pwc-archive/papers-with-abstracts huggingface dataset:
FINED: Feed Instance-Wise Information Need with Essential and Disentangled Parametric Knowledge from the Past
Authors: Kounianhua Du, Jizheng Chen, Jianghao Lin, Menghui Zhu, Bo Chen, Shuai Li, Yong Yu, Weinan Zhang
Date: 2024-05-20 00:00:00
Tasks: Disentanglement, Memorization

Recommender models play a vital role in various industrial scenarios, while often faced with the catastrophic forgetting problem caused by the fast shifting data distribution. To alleviate this problem, a common approach is to reuse knowledge from the historical data. However, preserving the vast and fast-accumulating data is hard, which causes dramatic storage overhead. Memorizing old data through a parametric knowledge base is then proposed, which compresses the vast amount of raw data into model parameters. Despite the flexibility, how to improve the memorization and generalization capabilities of the parametric knowledge base and suit the flexible information need of each instance are challenging. In this paper, we propose FINED to Feed INstance-wise information need with Essential and Disentangled parametric knowledge from past data for recommendation enhancement. Concretely, we train a knowledge extractor that extracts knowledge patterns of arbitrary order from past data and a knowledge encoder that memorizes the arbitrary order patterns, which serves as the retrieval key generator and memory network respectively in the following knowledge reusing phase. The whole process is regularized by the proposed two constraints, which improve the capabilities of the parametric knowledge base without increasing the size of it. The essential principle helps to compress the input into representative vectors that capture the task-relevant information and filter out the noisy information. The disentanglement principle reduces the redundancy of stored information and pushes the knowledge base to focus on capturing the disentangled invariant patterns. These two rules together promote rational compression of information for robust and generalized knowledge representations. Extensive experiments on two datasets justify the effectiveness of the proposed method.
EnterpriseEM: Fine-tuned Embeddings for Enterprise Semantic Search
Authors: Kamalkumar Rathinasamy, Jayarama Nettar, Amit Kumar, Vishal Manchanda, Arun Vijayakumar, Ayush Kataria, Venkateshprasanna Manjunath, Chidambaram GS, Jaskirat Singh Sodhi, Shoeb Shaikh, Wasim Akhtar Khan, Prashant Singh, Tanishq Dattatray Ige, Vipin Tiwari, Rajab Ali Mondal, Harshini K, S Reka, Chetana Amancharla, Faiz ur Rahman, Harikrishnan P A, Indraneel Saha, Bhavya Tiwary, Navin Shankar Patel, Pradeep T S, Balaji A J, Priyapravas, Mohammed Rafee Tarafdar
Date: 2024-05-18 00:00:00
Tasks: Information Retrieval, Management, Retrieval

Enterprises grapple with the significant challenge of managing proprietary unstructured data, hindering efficient information retrieval. This has led to the emergence of AI-driven information retrieval solutions, designed to adeptly extract relevant insights to address employee inquiries. These solutions often leverage pre-trained embedding models and generative models as foundational components. While pre-trained embeddings may exhibit proximity or disparity based on their original training objectives, they might not fully align with the unique characteristics of enterprise-specific data, leading to suboptimal alignment with the retrieval goals of enterprise environments. In this paper, we propose a comprehensive methodology for contextualizing pre-trained embedding models to enterprise environments, covering the entire process from data preparation to model fine-tuning and evaluation. By adapting the embeddings to better suit the retrieval tasks prevalent in enterprises, we aim to enhance the performance of information retrieval solutions. We discuss the process of fine-tuning, its effect on retrieval accuracy, and the potential benefits for enterprise information management. Our findings demonstrate the efficacy of fine-tuned embedding models in improving the precision and relevance of search results in enterprise settings.
KnowledgeHub: An end-to-end Tool for Assisted Scientific Discovery
Authors: Shinnosuke Tanaka, James Barry, Vishnudev Kuruvanthodi, Movina Moses, Maxwell J. Giammona, Nathan Herr, Mohab Elkaref, Geeth de Mel
Date: 2024-05-16 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering, Relation, Relation Classification, scientific discovery

This paper describes the KnowledgeHub tool, a scientific literature Information Extraction (IE) and Question Answering (QA) pipeline. This is achieved by supporting the ingestion of PDF documents that are converted to text and structured representations. An ontology can then be constructed where a user defines the types of entities and relationships they want to capture. A browser-based annotation tool enables annotating the contents of the PDF documents according to the ontology. Named Entity Recognition (NER) and Relation Classification (RC) models can be trained on the resulting annotations and can be used to annotate the unannotated portion of the documents. A knowledge graph is constructed from these entity and relation triples which can be queried to obtain insights from the data. Furthermore, we integrate a suite of Large Language Models (LLMs) that can be used for QA and summarisation that is grounded in the included documents via a retrieval component. KnowledgeHub is a unique tool that supports annotation, IE and QA, which gives the user full insight into the knowledge discovery pipeline.
PyTorch-IE: Fast and Reproducible Prototyping for Information Extraction
Authors: Arne Binder, Leonhard Hennig, Christoph Alt
Date: 2024-05-16 00:00:00

