pwc-archive/papers-with-abstracts huggingface dataset:
From Prompting to Alignment: A Generative Framework for Query Recommendation
Authors: Erxue Min, Hsiu-Yuan Huang, Min Yang, Xihong Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, Dawei Yin
Date: 2025-04-14 00:00:00

In modern search systems, search engines often suggest relevant queries to users through various panels or components, helping refine their information needs. Traditionally, these recommendations heavily rely on historical search logs to build models, which suffer from cold-start or long-tail issues. Furthermore, tasks such as query suggestion, completion or clarification are studied separately by specific design, which lacks generalizability and hinders adaptation to novel applications. Despite recent attempts to explore the use of LLMs for query recommendation, these methods mainly rely on the inherent knowledge of LLMs or external sources like few-shot examples, retrieved documents, or knowledge bases, neglecting the importance of the calibration and alignment with user feedback, thus limiting their practical utility. To address these challenges, we first propose a general Generative Query Recommendation (GQR) framework that aligns LLM-based query generation with user preference. Specifically, we unify diverse query recommendation tasks by a universal prompt framework, leveraging the instruct-following capability of LLMs for effective generation. Secondly, we align LLMs with user feedback via presenting a CTR-alignment framework, which involves training a query-wise CTR predictor as a process reward model and employing list-wise preference alignment to maximize the click probability of the generated query list. Furthermore, recognizing the inconsistency between LLM knowledge and proactive search intents arising from the separation of user-initiated queries from models, we align LLMs with user initiative via retrieving co-occurrence queries as side information when historical logs are available.
On Precomputation and Caching in Information Retrieval Experiments with Pipeline Architectures
Authors: Sean MacAvaney, Craig Macdonald
Date: 2025-04-14 00:00:00
Tasks: Information Retrieval, Retrieval

Modern information retrieval systems often rely on multiple components executed in a pipeline. In a research setting, this can lead to substantial redundant computations (e.g., retrieving the same query multiple times for evaluating different downstream rerankers). To overcome this, researchers take cached "result" files as inputs, which represent the output of another pipeline. However, these result files can be brittle and can cause a disconnect between the conceptual design of the pipeline and its logical implementation. To overcome both the redundancy problem (when executing complete pipelines) and the disconnect problem (when relying on intermediate result files), we describe our recent efforts to improve the caching capabilities in the open-source PyTerrier IR platform. We focus on two main directions: (1) automatic implicit caching of common pipeline prefixes when comparing systems and (2) explicit caching of operations through a new extension package, pyterrier-caching. These approaches allow for the best of both worlds: pipelines can be fully expressed end-to-end, while also avoiding redundant computations between pipelines.
Physical Layer Authentication With Colored RIS in Visible Light Communications
Authors: Besra Cetindere Vela, Serkan Vela, Stefano Tomasin
Date: 2025-04-18 00:00:00

We study a visible light communication (VLC) system that employs a colored reconfigurable intelligent surface (CRIS) based on dichroic mirrors that reflect light at tunable frequencies. A verifier can use the CRIS to authenticate transmissions by comparing received multicolor power profiles with expected patterns. Four CRIS configuration strategies are evaluated: a deterministic cyclic pattern, static random reflectance, dynamic random reflectance, and dynamic random permutation of fixed profiles. Randomized configurations, especially dynamic ones, achieve superior authentication, enabling a novel challenge-response physical-layer authentication scheme over CRIS.
Constrained Auto-Regressive Decoding Constrains Generative Retrieval
Authors: Shiguang Wu, Zhaochun Ren, Xin Xin, Jiyuan Yang, Mengqi Zhang, Zhumin Chen, Maarten de Rijke, Pengjie Ren
Date: 2025-04-14 00:00:00
Tasks: Retrieval

