pwc-archive/papers-with-abstracts huggingface dataset:
Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference
Authors: Zachary Levonian, Chenglu Li, Wangda Zhu, Anoushka Gade, Owen Henkel, Millie-Ellen Postle, Wanli Xing
Date: 2023-10-04 00:00:00
Tasks: Math, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions of the tutoring process - including interactive QA to support conceptual discussion of mathematical concepts. However, LLM responses to math questions can be incorrect or mismatched to the educational context - such as being misaligned with a school's curriculum. One potential solution is retrieval-augmented generation (RAG), which involves incorporating a vetted external knowledge source in the LLM prompt to increase response quality. In this paper, we designed prompts that retrieve and use content from a high-quality open-source math textbook to generate responses to real student questions. We evaluate the efficacy of this RAG system for middle-school algebra and geometry QA by administering a multi-condition survey, finding that humans prefer responses generated using RAG, but not when responses are too grounded in the textbook content. We argue that while RAG is able to improve response quality, designers of math QA systems must consider trade-offs between generating responses preferred by students and responses closely matched to specific educational resources.
$\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis
Authors: Zishun Yu, Yunzhe Tao, Liyu Chen, Tao Sun, Hongxia Yang
Date: 2023-10-04 00:00:00
Tasks: Code Generation, Deep Reinforcement Learning, Program Synthesis, reinforcement-learning, Reinforcement Learning (RL)

Program synthesis aims to create accurate, executable programs from problem specifications, specifically from natural language descriptions in our context. Recent studies have leveraged the power of reinforcement learning (RL) in conjunction with large language models (LLMs), significantly enhancing code generation capabilities. The application of RL focuses on directly optimizing for functional correctness, offering an advantage over conventional supervised methods. Despite policy-based RL methods dominating the literature on RL for program synthesis, the nature of program synthesis tasks hints at a natural alignment with value-based methods. This stems from the rich collection of off-policy programs, including those developed by human programmers and also historical samples, coupled with the straightforward verification of generated programs through automated unit testing, meaning rewards are easy to obtain. Diverging from the dominant use of policy-based algorithms, our work explores the feasibility of value-based approaches, leading to the development of our $\mathcal{B}$-Coder (pronounced Bellman coder). Yet, training value-based methods presents challenges due to the enormous search space inherent to program synthesis. To this end, we introduce an initialization protocol for RL agents utilizing pre-trained LMs and a conservative Bellman operator to reduce training complexities. Moreover, we demonstrate how to leverage the learned value functions as a dual strategy to post-process generated programs. Our empirical evaluations demonstrated $\mathcal{B}$-Coder's capability in achieving state-of-the-art performance when compared to policy-based methods. Remarkably, this achievement is reached with minimal reward engineering effort, highlighting the effectiveness of value-based RL, independent of reward designs.
MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
Authors: Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, Yao Wan, Neil Zhenqiang Gong, Lichao Sun
Date: 2023-10-04 00:00:00
Tasks: Decision Making

Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the tool utilization ability of LLMs. They primarily investigated how LLMs effectively collaborate with given specific tools. However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests. Therefore, in this paper, we introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. Specifically, we create a dataset called ToolE within the benchmark. This dataset contains various types of user queries in the form of prompts that trigger LLMs to use tools, including both single-tool and multi-tool scenarios. Subsequently, we set the tasks for both tool usage awareness and tool selection. We define four subtasks from different perspectives in tool selection, including tool selection with similar choices, tool selection in specific scenarios, tool selection with possible reliability issues, and multi-tool selection. We conduct experiments involving eight popular LLMs and find that the majority of them still struggle to effectively select tools, highlighting the existing gaps between LLMs and genuine intelligent agents. However, through the error analysis, we found there is still significant room for improvement. Finally, we conclude with insights for tool developers -- we strongly recommend that tool developers choose an appropriate rewrite model for generating new descriptions based on the downstream LLM the tool will apply to. Our code is in https://github.com/HowieHwong/MetaTool.
Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks
Authors: Xilin Yang, Md Sadman Sakib Rahman, Bijie Bai, Jingxi Li, Aydogan Ozcan
Date: 2023-10-05 00:00:00