The objective of Information Extraction (IE) is to derive structured representations from unstructured or semi-structured documents. However, developing IE models is complex due to the need of integrating several subtasks. Additionally, representation of data among varied tasks and transforming datasets into task-specific model inputs presents further challenges. To streamline this undertaking for researchers, we introduce PyTorch-IE, a deep-learning-based framework uniquely designed to enable swift, reproducible, and reusable implementations of IE models. PyTorch-IE offers a flexible data model capable of creating complex data structures by integrating interdependent layers of annotations derived from various data types, like plain text or semi-structured text, and even images. We propose task modules to decouple the concerns of data representation and model-specific representations, thereby fostering greater flexibility and reusability of code. PyTorch-IE also extends support for widely used libraries such as PyTorch-Lightning for training, HuggingFace datasets for dataset reading, and Hydra for experiment configuration. Supplementary libraries and GitHub templates for the easy setup of new projects are also provided. By ensuring functionality and versatility, PyTorch-IE provides vital support to the research community engaged in Information Extraction.
Disentangling Specificity for Abstractive Multi-document Summarization
Authors: Congbo Ma, Wei Emma Zhang, Hu Wang, Haojie Zhuang, Mingyu Guo
Date: 2024-05-12 00:00:00
Tasks: Document Summarization, Multi-Document Summarization, Specificity

Multi-document summarization (MDS) generates a summary from a document set. Each document in a set describes topic-relevant concepts, while per document also has its unique contents. However, the document specificity receives little attention from existing MDS approaches. Neglecting specific information for each document limits the comprehensiveness of the generated summaries. To solve this problem, in this paper, we propose to disentangle the specific content from documents in one document set. The document-specific representations, which are encouraged to be distant from each other via a proposed orthogonal constraint, are learned by the specific representation learner. We provide extensive analysis and have interesting findings that specific information and document set representations contribute distinctive strengths and their combination yields a more comprehensive solution for the MDS. Also, we find that the common (i.e. shared) information could not contribute much to the overall performance under the MDS settings. Implemetation codes are available at https://github.com/congboma/DisentangleSum.
IQLS: Framework for leveraging Metadata to enable Large Language Model based queries to complex, versatile Data
Authors: Sami Azirar, Hossam A. Gabbar, Chaouki Regoui
Date: 2024-05-04 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Large Language Model, Retrieval

As the amount and complexity of data grows, retrieving it has become a more difficult task that requires greater knowledge and resources. This is especially true for the logistics industry, where new technologies for data collection provide tremendous amounts of interconnected real-time data. The Intelligent Query and Learning System (IQLS) simplifies the process by allowing natural language use to simplify data retrieval . It maps structured data into a framework based on the available metadata and available data models. This framework creates an environment for an agent powered by a Large Language Model. The agent utilizes the hierarchical nature of the data to filter iteratively by making multiple small context-aware decisions instead of one-shot data retrieval. After the Data filtering, the IQLS enables the agent to fulfill tasks given by the user query through interfaces. These interfaces range from multimodal transportation information retrieval to route planning under multiple constraints. The latter lets the agent define a dynamic object, which is determined based on the query parameters. This object represents a driver capable of navigating a road network. The road network is depicted as a graph with attributes based on the data. Using a modified version of the Dijkstra algorithm, the optimal route under the given constraints can be determined. Throughout the entire process, the user maintains the ability to interact and guide the system. The IQLS is showcased in a case study on the Canadian logistics sector, allowing geospatial, visual, tabular and text data to be easily queried semantically in natural language.
Learning-based models for building user profiles for personalized information access
Authors: Minyar Sassi Hidri
Date: 2024-05-04 00:00:00

This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data.
Towards Fairness in Provably Communication-Efficient Federated Recommender Systems
Authors: Kirandeep Kaur, Sujit Gujar, Shweta Jain
Date: 2024-05-03 00:00:00
Tasks: Fairness, Federated Learning, Recommendation Systems