Generative retrieval seeks to replace traditional search index data structures with a single large-scale neural network, offering the potential for improved efficiency and seamless integration with generative large language models. As an end-to-end paradigm, generative retrieval adopts a learned differentiable search index to conduct retrieval by directly generating document identifiers through corpus-specific constrained decoding. The generalization capabilities of generative retrieval on out-of-distribution corpora have gathered significant attention. In this paper, we examine the inherent limitations of constrained auto-regressive generation from two essential perspectives: constraints and beam search. We begin with the Bayes-optimal setting where the generative retrieval model exactly captures the underlying relevance distribution of all possible documents. Then we apply the model to specific corpora by simply adding corpus-specific constraints. Our main findings are two-fold: (i) For the effect of constraints, we derive a lower bound of the error, in terms of the KL divergence between the ground-truth and the model-predicted step-wise marginal distributions. (ii) For the beam search algorithm used during generation, we reveal that the usage of marginal distributions may not be an ideal approach. This paper aims to improve our theoretical understanding of the generalization capabilities of the auto-regressive decoding retrieval paradigm, laying a foundation for its limitations and inspiring future advancements toward more robust and generalizable generative retrieval.
Constructing Micro Knowledge Graphs from Technical Support Documents
Authors: Atul Kumar, Nisha Gupta, Saswati Dana
Date: 2025-04-14 00:00:00
Tasks: Knowledge Graphs, Question Answering

Short technical support pages such as IBM Technotes are quite common in technical support domain. These pages can be very useful as the knowledge sources for technical support applications such as chatbots, search engines and question-answering (QA) systems. Information extracted from documents to drive technical support applications is often stored in the form of Knowledge Graph (KG). Building KGs from a large corpus of documents poses a challenge of granularity because a large number of entities and actions are present in each page. The KG becomes virtually unusable if all entities and actions from these pages are stored in the KG. Therefore, only key entities and actions from each page are extracted and stored in the KG. This approach however leads to loss of knowledge represented by entities and actions left out of the KG as they are no longer available to graph search and reasoning functions. We propose a set of techniques to create micro knowledge graph (micrograph) for each of such web pages. The micrograph stores all the entities and actions in a page and also takes advantage of the structure of the page to represent exactly in which part of that page these entities and actions appeared, and also how they relate to each other. These micrographs can be used as additional knowledge sources by technical support applications. We define schemas for representing semi-structured and plain text knowledge present in the technical support web pages. Solutions in technical support domain include procedures made of steps. We also propose a technique to extract procedures from these webpages and the schemas to represent them in the micrographs. We also discuss how technical support applications can take advantage of the micrographs.
RAKG:Document-level Retrieval Augmented Knowledge Graph Construction
Authors: Hairong Zhang, Jiaheng Si, Guohang Yan, Boyuan Qi, Pinlong Cai, Song Mao, Ding Wang, Botian Shi
Date: 2025-04-14 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Entity Disambiguation, graph construction, Knowledge Graphs, RAG, Retrieval, Retrieval-augmented Generation

With the rise of knowledge graph based retrieval-augmented generation (RAG) techniques such as GraphRAG and Pike-RAG, the role of knowledge graphs in enhancing the reasoning capabilities of large language models (LLMs) has become increasingly prominent. However, traditional Knowledge Graph Construction (KGC) methods face challenges like complex entity disambiguation, rigid schema definition, and insufficient cross-document knowledge integration. This paper focuses on the task of automatic document-level knowledge graph construction. It proposes the Document-level Retrieval Augmented Knowledge Graph Construction (RAKG) framework. RAKG extracts pre-entities from text chunks and utilizes these pre-entities as queries for RAG, effectively addressing the issue of long-context forgetting in LLMs and reducing the complexity of Coreference Resolution. In contrast to conventional KGC methods, RAKG more effectively captures global information and the interconnections among disparate nodes, thereby enhancing the overall performance of the model. Additionally, we transfer the RAG evaluation framework to the KGC field and filter and evaluate the generated knowledge graphs, thereby avoiding incorrectly generated entities and relationships caused by hallucinations in LLMs. We further developed the MINE dataset by constructing standard knowledge graphs for each article and experimentally validated the performance of RAKG. The results show that RAKG achieves an accuracy of 95.91 % on the MINE dataset, a 6.2 % point improvement over the current best baseline, GraphRAG (89.71 %). The code is available at https://github.com/LMMApplication/RAKG.
Augmented Relevance Datasets with Fine-Tuned Small LLMs
Authors: Quentin Fitte-Rey, Matyas Amrouche, Romain Deveaud
Date: 2025-04-14 00:00:00

