely heavily on linearity assumptions, leading to potential inaccuracies. To address this, we propose Token Distribution Dynamics (TDD), a \textcolor{black}{simple yet effective} approach to unveil and manipulate the role of prompts in generating LLM outputs. TDD leverages the robust interpreting capabilities of the language model head (LM head) to assess input saliency. It projects input tokens into the embedding space and then estimates their significance based on distribution dynamics over the vocabulary. We introduce three TDD variants: forward, backward, and bidirectional, each offering unique insights into token relevance. Extensive experiments reveal that the TDD surpasses state-of-the-art baselines with a big margin in elucidating the causal relationships between prompts and LLM outputs. Beyond mere interpretation, we apply TDD to two prompt manipulation tasks for controlled text generation: zero-shot toxic language suppression and sentiment steering. Empirical results underscore TDD's proficiency in identifying both toxic and sentimental cues in prompts, subsequently mitigating toxicity or modulating sentiment in the generated content.
Out-of-Distribution Detection with a Single Unconditional Diffusion Model
Authors: Alvin Heng, Alexandre H. Thiery, Harold Soh
Date: 2024-05-20 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Out-of-distribution (OOD) detection is a critical task in machine learning that seeks to identify abnormal samples. Traditionally, unsupervised methods utilize a deep generative model for OOD detection. However, such approaches require a new model to be trained for each inlier dataset. This paper explores whether a single model can perform OOD detection across diverse tasks. To that end, we introduce Diffusion Paths (DiffPath), which uses a single diffusion model originally trained to perform unconditional generation for OOD detection. We introduce a novel technique of measuring the rate-of-change and curvature of the diffusion paths connecting samples to the standard normal. Extensive experiments show that with a single model, DiffPath is competitive with prior work using individual models on a variety of OOD tasks involving different distributions. Our code is publicly available at https://github.com/clear-nus/diffpath.
Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs
Authors: Siyu Lou, Yuntian Chen, Xiaodan Liang, Liang Lin, Quanshi Zhang
Date: 2024-05-20 00:00:00
Tasks: Disentanglement, Language Modeling, Language Modelling, Large Language Model, Memorization, Text Generation

In this study, we propose an axiomatic system to define and quantify the precise memorization and in-context reasoning effects used by the large language model (LLM) for language generation. These effects are formulated as non-linear interactions between tokens/words encoded by the LLM. Specifically, the axiomatic system enables us to categorize the memorization effects into foundational memorization effects and chaotic memorization effects, and further classify in-context reasoning effects into enhanced inference patterns, eliminated inference patterns, and reversed inference patterns. Besides, the decomposed effects satisfy the sparsity property and the universal matching property, which mathematically guarantee that the LLM's confidence score can be faithfully decomposed into the memorization effects and in-context reasoning effects. Experiments show that the clear disentanglement of memorization effects and in-context reasoning effects enables a straightforward examination of detailed inference patterns encoded by LLMs.
A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus
Authors: Eduard Poesina, Cornelia Caragea, Radu Tudor Ionescu
Date: 2024-05-20 00:00:00
Tasks: Machine Translation, Natural Language Inference, Natural Language Understanding, Sentence, text-classification, Text Classification, Word Embeddings

Natural language inference (NLI), the task of recognizing the entailment relationship in sentence pairs, is an actively studied topic serving as a proxy for natural language understanding. Despite the relevance of the task in building conversational agents and improving text classification, machine translation and other NLP tasks, to the best of our knowledge, there is no publicly available NLI corpus for the Romanian language. To this end, we introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs, which are obtained via distant supervision, and 6K validation and test sentence pairs, which are manually annotated with the correct labels. We conduct experiments with multiple machine learning methods based on distant learning, ranging from shallow models based on word embeddings to transformer-based neural networks, to establish a set of competitive baselines. Furthermore, we improve on the best model by employing a new curriculum learning strategy based on data cartography. Our dataset and code to reproduce the baselines are available at https://github.com/Eduard6421/RONLI.
CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing
Authors: Yujiao Hu, Qingmin Jia, Jinchao Chen, Yuan YAO, Yan Pan, Renchao Xie, F. Richard Yu
Date: 2024-02-04 00:00:00
Tasks: Edge-computing, Scheduling

Multi-edge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multi-edge computing system particularly complicated. This paper first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Secondly, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS, is proposed. CoRaiS embeds the real-time states of multi-edge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that CoRaiS can make a high-quality scheduling decision in real time, and can be generalized to other multi-edge computing system, regardless of system scales. Characteristic validation also demonstrates that CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling.
Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process
Authors: Ermo Hua, Biqing Qi, Kaiyan Zhang, Yue Yu, Ning Ding, Xingtai Lv, Kai Tian, BoWen Zhou
Date: 2024-05-20 00:00:00

Supervised Fine-Tuning (SFT) and Preference Optimization (PO) are two fundamental processes for enhancing the capabilities of Language Models (LMs) post pre-training, aligning them better with human preferences. Although SFT advances in training efficiency, PO delivers better alignment, thus they are often combined. However, common practices simply apply them sequentially without integrating their optimization objectives, ignoring the opportunities to bridge their paradigm gap and take the strengths from both. To obtain a unified understanding, we interpret SFT and PO with two sub-processes -- Preference Estimation and Transition Optimization -- defined at token level within the Markov Decision Process (MDP) framework. This modeling shows that SFT is only a specialized case of PO with inferior estimation and optimization. PO evaluates the quality of model's entire generated answer, whereas SFT only scores predicted tokens based on preceding tokens from target answers. Therefore, SFT overestimates the ability of model, leading to inferior optimization. Building on this view, we introduce Intuitive Fine-Tuning (IFT) to integrate SFT and Preference Optimization into a single process. IFT captures LMs' intuitive sense of the entire answers through a temporal residual connection, but it solely relies on a single policy and the same volume of non-preference-labeled data as SFT. Our experiments show that IFT performs comparably or even superiorly to sequential recipes of SFT and some typical Preference Optimization methods across several tasks, particularly those requires generation, reasoning, and fact-following abilities. An explainable Frozen Lake game further validates the effectiveness of IFT for getting competitive policy.