To reduce the communication overhead caused by parallel training of multiple clients, various federated learning (FL) techniques use random client sampling. Nonetheless, ensuring the efficacy of random sampling and determining the optimal number of clients to sample in federated recommender systems (FRSs) remains challenging due to the isolated nature of each user as a separate client. This challenge is exacerbated in models where public and private features can be separated, and FL allows communication of only public features (item gradients). In this study, we establish sample complexity bounds that dictate the ideal number of clients required for improved communication efficiency and retained accuracy in such models. In line with our theoretical findings, we empirically demonstrate that RS-FairFRS reduces communication cost (~47%). Second, we demonstrate the presence of class imbalance among clients that raises a substantial equity concern for FRSs. Unlike centralized machine learning, clients in FRS can not share raw data, including sensitive attributes. For this, we introduce RS-FairFRS, first fairness under unawareness FRS built upon random sampling based FRS. While random sampling improves communication efficiency, we propose a novel two-phase dual-fair update technique to achieve fairness without revealing protected attributes of active clients participating in training. Our results on real-world datasets and different sensitive features illustrate a significant reduction in demographic bias (~approx40\%), offering a promising path to achieving fairness and communication efficiency in FRSs without compromising the overall accuracy of FRS.
Extracting chemical food safety hazards from the scientific literature automatically using large language models
Authors: Neris Özen, Wenjuan Mu, Esther D. van Asselt, Leonieke M. van den Bulk
Date: 2024-05-01 00:00:00
Tasks: Articles, Language Modelling, Large Language Model

The number of scientific articles published in the domain of food safety has consistently been increasing over the last few decades. It has therefore become unfeasible for food safety experts to read all relevant literature related to food safety and the occurrence of hazards in the food chain. However, it is important that food safety experts are aware of the newest findings and can access this information in an easy and concise way. In this study, an approach is presented to automate the extraction of chemical hazards from the scientific literature through large language models. The large language model was used out-of-the-box and applied on scientific abstracts; no extra training of the models or a large computing cluster was required. Three different styles of prompting the model were tested to assess which was the most optimal for the task at hand. The prompts were optimized with two validation foods (leafy greens and shellfish) and the final performance of the best prompt was evaluated using three test foods (dairy, maize and salmon). The specific wording of the prompt was found to have a considerable effect on the results. A prompt breaking the task down into smaller steps performed best overall. This prompt reached an average accuracy of 93% and contained many chemical contaminants already included in food monitoring programs, validating the successful retrieval of relevant hazards for the food safety domain. The results showcase how valuable large language models can be for the task of automatic information extraction from the scientific literature.
Enhancement of Subjective Content Descriptions by using Human Feedback
Authors: Magnus Bender, Tanya Braun, Ralf Möller, Marcel Gehrke
Date: 2024-04-30 00:00:00
Tasks: Information Retrieval

An agent providing an information retrieval service may work with a corpus of text documents. The documents in the corpus may contain annotations such as Subjective Content Descriptions (SCD) -- additional data associated with different sentences of the documents. Each SCD is associated with multiple sentences of the corpus and has relations among each other. The agent uses the SCDs to create its answers in response to queries supplied by users. However, the SCD the agent uses might reflect the subjective perspective of another user. Hence, answers may be considered faulty by an agent's user, because the SCDs may not exactly match the perceptions of an agent's user. A naive and very costly approach would be to ask each user to completely create all the SCD themselves. To use existing knowledge, this paper presents ReFrESH, an approach for Relation-preserving Feedback-reliant Enhancement of SCDs by Humans. An agent's user can give feedback about faulty answers to the agent. This feedback is then used by ReFrESH to update the SCDs incrementally. However, human feedback is not always unambiguous. Therefore, this paper additionally presents an approach to decide how to incorporate the feedback and when to update the SCDs. Altogether, SCDs can be updated with human feedback, allowing users to create even more specific SCDs for their needs.
GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements
Authors: Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Roberta Raileanu
Date: 2024-02-13 00:00:00
Tasks: GSM8K, Math

State-of-the-art language models can exhibit impressive reasoning refinement capabilities on math, science or coding tasks. However, recent work demonstrates that even the best models struggle to identify \textit{when and where to refine} without access to external feedback. Outcome-based Reward Models (\textbf{ORMs}), trained to predict correctness of the final answer indicating when to refine, offer one convenient solution for deciding when to refine. Process Based Reward Models (\textbf{PRMs}), trained to predict correctness of intermediate steps, can then be used to indicate where to refine. But they are expensive to train, requiring extensive human annotations. In this paper, we propose Stepwise ORMs (\textbf{SORMs}) which are trained, only on synthetic data, to approximate the expected future reward of the optimal policy or $V^{\star}$. More specifically, SORMs are trained to predict the correctness of the final answer when sampling the current policy many times (rather than only once as in the case of ORMs). Our experiments show that SORMs can more accurately detect incorrect reasoning steps compared to ORMs, thus improving downstream accuracy when doing refinements. We then train \textit{global} refinement models, which take only the question and a draft solution as input and predict a corrected solution, and \textit{local} refinement models which also take as input a critique indicating the location of the first reasoning error. We generate training data for both models synthetically by reusing data used to train the SORM. We find combining global and local refinements, using the ORM as a reranker, significantly outperforms either one individually, as well as a best of three sample baseline. With this strategy we can improve the accuracy of a LLaMA-2 13B model (already fine-tuned with RL) on GSM8K from 53\% to 65\% when greedily sampled.
CORE-ReID: Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-Identification
Authors: Trinh Quoc Nguyen, Oky Dicky Ardiansyah Prima, Katsuyoshi Hotta
Date: 2024-06-03 00:00:00
Proceeding: Software 2024 6
Tasks: Domain Adaptation, Person Re-Identification, Unsupervised Domain Adaptation, Unsupervised Person Re-Identification

