pwc-archive/papers-with-abstracts huggingface dataset:
Gender Dynamics in Russian Online Political Discourse
Authors: Elizaveta Savchenko, Michael Raphael Freedman
Date: 2024-08-18 00:00:00

The digital landscape provides a dynamic platform for political discourse crucial for understanding shifts in public opinion and engagement especially under authoritarian governments This study examines YouTube user behavior during the Russian-Ukrainian war analyzing 2168 videos with over 36000 comments from January 2022 to February 2024 We observe distinct patterns of participation and gender dynamics that correlate with major political and military events Notably females were more active in antigovernment channels especially during peak conflict periods Contrary to assumptions about online engagement in authoritarian contexts our findings suggest a complex interplay where women emerge as pivotal digital communicators This highlights online platforms role in facilitating political expression under authoritarian regimes demonstrating its potential as a barometer for public sentiment.
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning
Authors: Jiadong Feng, Wei Li, Zhao Wei, Yong Xu, Juhong Wang, Hui Li
Date: 2024-08-18 00:00:00
Tasks: Code Search, Contrastive Learning, Deep Learning, MULTI-VIEW LEARNING, Representation Learning

Software development is a repetitive task, as developers usually reuse or get inspiration from existing implementations. Code search, which refers to the retrieval of relevant code snippets from a codebase according to the developer's intent that has been expressed as a query, has become increasingly important in the software development process. Due to the success of deep learning in various applications, a great number of deep learning based code search approaches have sprung up and achieved promising results. However, developers may not follow the same naming conventions and the same variable may have different variable names in different implementations, bringing a challenge to deep learning based code search methods that rely on explicit variable correspondences to understand source code. To overcome this challenge, we propose a naming-agnostic code search method (NACS) based on contrastive multi-view code representation learning. NACS strips information bound to variable names from Abstract Syntax Tree (AST), the representation of the abstract syntactic structure of source code, and focuses on capturing intrinsic properties solely from AST structures. We use semantic-level and syntax-level augmentation techniques to prepare realistically rational data and adopt contrastive learning to design a graph-view modeling component in NACS to enhance the understanding of code snippets. We further model ASTs in a path view to strengthen the graph-view modeling component through multi-view learning. Extensive experiments show that NACS provides superior code search performance compared to baselines and NACS can be adapted to help existing code search methods overcome the impact of different naming conventions.
A Study of PHOC Spatial Region Configurations for Math Formula Retrieval
Authors: Matt Langsenkamp, Bryan Amador, Richard Zanibbi
Date: 2024-08-17 00:00:00
Tasks: Math, Retrieval

A Pyramidal Histogram Of Characters (PHOC) represents the spatial location of symbols as binary vectors. The vectors are composed of levels that split a formula into equal-sized regions of one or more types (e.g., rectangles or ellipses). For each region type, this produces a pyramid of overlapping regions, where the first level contains the entire formula, and the final level the finest-grained regions. In this work, we introduce concentric rectangles for regions, and analyze whether subsequent PHOC levels encode redundant information by omitting levels from PHOC configurations. As a baseline, we include a bag of words PHOC containing only the first whole-formula level. Finally, using the ARQMath-3 formula retrieval benchmark, we demonstrate that some levels encoded in the original PHOC configurations are redundant, that PHOC models with rectangular regions outperform earlier PHOC models, and that despite their simplicity, PHOC models are surprisingly competitive with the state-of-the-art. PHOC is not math-specific, and might be used for chemical diagrams, charts, or other graphics.
FabricQA-Extractor: A Question Answering System to Extract Information from Documents using Natural Language Questions
Authors: Qiming Wang, Raul Castro Fernandez
Date: 2024-08-17 00:00:00
Tasks: Management, Question Answering, Reading Comprehension, Relation