As an optical processor, a Diffractive Deep Neural Network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees-of-freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are non-negative, acting on diffraction-limited optical intensity patterns at the input field-of-view (FOV). Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.
Efficient Video and Audio processing with Loihi 2
Authors: Sumit Bam Shrestha, Jonathan Timcheck, Paxon Frady, Leobardo Campos-Macias, Mike Davies
Date: 2023-10-05 00:00:00
Tasks: Audio Denoising, Denoising

Loihi 2 is an asynchronous, brain-inspired research processor that generalizes several fundamental elements of neuromorphic architecture, such as stateful neuron models communicating with event-driven spikes, in order to address limitations of the first generation Loihi. Here we explore and characterize some of these generalizations, such as sigma-delta encapsulation, resonate-and-fire neurons, and integer-valued spikes, as applied to standard video, audio, and signal processing tasks. We find that these new neuromorphic approaches can provide orders of magnitude gains in combined efficiency and latency (energy-delay-product) for feed-forward and convolutional neural networks applied to video, audio denoising, and spectral transforms compared to state-of-the-art solutions.
Personalized Transformer-based Ranking for e-Commerce at Yandex
Authors: Kirill Khrylchenko, Alexander Fritzler
Date: 2023-10-05 00:00:00
Tasks: Retrieval

Personalizing user experience with high-quality recommendations based on user activity is vital for e-commerce platforms. This is particularly important in scenarios where the user's intent is not explicit, such as on the homepage. Recently, personalized embedding-based systems have significantly improved the quality of recommendations and search in the e-commerce domain. However, most of these works focus on enhancing the retrieval stage. In this paper, we demonstrate that features produced by retrieval-focused deep learning models are sub-optimal for ranking stage in e-commerce recommendations. To address this issue, we propose a two-stage training process that fine-tunes two-tower models to achieve optimal ranking performance. We provide a detailed description of our transformer-based two-tower model architecture, which is specifically designed for personalization in e-commerce. Additionally, we introduce a novel technique for debiasing context in offline models and report significant improvements in ranking performance when using web-search queries for e-commerce recommendations. Our model has been successfully deployed at Yandex, serves millions of users daily, and has delivered strong performance in online A/B testing.
Amazon Books Rating prediction & Recommendation Model
Authors: Hsiu-Ping Lin, Suman Chauhan, Yougender Chauhan, Nagender Chauhan, Jongwook Woo
Date: 2023-10-04 00:00:00
Tasks: Binary Classification, Classification, model, Prediction

This paper uses the dataset of Amazon to predict the books ratings listed on Amazon website. As part of this project, we predicted the ratings of the books, and also built a recommendation cluster. This recommendation cluster provides the recommended books based on the column's values from dataset, for instance, category, description, author, price, reviews etc. This paper provides a flow of handling big data files, data engineering, building models and providing predictions. The models predict book ratings column using various PySpark Machine Learning APIs. Additionally, we used hyper-parameters and parameters tuning. Also, Cross Validation and TrainValidationSplit were used for generalization. Finally, we performed a comparison between Binary Classification and Multiclass Classification in their accuracies. We converted our label from multiclass to binary to see if we could find any difference between the two classifications. As a result, we found out that we get higher accuracy in binary classification than in multiclass classification.
Impedance Leakage Vulnerability and its Utilization in Reverse-engineering Embedded Software
Authors: Md Sadik Awal, Md Tauhidur Rahman
Date: 2023-10-04 00:00:00

Discovering new vulnerabilities and implementing security and privacy measures are important to protect systems and data against physical attacks. One such vulnerability is impedance, an inherent property of a device that can be exploited to leak information through an unintended side channel, thereby posing significant security and privacy risks. Unlike traditional vulnerabilities, impedance is often overlooked or narrowly explored, as it is typically treated as a fixed value at a specific frequency in research and design endeavors. Moreover, impedance has never been explored as a source of information leakage. This paper demonstrates that the impedance of an embedded device is not constant and directly relates to the programs executed on the device. We define this phenomenon as impedance leakage and use this as a side channel to extract software instructions from protected memory. Our experiment on the ATmega328P microcontroller and the Artix 7 FPGA indicates that the impedance side channel can detect software instructions with 96.1% and 92.6% accuracy, respectively. Furthermore, we explore the dual nature of the impedance side channel, highlighting the potential for beneficial purposes and the associated risk of intellectual property theft. Finally, potential countermeasures that specifically address impedance leakage are discussed.
Context-Based Tweet Engagement Prediction
Authors: Jovan Jeromela
Date: 2023-09-28 00:00:00
Tasks: Feature Engineering, feature selection, Prediction, Recommendation Systems