This study introduces a novel framework, “Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-identification (CORE-ReID)”, to address an Unsupervised Domain Adaptation (UDA) for Person Re-identification (ReID). The framework utilizes CycleGAN to generate diverse data that harmonize differences in image characteristics from different camera sources in the pre-training stage. In the fine-tuning stage, based on a pair of teacher–student networks, the framework integrates multi-view features for multi-level clustering to derive diverse pseudo-labels. A learnable Ensemble Fusion component that focuses on fine grained local information within global features is introduced to enhance learning comprehensiveness and avoid ambiguity associated with multiple pseudo-labels. Experimental results on three common UDAs in Person ReID demonstrated significant performance gains over state-of-the-art approaches. Additional enhancements, such as Efficient Channel Attention Block and Bidirectional Mean Feature Normalization mitigate deviation effects and the adaptive fusion of global and local features using the ResNet-based model, further strengthening the framework. The proposed framework ensures clarity in fusion features, avoids ambiguity, and achieves high accuracy in terms of Mean Average Precision, Top-1, Top-5, and Top-10, positioning it as an advanced and effective solution for UDA in Person ReID.
GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models
Authors: Zicheng Liu, Jiahui Li, Siyuan Li, Zelin Zang, Cheng Tan, Yufei Huang, Yajing Bai, Stan Z. Li
Date: 2024-06-01 00:00:00
Tasks: Benchmarking

The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application across a spectrum of downstream applications. Despite advancements, a lack of evaluation framework makes it difficult to ensure equitable assessment due to experimental settings, model intricacy, benchmark datasets, and reproducibility challenges. In the absence of standardization, comparative analyses risk becoming biased and unreliable. To surmount this impasse, we introduce GenBench, a comprehensive benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. Through systematic evaluations of datasets spanning diverse biological domains with a particular emphasis on both short-range and long-range genomic tasks, firstly including the three most important DNA tasks covering Coding Region, Non-Coding Region, Genome Structure, etc. Moreover, We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance. Our findings reveal an interesting observation: independent of the number of parameters, the discernible difference in preference between the attention-based and convolution-based models on short- and long-range tasks may provide insights into the future design of GFM.
Cross-modal Retrieval with Noisy Correspondence via Consistency Refining and Mining
Authors: Xinran Ma, Mouxing Yang, Yunfan Li, Peng Hu, Jiancheng Lv, Xi Peng
Date: 2024-03-25 00:00:00
Proceeding: IEEE Transactions on Image Processing 2024 3
Tasks: Cross-Modal Retrieval, Cross-modal retrieval with noisy correspondence, Graph Matching, Retrieval

The success of existing cross-modal retrieval (CMR) methods heavily rely on the assumption that the annotated cross-modal correspondence is faultless. In practice, however, the correspondence of some pairs would be inevitably contaminated during data collection or annotation, thus leading to the so-called Noisy Correspondence (NC) problem. To alleviate the influence of NC, we propose a novel method termed Consistency REfining And Mining (CREAM) by revealing and exploiting the difference between correspondence and consistency. Specifically, the correspondence and the consistency only be coincident for true positive and true negative pairs, while being distinct for false positive and false negative pairs. Based on the observation, CREAM employs a collaborative learning paradigm to detect and rectify the correspondence of positives, and a negative mining approach to explore and utilize the consistency. Thanks to the consistency refining and mining strategy of CREAM, the overfitting on the false positives could be prevented and the consistency rooted in the false negatives could be exploited, thus leading to a robust CMR method. Extensive experiments verify the effectiveness of our method on three image-text benchmarks including Flickr30K, MS-COCO, and Conceptual Captions. Furthermore, we adopt our method into the graph matching task and the results demonstrate the robustness of our method against fine-grained NC problem. The code is available on https://github.com/XLearning-SCU/2024-TIP-CREAM .