pwc-archive/papers-with-abstracts huggingface dataset:
Optimal convex $M$-estimation via score matching
Authors: Oliver Y. Feng, Yu-Chun Kao, Min Xu, Richard J. Samworth
Date: 2024-03-25 00:00:00
Tasks: regression

In the context of linear regression, we construct a data-driven convex loss function with respect to which empirical risk minimisation yields optimal asymptotic variance in the downstream estimation of the regression coefficients. At the population level, the negative derivative of the optimal convex loss is the best decreasing approximation of the derivative of the log-density of the noise distribution. This motivates a fitting process via a nonparametric extension of score matching, corresponding to a log-concave projection of the noise distribution with respect to the Fisher divergence. At the sample level, our semiparametric estimator is computationally efficient, and we prove that it attains the minimal asymptotic covariance among all convex $M$-estimators. As an example of a non-log-concave setting, the optimal convex loss function for Cauchy errors is Huber-like, and our procedure yields asymptotic efficiency greater than $0.87$ relative to the maximum likelihood estimator of the regression coefficients that uses oracle knowledge of this error distribution. In this sense, we provide robustness and facilitate computation without sacrificing much statistical efficiency. Numerical experiments using our accompanying R package 'asm' confirm the practical merits of our proposal.
Integrated path stability selection
Authors: Omar Melikechi, Jeffrey W. Miller
Date: 2024-03-23 00:00:00
Tasks: feature selection

Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of false positives, E(FP), enabling control of false positives in practice. However, stability selection often selects very few features, resulting in low sensitivity. This is because existing bounds on E(FP) are relatively loose, causing stability selection to overestimate the number of false positives. In this paper, we introduce a novel approach to stability selection based on integrating stability paths rather than maximizing over them. This yields upper bounds on E(FP) that are orders of magnitude stronger than previous bounds, leading to significantly more true positives in practice for the same target E(FP). Furthermore, our method takes the same amount of computation as the original stability selection algorithm, and only requires one user-specified parameter, which can be either the target E(FP) or target false discovery rate. We demonstrate the method on simulations and real data from prostate and colon cancer studies.
Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models
Authors: Yunjia Xi, Weiwen Liu, Jianghao Lin, Chuhan Wu, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
Date: 2024-03-25 00:00:00
Tasks: Language Modelling, Large Language Model, Recommendation Systems

The rise of large language models (LLMs) has opened new opportunities in Recommender Systems (RSs) by enhancing user behavior modeling and content understanding. However, current approaches that integrate LLMs into RSs solely utilize either LLM or conventional recommender model (CRM) to generate final recommendations, without considering which data segments LLM or CRM excel in. To fill in this gap, we conduct experiments on MovieLens-1M and Amazon-Books datasets, and compare the performance of a representative CRM (DCNv2) and an LLM (LLaMA2-7B) on various groups of data samples. Our findings reveal that LLMs excel in data segments where CRMs exhibit lower confidence and precision, while samples where CRM excels are relatively challenging for LLM, requiring substantial training data and a long training time for comparable performance. This suggests potential synergies in the combination between LLM and CRM. Motivated by these insights, we propose Collaborative Recommendation with conventional Recommender and Large Language Model (dubbed \textit{CoReLLa}). In this framework, we first jointly train LLM and CRM and address the issue of decision boundary shifts through alignment loss. Then, the resource-efficient CRM, with a shorter inference time, handles simple and moderate samples, while LLM processes the small subset of challenging samples for CRM. Our experimental results demonstrate that CoReLLa outperforms state-of-the-art CRM and LLM methods significantly, underscoring its effectiveness in recommendation tasks.
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
Authors: Jiyuan Yang, Yuanzi Li, Jingyu Zhao, Hanbing Wang, Muyang Ma, Jun Ma, Zhaochun Ren, Mengqi Zhang, Xin Xin, Zhumin Chen, Pengjie Ren
Date: 2024-03-25 00:00:00
Tasks: 2k, Mamba, Sequential Recommendation

Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions. With users increasingly engaging with online platforms, vast amounts of lifelong user behavioral sequences have been generated. However, existing sequential recommender models often struggle to handle such lifelong sequences. The primary challenges stem from computational complexity and the ability to capture long-range dependencies within the sequence. Recently, a state space model featuring a selective mechanism (i.e., Mamba) has emerged. In this work, we investigate the performance of Mamba for lifelong sequential recommendation (i.e., length>=2k). More specifically, we leverage the Mamba block to model lifelong user sequences selectively. We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences. Experiments on two real-world datasets demonstrate the superiority of Mamba. We found that RecMamba achieves performance comparable to the representative model while significantly reducing training duration by approximately 70% and memory costs by 80%. Codes and data are available at \url{https://github.com/nancheng58/RecMamba}.
Ultra Low-Cost Two-Stage Multimodal System for Non-Normative Behavior Detection
Authors: Albert Lu, Stephen Cranefield
Date: 2024-03-24 00:00:00