Twitter is currently one of the biggest social media platforms. Its users may share, read, and engage with short posts called tweets. For the ACM Recommender Systems Conference 2020, Twitter published a dataset around 70 GB in size for the annual RecSys Challenge. In 2020, the RecSys Challenge invited participating teams to create models that would predict engagement likelihoods for given user-tweet combinations. The submitted models predicting like, reply, retweet, and quote engagements were evaluated based on two metrics: area under the precision-recall curve (PRAUC) and relative cross-entropy (RCE). In this diploma thesis, we used the RecSys 2020 Challenge dataset and evaluation procedure to investigate how well context alone may be used to predict tweet engagement likelihood. In doing so, we employed the Spark engine on TU Wien's Little Big Data Cluster to create scalable data preprocessing, feature engineering, feature selection, and machine learning pipelines. We manually created just under 200 additional features to describe tweet context. The results indicate that features describing users' prior engagement history and the popularity of hashtags and links in the tweet were the most informative. We also found that factors such as the prediction algorithm, training dataset size, training dataset sampling method, and feature selection significantly affect the results. After comparing the best results of our context-only prediction models with content-only models and with models developed by the Challenge winners, we identified that the context-based models underperformed in terms of the RCE score. This work thus concludes by situating this discrepancy and proposing potential improvements to our implementation, which is shared in a public git repository.
Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations
Authors: Phanideep Gampa, Farnoosh Javadi, Belhassen Bayar, Ainur Yessenalina
Date: 2023-09-25 00:00:00
Tasks: Multi-Task Learning, Recommendation Systems

Various data imbalances that naturally arise in a multi-territory personalized recommender system can lead to a significant item bias for globally prevalent items. A locally popular item can be overshadowed by a globally prevalent item. Moreover, users' viewership patterns/statistics can drastically change from one geographic location to another which may suggest to learn specific user embeddings. In this paper, we propose a multi-task learning (MTL) technique, along with an adaptive upsampling method to reduce popularity bias in multi-territory recommendations. Our proposed framework is designed to enrich training examples with active users representation through upsampling, and capable of learning geographic-based user embeddings by leveraging MTL. Through experiments, we demonstrate the effectiveness of our framework in multiple territories compared to a baseline not incorporating our proposed techniques.~Noticeably, we show improved relative gain of up to $65.27\%$ in PR-AUC metric. A case study is presented to demonstrate the advantages of our methods in attenuating the popularity bias of global items.
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
Authors: Cong Xu, Jun Wang, Jianyong Wang, Wei zhang
Date: 2023-09-24 00:00:00
Tasks: Decision Making, Graph Neural Network, Recommendation Systems

Embedding plays a key role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision-making models. In this paper, we propose a novel embedding update mechanism, Structure-aware Embedding Evolution (SEvo for short), to encourage related nodes to evolve similarly at each step. Unlike GNN (Graph Neural Network) that typically serves as an intermediate module, SEvo is able to directly inject graph structural information into embedding with minimal computational overhead during training. The convergence properties of SEvo along with its potential variants are theoretically analyzed to justify the validity of the designs. Moreover, SEvo can be seamlessly integrated into existing optimizers for state-of-the-art performance. Particularly SEvo-enhanced AdamW with moment estimate correction demonstrates consistent improvements across a spectrum of models and datasets, suggesting a novel technical route to effectively utilize graph structural information beyond explicit GNN modules.
DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality
Authors: Li Wang, Xin Wu, Yi Zhang, Xinyun Zhang, LianmingXu, Zhihua Wu, Aiguo Fei
Date: 2023-09-22 00:00:00
Proceeding: IEEE Journal of Selected Topics in Signal Processing 2023 9
Tasks: Collaborative Inference, Feature Compression, Object, Object Localization