Reading comprehension models answer questions posed in natural language when provided with a short passage of text. They present an opportunity to address a long-standing challenge in data management: the extraction of structured data from unstructured text. Consequently, several approaches are using these models to perform information extraction. However, these modern approaches leave an opportunity behind because they do not exploit the relational structure of the target extraction table. In this paper, we introduce a new model, Relation Coherence, that exploits knowledge of the relational structure to improve the extraction quality. We incorporate the Relation Coherence model as part of FabricQA-Extractor, an end-to-end system we built from scratch to conduct large scale extraction tasks over millions of documents. We demonstrate on two datasets with millions of passages that Relation Coherence boosts extraction performance and evaluate FabricQA-Extractor on large scale datasets.
TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems
Authors: Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xu Chu, Junfeng Zhao, Yasha Wang
Date: 2024-08-17 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized queries. However, existing approaches to RAG fall short by neglecting system state variables, which are crucial for ensuring adaptive control, retrieval halting, and system convergence. In this paper, we introduce the TC-RAG through rigorous proof, a novel framework that addresses these challenges by incorporating a Turing Complete System to manage state variables, thereby enabling more efficient and accurate knowledge retrieval. By leveraging a memory stack system with adaptive retrieval, reasoning, and planning capabilities, TC-RAG not only ensures the controlled halting of retrieval processes but also mitigates the accumulation of erroneous knowledge via Push and Pop actions. In the case study of the medical domain, our extensive experiments on real-world healthcare datasets demonstrate the superiority of TC-RAG over existing methods in accuracy by over 7.20\%. Our dataset and code have been available at https://https://github.com/Artessay/SAMA.git.
Meta Knowledge for Retrieval Augmented Large Language Models
Authors: Laurent Mombaerts, Terry Ding, Adi Banerjee, Florian Felice, Jonathan Taws, Tarik Borogovac
Date: 2024-08-16 00:00:00
Tasks: Chunking, Information Retrieval, RAG, Retrieval, Retrieval-augmented Generation, Specificity

Retrieval Augmented Generation (RAG) is a technique used to augment Large Language Models (LLMs) with contextually relevant, time-critical, or domain-specific information without altering the underlying model parameters. However, constructing RAG systems that can effectively synthesize information from large and diverse set of documents remains a significant challenge. We introduce a novel data-centric RAG workflow for LLMs, transforming the traditional retrieve-then-read system into a more advanced prepare-then-rewrite-then-retrieve-then-read framework, to achieve higher domain expert-level understanding of the knowledge base. Our methodology relies on generating metadata and synthetic Questions and Answers (QA) for each document, as well as introducing the new concept of Meta Knowledge Summary (MK Summary) for metadata-based clusters of documents. The proposed innovations enable personalized user-query augmentation and in-depth information retrieval across the knowledge base. Our research makes two significant contributions: using LLMs as evaluators and employing new comparative performance metrics, we demonstrate that (1) using augmented queries with synthetic question matching significantly outperforms traditional RAG pipelines that rely on document chunking (p < 0.01), and (2) meta knowledge-augmented queries additionally significantly improve retrieval precision and recall, as well as the final answers breadth, depth, relevancy, and specificity. Our methodology is cost-effective, costing less than $20 per 2000 research papers using Claude 3 Haiku, and can be adapted with any fine-tuning of either the language or embedding models to further enhance the performance of end-to-end RAG pipelines.
MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction
Authors: Zhiming Yang, Haining Gao, Dehong Gao, Luwei Yang, Libin Yang, Xiaoyan Cai, Wei Ning, Guannan Zhang
Date: 2024-08-14 00:00:00
Tasks: Click-Through Rate Prediction, Prediction

Click-through rate (CTR) prediction is one of the fundamental tasks in the industry, especially in e-commerce, social media, and streaming media. It directly impacts website revenues, user satisfaction, and user retention. However, real-world production platforms often encompass various domains to cater for diverse customer needs. Traditional CTR prediction models struggle in multi-domain recommendation scenarios, facing challenges of data sparsity and disparate data distributions across domains. Existing multi-domain recommendation approaches introduce specific-domain modules for each domain, which partially address these issues but often significantly increase model parameters and lead to insufficient training. In this paper, we propose a Multi-domain Low-Rank Adaptive network (MLoRA) for CTR prediction, where we introduce a specialized LoRA module for each domain. This approach enhances the model's performance in multi-domain CTR prediction tasks and is able to be applied to various deep-learning models. We evaluate the proposed method on several multi-domain datasets. Experimental results demonstrate our MLoRA approach achieves a significant improvement compared with state-of-the-art baselines. Furthermore, we deploy it in the production environment of the Alibaba.COM. The online A/B testing results indicate the superiority and flexibility in real-world production environments. The code of our MLoRA is publicly available.
What should I wear to a party in a Greek taverna? Evaluation for Conversational Agents in the Fashion Domain
Authors: Antonis Maronikolakis, Ana Peleteiro Ramallo, Weiwei Cheng, Thomas Kober
Date: 2024-08-13 00:00:00
Tasks: 4k