Building high-quality datasets and labeling query-document relevance are essential yet resource-intensive tasks, requiring detailed guidelines and substantial effort from human annotators. This paper explores the use of small, fine-tuned large language models (LLMs) to automate relevance assessment, with a focus on improving ranking models' performance by augmenting their training dataset. We fine-tuned small LLMs to enhance relevance assessments, thereby improving dataset creation quality for downstream ranking model training. Our experiments demonstrate that these fine-tuned small LLMs not only outperform certain closed source models on our dataset but also lead to substantial improvements in ranking model performance. These results highlight the potential of leveraging small LLMs for efficient and scalable dataset augmentation, providing a practical solution for search engine optimization.
VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents
Authors: Ryota Tanaka, Taichi Iki, Taku Hasegawa, Kyosuke Nishida, Kuniko Saito, Jun Suzuki
Date: 2025-04-14 00:00:00
Proceeding: CVPR 2025 1
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Visual Question Answering

We aim to develop a retrieval-augmented generation (RAG) framework that answers questions over a corpus of visually-rich documents presented in mixed modalities (e.g., charts, tables) and diverse formats (e.g., PDF, PPTX). In this paper, we introduce a new RAG framework, VDocRAG, which can directly understand varied documents and modalities in a unified image format to prevent missing information that occurs by parsing documents to obtain text. To improve the performance, we propose novel self-supervised pre-training tasks that adapt large vision-language models for retrieval by compressing visual information into dense token representations while aligning them with textual content in documents. Furthermore, we introduce OpenDocVQA, the first unified collection of open-domain document visual question answering datasets, encompassing diverse document types and formats. OpenDocVQA provides a comprehensive resource for training and evaluating retrieval and question answering models on visually-rich documents in an open-domain setting. Experiments show that VDocRAG substantially outperforms conventional text-based RAG and has strong generalization capability, highlighting the potential of an effective RAG paradigm for real-world documents.
Outage Probability Analysis for OTFS with Finite Blocklength
Authors: Xin Zhang, Wensheng Lin, Lixin Li, Zhu Han, Tad Matsumoto
Date: 2025-04-13 00:00:00

Orthogonal time frequency space (OTFS) modulation is widely acknowledged as a prospective waveform for future wireless communication networks.To provide insights for the practical system design, this paper analyzes the outage probability of OTFS modulation with finite blocklength.To begin with, we present the system model and formulate the analysis of outage probability for OTFS with finite blocklength as an equivalent problem of calculating the outage probability with finite blocklength over parallel additive white Gaussian noise (AWGN) channels.Subsequently, we apply the equivalent noise approach to derive a lower bound on the outage probability of OTFS with finite blocklength under both average power allocation and water-filling power allocation strategies, respectively.Finally, the lower bounds of the outage probability are determined using the Monte-Carlo method for the two power allocation strategies.The impact of the number of resolvable paths and coding rates on the outage probability is analyzed, and the simulation results are compared with the theoretical lower bounds.
HD-RAG: Retrieval-Augmented Generation for Hybrid Documents Containing Text and Hierarchical Tables
Authors: Chi Zhang, Qiyang Chen
Date: 2025-04-13 00:00:00
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Semantic Similarity, Semantic Textual Similarity

With the rapid advancement of large language models (LLMs), Retrieval-Augmented Generation (RAG) effectively combines LLMs generative capabilities with external retrieval-based information. The Hybrid Document RAG task aims to integrate textual and hierarchical tabular data for more comprehensive retrieval and generation in complex scenarios. However, there is no existing dataset specifically designed for this task that includes both text and tabular data. Additionally, existing methods struggle to retrieve relevant tabular data and integrate it with text. Semantic similarity-based retrieval lacks accuracy, while table-specific methods fail to handle complex hierarchical structures effectively. Furthermore, the QA task requires complex reasoning and calculations, further complicating the challenge. In this paper, we propose a new large-scale dataset, DocRAGLib, specifically designed for the question answering (QA) task scenario under Hybrid Document RAG. To tackle these challenges, we introduce HD-RAG, a novel framework that incorporates a row-and-column level (RCL) table representation, employs a two-stage process combining ensemble and LLM-based retrieval, and integrates RECAP, which is designed for multi-step reasoning and complex calculations in Document-QA tasks. We conduct comprehensive experiments with DocRAGLib, showing that HD-RAG outperforms existing baselines in both retrieval accuracy and QA performance, demonstrating its effectiveness.
GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
Authors: Alfred Clemedtson, Borun Shi
Date: 2025-04-07 00:00:00
Tasks: Knowledge Graphs, RAG, Retrieval, Retrieval-augmented Generation