The online community has increasingly been inundated by a toxic wave of harmful comments. In response to this growing challenge, we introduce a two-stage ultra-low-cost multimodal harmful behavior detection method designed to identify harmful comments and images with high precision and recall rates. We first utilize the CLIP-ViT model to transform tweets and images into embeddings, effectively capturing the intricate interplay of semantic meaning and subtle contextual clues within texts and images. Then in the second stage, the system feeds these embeddings into a conventional machine learning classifier like SVM or logistic regression, enabling the system to be trained rapidly and to perform inference at an ultra-low cost. By converting tweets into rich multimodal embeddings through the CLIP-ViT model and utilizing them to train conventional machine learning classifiers, our system is not only capable of detecting harmful textual information with near-perfect performance, achieving precision and recall rates above 99\% but also demonstrates the ability to zero-shot harmful images without additional training, thanks to its multimodal embedding input. This capability empowers our system to identify unseen harmful images without requiring extensive and costly image datasets. Additionally, our system quickly adapts to new harmful content; if a new harmful content pattern is identified, we can fine-tune the classifier with the corresponding tweets' embeddings to promptly update the system. This makes it well suited to addressing the ever-evolving nature of online harmfulness, providing online communities with a robust, generalizable, and cost-effective tool to safeguard their communities.
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
Authors: Maria Heuss, Maarten de Rijke, Avishek Anand
Date: 2024-03-24 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, valid

While SHAP (SHapley Additive exPlanations) and other feature attribution methods are commonly employed to explain model predictions, their application within information retrieval (IR), particularly for complex outputs such as ranked lists, remains limited. Existing attribution methods typically provide pointwise explanations, focusing on why a single document received a high-ranking score, rather than considering the relationships between documents in a ranked list. We present three key contributions to address this gap. First, we rigorously define listwise feature attribution for ranking models. Secondly, we introduce RankingSHAP, extending the popular SHAP framework to accommodate listwise ranking attribution, addressing a significant methodological gap in the field. Third, we propose two novel evaluation paradigms for assessing the faithfulness of attributions in learning-to-rank models, measuring the correctness and completeness of the explanation with respect to different aspects. Through experiments on standard learning-to-rank datasets, we demonstrate RankingSHAP's practical application while identifying the constraints of selection-based explanations. We further employ a simulated study with an interpretable model to showcase how listwise ranking attributions can be used to examine model decisions and conduct a qualitative evaluation of explanations. Due to the contrastive nature of the ranking task, our understanding of ranking model decisions can substantially benefit from feature attribution explanations like RankingSHAP.
Knowledge-aware Dual-side Attribute-enhanced Recommendation
Authors: Taotian Pang, Xingyu Lou, Fei Zhao, Zhen Wu, Kuiyao Dong, Qiuying Peng, Yue Qi, Xinyu Dai
Date: 2024-03-24 00:00:00
Tasks: Attribute, Collaborative Filtering, Contrastive Learning, Knowledge-Aware Recommendation, Knowledge Graphs

\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. However, they fall short in modeling fine-grained user preferences and further fail to leverage the \textit{preference-attribute connection} to make predictions, leading to sub-optimal performance. To address the issue, we propose a method named \textit{\textbf{K}nowledge-aware \textbf{D}ual-side \textbf{A}ttribute-enhanced \textbf{R}ecommendation} (KDAR). Specifically, we build \textit{user preference representations} and \textit{attribute fusion representations} upon the attribute information in knowledge graphs, which are utilized to enhance \textit{collaborative filtering} (CF) based user and item representations, respectively. To discriminate the contribution of each attribute in these two types of attribute-based representations, a \textit{multi-level collaborative alignment contrasting} mechanism is proposed to align the importance of attributes with CF signals. Experimental results on four benchmark datasets demonstrate the superiority of KDAR over several state-of-the-art baselines. Further analyses verify the effectiveness of our method. The code of KDAR is released at: \href{https://github.com/TJTP/KDAR}{https://github.com/TJTP/KDAR}.
Model, Analyze, and Comprehend User Interactions within a Social Media Platform
Authors: Md Kaykobad Reza, S M Maksudul Alam, Yiran Luo, Youzhe Liu, Md Siam
Date: 2024-03-23 00:00:00