The object inference for augmented reality (AR) requires a precise object localization within user's physical environment and the adaptability to dynamic communication conditions. Deep learning (DL) is advantageous in capturing highly-nonlinear features of diverse data sources drawn from complex objects. However, the existing DL techniques may have disfluency or instability issues when deployed on resource-constrained devices with poor communication conditions, resulting in bad user experiences. This paper addresses these issues by proposing a deep adaptive inference network called DeepAdaIn-Net for the real-time device-edge collaborative object inference, aiming at reducing feature transmission volume while ensuring high feature-fitting accuracy during inference. Specifically, DeepAdaIn-Net encompasses a partition point selection (PPS) module, a high feature compression learning (HFCL) module, a bandwidth-aware feature configuration (BaFC) module, and a feature consistency compensation (FCC) module. The PPS module minimizes the total execution latency, including inference and transmission latency. The HFCL and BaFC modules can decouple the training and inference process by integrating a high-compression ratio feature encoder with the bandwidth-aware feature configuration, which ensures that the compressed data can adapt to the varying communication bandwidths. The FCC module fills the information gaps among the compressed features, guaranteeing high feature expression ability. We conduct extensive experiments to validate DeepAdaIn-Net using two object inference datasets: COCO2017 and emergency fire datasets, and the results demonstrate that our approach outperforms several conventional methods by deriving an optimal 123x feature compression for 640* 640 images, which results in a mere 63.3 ms total latency and an accuracy loss of less than 3% when operating at a bandwidth of 16 Mbps.
Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction
Authors: Yuping Jin, Yanbin Yang and a Wei Liu
Date: 2022-09-22 00:00:00
Proceeding: Sustainability 2022 9
Tasks: Link Prediction

Unstable factors such as international relations, geopolitics, and transportation routes make natural gas trade complex and changeable. Diversified and flexible sources of liquefied natural gas (LNG) can guarantee the energy supply security of natural gas-consuming countries. Therefore, it is very important to find potential natural gas trade links to help the government find potential partners and prepare strategically in advance. In this paper, the global LNG network is taken as the research object. In order to fully consider the importance of nodes and the influence of economic and political factors, the “centrality degree” and “node attraction degree” are added into the link prediction algorithm, and multifactor coupling is carried out. The reliability of the improved algorithm is verified using the area under the curve (AUC) evaluation index, and the prediction results are analyzed. The results are as follows: Trinidad, Russia, Algeria, Nigeria, Angola, and Equatorial Guinea (Eq. Guinea) are more likely to establish new LNG trading relationships with other countries. For all potential trade relationships, potential relations involving the above countries are more likely to be realized within 5 years, while potential relations involving China, India, Japan, and South Korea are more likely to be realized within 2 years. China, India, and South Korea are more likely to import LNG from Algeria, and Taiwan Province is more likely to import LNG from Algeria, Angola, Eq. Guinea, and America. On the basis of the above study, states and governments can give priority to the above countries and regions when dealing with the possible LNG supply crisis.
First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures
Authors: Siying Liu, Vincent C. H. Leung, Pier Luigi Dragotti
Date: 2023-10-02 00:00:00
Proceeding: Frontiers in Neuroscience 2023 10
Tasks: Decision Making

Spiking neural networks (SNNs) are well-suited to process asynchronous event-based data. Most of the existing SNNs use rate-coding schemes that focus on firing rate (FR), and so they generally ignore the spike timing in events. On the contrary, methods based on temporal coding, particularly time-to-first-spike (TTFS) coding, can be accurate and efficient but they are difficult to train. Currently, there is limited research on applying TTFS coding to real events, since traditional TTFS-based methods impose one-spike constraint, which is not realistic for event-based data. In this study, we present a novel decision-making strategy based on first-spike (FS) coding that encodes FS timings of the output neurons to investigate the role of the first-spike timing in classifying real-world event sequences with complex temporal structures. To achieve FS coding, we propose a novel surrogate gradient learning method for discrete spike trains. In the forward pass, output spikes are encoded into discrete times to generate FS times. In the backpropagation, we develop an error assignment method that propagates error from FS times to spikes through a Gaussian window, and then supervised learning for spikes is implemented through a surrogate gradient approach. Additional strategies are introduced to facilitate the training of FS timings, such as adding empty sequences and employing different parameters for different layers. We make a comprehensive comparison between FS and FR coding in the experiments. Our results show that FS coding achieves comparable accuracy to FR coding while leading to superior energy efficiency and distinct neuronal dynamics on data sequences with very rich temporal structures. Additionally, a longer time delay in the first spike leads to higher accuracy, indicating important information is encoded in the timing of the first spike.