Large language models (LLMs) are poised to revolutionize the domain of online fashion retail, enhancing customer experience and discovery of fashion online. LLM-powered conversational agents introduce a new way of discovery by directly interacting with customers, enabling them to express in their own ways, refine their needs, obtain fashion and shopping advice that is relevant to their taste and intent. For many tasks in e-commerce, such as finding a specific product, conversational agents need to convert their interactions with a customer to a specific call to different backend systems, e.g., a search system to showcase a relevant set of products. Therefore, evaluating the capabilities of LLMs to perform those tasks related to calling other services is vital. However, those evaluations are generally complex, due to the lack of relevant and high quality datasets, and do not align seamlessly with business needs, amongst others. To this end, we created a multilingual evaluation dataset of 4k conversations between customers and a fashion assistant in a large e-commerce fashion platform to measure the capabilities of LLMs to serve as an assistant between customers and a backend engine. We evaluate a range of models, showcasing how our dataset scales to business needs and facilitates iterative development of tools.
Bundle Recommendation with Item-level Causation-enhanced Multi-view Learning
Authors: Huy-Son Nguyen, Tuan-Nghia Bui, Long-Hai Nguyen, Hoang Manh-Hung, Cam-Van Thi Nguyen, Hoang-Quynh Le, Duc-Trong Le
Date: 2024-08-13 00:00:00
Tasks: Contrastive Learning, MULTI-VIEW LEARNING

Bundle recommendation aims to enhance business profitability and user convenience by suggesting a set of interconnected items. In real-world scenarios, leveraging the impact of asymmetric item affiliations is crucial for effective bundle modeling and understanding user preferences. To address this, we present BunCa, a novel bundle recommendation approach employing item-level causation-enhanced multi-view learning. BunCa provides comprehensive representations of users and bundles through two views: the Coherent View, leveraging the Multi-Prospect Causation Network for causation-sensitive relations among items, and the Cohesive View, employing LightGCN for information propagation among users and bundles. Modeling user preferences and bundle construction combined from both views ensures rigorous cohesion in direct user-bundle interactions through the Cohesive View and captures explicit intents through the Coherent View. Simultaneously, the integration of concrete and discrete contrastive learning optimizes the consistency and self-discrimination of multi-view representations. Extensive experiments with BunCa on three benchmark datasets demonstrate the effectiveness of this novel research and validate our hypothesis.
PATopics: An automatic framework to extract useful information from pharmaceutical patents documents
Authors: Pablo Cecilio, Antônio Perreira, Juliana Santos Rosa Viegas, Washington Cunha, Felipe Viegas, Elisa Tuler, Fabiana Testa Moura de Carvalho Vicentini, Leonardo Rocha
Date: 2024-08-12 00:00:00

Pharmaceutical patents play an important role by protecting the innovation from copies but also drive researchers to innovate, create new products, and promote disruptive innovations focusing on collective health. The study of patent management usually refers to an exhaustive manual search. This happens, because patent documents are complex with a lot of details regarding the claims and methodology/results explanation of the invention. To mitigate the manual search, we proposed PATopics, a framework specially designed to extract relevant information for Pharmaceutical patents. PATopics is composed of four building blocks that extract textual information from the patents, build relevant topics that are capable of summarizing the patents, correlate these topics with useful patent characteristics and then, summarize the information in a friendly web interface to final users. The general contributions of PATopics are its ability to centralize patents and to manage patents into groups based on their similarities. We extensively analyzed the framework using 4,832 pharmaceutical patents concerning 809 molecules patented by 478 companies. In our analysis, we evaluate the use of the framework considering the demands of three user profiles -- researchers, chemists, and companies. We also designed four real-world use cases to evaluate the framework's applicability. Our analysis showed how practical and helpful PATopics are in the pharmaceutical scenario.
Bayesian inference to improve quality of Retrieval Augmented Generation
Authors: Dattaraj Rao
Date: 2024-08-12 00:00:00
Tasks: Bayesian Inference, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

Retrieval Augmented Generation or RAG is the most popular pattern for modern Large Language Model or LLM applications. RAG involves taking a user query and finding relevant paragraphs of context in a large corpus typically captured in a vector database. Once the first level of search happens over a vector database, the top n chunks of relevant text are included directly in the context and sent as prompt to the LLM. Problem with this approach is that quality of text chunks depends on effectiveness of search. There is no strong post processing after search to determine if the chunk does hold enough information to include in prompt. Also many times there may be chunks that have conflicting information on the same subject and the model has no prior experience which chunk to prioritize to make a decision. Often times, this leads to the model providing a statement that there are conflicting statements, and it cannot produce an answer. In this research we propose a Bayesian approach to verify the quality of text chunks from the search results. Bayes theorem tries to relate conditional probabilities of the hypothesis with evidence and prior probabilities. We propose that, finding likelihood of text chunks to give a quality answer and using prior probability of quality of text chunks can help us improve overall quality of the responses from RAG systems. We can use the LLM itself to get a likelihood of relevance of a context paragraph. For prior probability of the text chunk, we use the page number in the documents parsed. Assumption is that that paragraphs in earlier pages have a better probability of being findings and more relevant to generalizing an answer.
Towards Effective Authorship Attribution: Integrating Class-Incremental Learning
Authors: Mostafa Rahgouy, Hamed Babaei Giglou, Mehnaz Tabassum, Dongji Feng, Amit Das, Taher Rahgooy, Gerry Dozier, Cheryl D. Seals
Date: 2024-08-12 00:00:00
Tasks: Authorship Attribution, class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning

AA is the process of attributing an unidentified document to its true author from a predefined group of known candidates, each possessing multiple samples. The nature of AA necessitates accommodating emerging new authors, as each individual must be considered unique. This uniqueness can be attributed to various factors, including their stylistic preferences, areas of expertise, gender, cultural background, and other personal characteristics that influence their writing. These diverse attributes contribute to the distinctiveness of each author, making it essential for AA systems to recognize and account for these variations. However, current AA benchmarks commonly overlook this uniqueness and frame the problem as a closed-world classification, assuming a fixed number of authors throughout the system's lifespan and neglecting the inclusion of emerging new authors. This oversight renders the majority of existing approaches ineffective for real-world applications of AA, where continuous learning is essential. These inefficiencies manifest as current models either resist learning new authors or experience catastrophic forgetting, where the introduction of new data causes the models to lose previously acquired knowledge. To address these inefficiencies, we propose redefining AA as CIL, where new authors are introduced incrementally after the initial training phase, allowing the system to adapt and learn continuously. To achieve this, we briefly examine subsequent CIL approaches introduced in other domains. Moreover, we have adopted several well-known CIL methods, along with an examination of their strengths and weaknesses in the context of AA. Additionally, we outline potential future directions for advancing CIL AA systems. As a result, our paper can serve as a starting point for evolving AA systems from closed-world models to continual learning through CIL paradigms.
LLMJudge: LLMs for Relevance Judgments
Authors: Hossein A. Rahmani, Emine Yilmaz, Nick Craswell, Bhaskar Mitra, Paul Thomas, Charles L. A. Clarke, Mohammad Aliannejadi, Clemencia Siro, Guglielmo Faggioli
Date: 2024-08-09 00:00:00
Tasks: Information Retrieval, Retrieval

The LLMJudge challenge is organized as part of the LLM4Eval workshop at SIGIR 2024. Test collections are essential for evaluating information retrieval (IR) systems. The evaluation and tuning of a search system is largely based on relevance labels, which indicate whether a document is useful for a specific search and user. However, collecting relevance judgments on a large scale is costly and resource-intensive. Consequently, typical experiments rely on third-party labelers who may not always produce accurate annotations. The LLMJudge challenge aims to explore an alternative approach by using LLMs to generate relevance judgments. Recent studies have shown that LLMs can generate reliable relevance judgments for search systems. However, it remains unclear which LLMs can match the accuracy of human labelers, which prompts are most effective, how fine-tuned open-source LLMs compare to closed-source LLMs like GPT-4, whether there are biases in synthetically generated data, and if data leakage affects the quality of generated labels. This challenge will investigate these questions, and the collected data will be released as a package to support automatic relevance judgment research in information retrieval and search.
Enhancing Exploratory Learning through Exploratory Search with the Emergence of Large Language Models
Authors: Yiming Luo, Patrick Cheong-Iao Pang, Shanton Chang
Date: 2024-08-09 00:00:00
Tasks: Information Retrieval, Retrieval

In the information era, how learners find, evaluate, and effectively use information has become a challenging issue, especially with the added complexity of large language models (LLMs) that have further confused learners in their information retrieval and search activities. This study attempts to unpack this complexity by combining exploratory search strategies with the theories of exploratory learning to form a new theoretical model of exploratory learning from the perspective of students' learning. Our work adapts Kolb's learning model by incorporating high-frequency exploration and feedback loops, aiming to promote deep cognitive and higher-order cognitive skill development in students. Additionally, this paper discusses and suggests how advanced LLMs integrated into information retrieval and information theory can support students in their exploratory searches, contributing theoretically to promoting student-computer interaction and supporting their learning journeys in the new era with LLMs.
Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis
Authors: Cong Wang
Date: 2024-08-17 00:00:00
Tasks: Causal Inference, counterfactual

In this paper, we propose a novel method for causal inference within the framework of counterfactual and synthetic control. Matching forward the generalized synthetic control method, our instrumented principal component analysis method instruments factor loadings with predictive covariates rather than including them as regressors. These instrumented factor loadings exhibit time-varying dynamics, offering a better economic interpretation. Covariates are instrumented through a transformation matrix, $\Gamma$, when we have a large number of covariates it can be easily reduced in accordance with a small number of latent factors helping us to effectively handle high-dimensional datasets and making the model parsimonious. Moreover, the novel way of handling covariates is less exposed to model misspecification and achieved better prediction accuracy. Our simulations show that this method is less biased in the presence of unobserved covariates compared to other mainstream approaches. In the empirical application, we use the proposed method to evaluate the effect of Brexit on foreign direct investment to the UK.