In this study, we propose a novel graph-based approach to model, analyze and comprehend user interactions within a social media platform based on post-comment relationship. We construct a user interaction graph from social media data and analyze it to gain insights into community dynamics, user behavior, and content preferences. Our investigation reveals that while 56.05% of the active users are strongly connected within the community, only 0.8% of them significantly contribute to its dynamics. Moreover, we observe temporal variations in community activity, with certain periods experiencing heightened engagement. Additionally, our findings highlight a correlation between user activity and popularity showing that more active users are generally more popular. Alongside these, a preference for positive and informative content is also observed where 82.41% users preferred positive and informative content. Overall, our study provides a comprehensive framework for understanding and managing online communities, leveraging graph-based techniques to gain valuable insights into user behavior and community dynamics.
QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration
Authors: Kaustubh D. Dhole, Shivam Bajaj, Ramraj Chandradevan, Eugene Agichtein
Date: 2024-03-23 00:00:00
Tasks: Retrieval

Formulating effective search queries remains a challenging task, particularly when users lack expertise in a specific domain or are not proficient in the language of the content. Providing example documents of interest might be easier for a user. However, such query-by-example scenarios are prone to concept drift, and the retrieval effectiveness is highly sensitive to the query generation method, without a clear way to incorporate user feedback. To enable exploration and to support Human-In-The-Loop experiments we propose QueryExplorer -- an interactive query generation, reformulation, and retrieval interface with support for HuggingFace generation models and PyTerrier's retrieval pipelines and datasets, and extensive logging of human feedback. To allow users to create and modify effective queries, our demo supports complementary approaches of using LLMs interactively, assisting the user with edits and feedback at multiple stages of the query formulation process. With support for recording fine-grained interactions and user annotations, QueryExplorer can serve as a valuable experimental and research platform for annotation, qualitative evaluation, and conducting Human-in-the-Loop (HITL) experiments for complex search tasks where users struggle to formulate queries.
Spectral Initialization for High-Dimensional Phase Retrieval with Biased Spatial Directions
Authors: Pierre Bousseyroux, Marc Potters
Date: 2024-03-22 00:00:00
Tasks: Retrieval

We explore a spectral initialization method that plays a central role in contemporary research on signal estimation in nonconvex scenarios. In a noiseless phase retrieval framework, we precisely analyze the method's performance in the high-dimensional limit when sensing vectors follow a multivariate Gaussian distribution for two rotationally invariant models of the covariance matrix C. In the first model C is a projector on a lower dimensional space while in the second it is a Wishart matrix. Our analytical results extend the well-established case when C is the identity matrix. Our examination shows that the introduction of biased spatial directions leads to a substantial improvement in the spectral method's effectiveness, particularly when the number of measurements is less than the signal's dimension. This extension also consistently reveals a phase transition phenomenon dependent on the ratio between sample size and signal dimension. Surprisingly, both of these models share the same threshold value.
Loops On Retrieval Augmented Generation (LoRAG)
Authors: Ayush Thakur, Rashmi Vashisth
Date: 2024-03-18 00:00:00
Tasks: Retrieval, Retrieval-augmented Generation, Text Generation

This paper presents Loops On Retrieval Augmented Generation (LoRAG), a new framework designed to enhance the quality of retrieval-augmented text generation through the incorporation of an iterative loop mechanism. The architecture integrates a generative model, a retrieval mechanism, and a dynamic loop module, allowing for iterative refinement of the generated text through interactions with relevant information retrieved from the input context. Experimental evaluations on benchmark datasets demonstrate that LoRAG surpasses existing state-of-the-art models in terms of BLEU score, ROUGE score, and perplexity, showcasing its effectiveness in achieving both coherence and relevance in generated text. The qualitative assessment further illustrates LoRAG's capability to produce contextually rich and coherent outputs. This research contributes valuable insights into the potential of iterative loops in mitigating challenges in text generation, positioning LoRAG as a promising advancement in the field.
Tax Policy Handbook for Crypto Assets
Authors: Arindam Misra
Date: 2024-03-22 00:00:00

The Financial system has witnessed rapid technological changes. The rise of Bitcoin and other crypto assets based on Distributed Ledger Technology mark a fundamental change in the way people transact and transmit value over a decentralized network, spread across geographies. This has created regulatory and tax policy blind spots, as governments and tax administrations take time to understand and provide policy responses to this innovative, revolutionary, and fast-paced technology. Due to the breakneck speed of innovation in blockchain technology and advent of Decentralized Finance, Decentralized Autonomous Organizations and the Metaverse, it is unlikely that the policy interventions and guidance by regulatory authorities or tax administrations would be ahead or in sync with the pace of innovation. This paper tries to explain the principles on which crypto assets function, their underlying technology and relates them to the tax issues and taxable events which arise within this ecosystem. It also provides instances of tax and regulatory policy responses already in effect in various jurisdictions, including the recent changes in reporting standards by the FATF and the OECD. This paper tries to explain the rationale behind existing laws and policies and the challenges in their implementation. It also attempts to present a ballpark estimate of tax potential of this asset class and suggests creation of global public digital infrastructure that can address issues related to pseudonymity and extra-territoriality. The paper analyses both direct and indirect taxation issues related to crypto assets and discusses more recent aspects like proof-of-stake and maximal extractable value in greater detail.
Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course
Authors: Boxaun Ma, Li Chen, Shin'ichi Konomi
Date: 2024-03-20 00:00:00
Tasks: Code Generation

The integration of ChatGPT as a supportive tool in education, notably in programming courses, addresses the unique challenges of programming education by providing assistance with debugging, code generation, and explanations. Despite existing research validating ChatGPT's effectiveness, its application in university-level programming education and a detailed understanding of student interactions and perspectives remain limited. This paper explores ChatGPT's impact on learning in a Python programming course tailored for first-year students over eight weeks. By analyzing responses from surveys, open-ended questions, and student-ChatGPT dialog data, we aim to provide a comprehensive view of ChatGPT's utility and identify both its advantages and limitations as perceived by students. Our study uncovers a generally positive reception toward ChatGPT and offers insights into its role in enhancing the programming education experience. These findings contribute to the broader discourse on AI's potential in education, suggesting paths for future research and application.
The Journey to Trustworthy AI: Pursuit of Pragmatic Frameworks
Authors: Mohamad M Nasr-Azadani, Jean-Luc Chatelain
Date: 2024-03-19 00:00:00
Tasks: Fairness

This paper reviews Trustworthy Artificial Intelligence (TAI) and its various definitions. Considering the principles respected in any society, TAI is often characterized by a few attributes, some of which have led to confusion in regulatory or engineering contexts. We argue against using terms such as Responsible or Ethical AI as substitutes for TAI. And to help clarify any confusion, we suggest leaving them behind. Given the subjectivity and complexity inherent in TAI, developing a universal framework is deemed infeasible. Instead, we advocate for approaches centered on addressing key attributes and properties such as fairness, bias, risk, security, explainability, and reliability. We examine the ongoing regulatory landscape, with a focus on initiatives in the EU, China, and the USA. We recognize that differences in AI regulations based on geopolitical and geographical reasons pose an additional challenge for multinational companies. We identify risk as a core factor in AI regulation and TAI. For example, as outlined in the EU-AI Act, organizations must gauge the risk level of their AI products to act accordingly (or risk hefty fines). We compare modalities of TAI implementation and how multiple cross-functional teams are engaged in the overall process. Thus, a brute force approach for enacting TAI renders its efficiency and agility, moot. To address this, we introduce our framework Set-Formalize-Measure-Act (SFMA). Our solution highlights the importance of transforming TAI-aware metrics, drivers of TAI, stakeholders, and business/legal requirements into actual benchmarks or tests. Finally, over-regulation driven by panic of powerful AI models can, in fact, harm TAI too. Based on GitHub user-activity data, in 2023, AI open-source projects rose to top projects by contributor account. Enabling innovation in TAI hinges on the independent contributions of the open-source community.
Towards Measuring and Modeling "Culture" in LLMs: A Survey
Authors: Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Alham Fikri Aji, Jacki O'Neill, Ashutosh Modi, Monojit Choudhury
Date: 2024-03-05 00:00:00
Tasks: Survey

We present a survey of more than 90 recent papers that aim to study cultural representation and inclusion in large language models (LLMs). We observe that none of the studies explicitly define "culture, which is a complex, multifaceted concept; instead, they probe the models on some specially designed datasets which represent certain aspects of "culture". We call these aspects the proxies of culture, and organize them across two dimensions of demographic and semantic proxies. We also categorize the probing methods employed. Our analysis indicates that only certain aspects of ``culture,'' such as values and objectives, have been studied, leaving several other interesting and important facets, especially the multitude of semantic domains (Thompson et al., 2020) and aboutness (Hershcovich et al., 2022), unexplored. Two other crucial gaps are the lack of robustness of probing techniques and situated studies on the impact of cultural mis- and under-representation in LLM-based applications.