Large language models have shown remarkable language processing and reasoning ability but are prone to hallucinate when asked about private data. Retrieval-augmented generation (RAG) retrieves relevant data that fit into an LLM's context window and prompts the LLM for an answer. GraphRAG extends this approach to structured Knowledge Graphs (KGs) and questions regarding entities multiple hops away. The majority of recent GraphRAG methods either overlook the retrieval step or have ad hoc retrieval processes that are abstract or inefficient. This prevents them from being adopted when the KGs are stored in graph databases supporting graph query languages. In this work, we present GraphRAFT, a retrieve-and-reason framework that finetunes LLMs to generate provably correct Cypher queries to retrieve high-quality subgraph contexts and produce accurate answers. Our method is the first such solution that can be taken off-the-shelf and used on KGs stored in native graph DBs. Benchmarks suggest that our method is sample-efficient and scales with the availability of training data. Our method achieves significantly better results than all state-of-the-art models across all four standard metrics on two challenging Q\&As on large text-attributed KGs.
Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
Authors: Hengran Zhang, Keping Bi, Jiafeng Guo, Xiaojie Sun, Shihao Liu, Daiting Shi, Dawei Yin, Xueqi Cheng
Date: 2025-04-07 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Re-Ranking, Retrieval, Text Generation

Dense retrieval is a crucial task in Information Retrieval (IR) and is the foundation for downstream tasks such as re-ranking. Recently, large language models (LLMs) have shown compelling semantic understanding capabilities and are appealing to researchers studying dense retrieval. LLMs, as decoder-style generative models, are competent at language generation while falling short on modeling global information due to the lack of attention to tokens afterward. Inspired by the classical word-based language modeling approach for IR, i.e., the query likelihood (QL) model, we seek to sufficiently utilize LLMs' generative ability by QL maximization. However, instead of ranking documents with QL estimation, we introduce an auxiliary task of QL maximization to yield a better backbone for contrastively learning a discriminative retriever. We name our model as LLM-QL. To condense global document semantics to a single vector during QL modeling, LLM-QL has two major components, Attention Stop (AS) and Input Corruption (IC). AS stops the attention of predictive tokens to previous tokens until the ending token of the document. IC masks a portion of tokens in the input documents during prediction. Experiments on MSMARCO show that LLM-QL can achieve significantly better performance than other LLM-based retrievers and using QL estimated by LLM-QL for ranking outperforms word-based QL by a large margin.
Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation
Authors: Toqeer Ehsan, Thamar Solorio
Date: 2025-04-07 00:00:00
Tasks: Data Augmentation, Few-Shot Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named Entity Recognition (NER), a fundamental task in Natural Language Processing (NLP), has shown significant advancements for high-resource languages. However, due to a lack of annotated datasets and limited representation in Pre-trained Language Models (PLMs), it remains understudied and challenging for low-resource languages. To address these challenges, we propose a data augmentation technique that generates culturally plausible sentences and experiments on four low-resource Pakistani languages; Urdu, Shahmukhi, Sindhi, and Pashto. By fine-tuning multilingual masked Large Language Models (LLMs), our approach demonstrates significant improvements in NER performance for Shahmukhi and Pashto. We further explore the capability of generative LLMs for NER and data augmentation using few-shot learning.
Query Smarter, Trust Better? Exploring Search Behaviours for Verifying News Accuracy
Authors: David Elsweiler, Samy Ateia, Markus Bink, Gregor Donabauer, Marcos Fernández Pichel, Alexander Frummet, Udo Kruschwitz, David Losada, Bernd Ludwig, Selina Meyer, Noel Pascual Presa
Date: 2025-04-07 00:00:00
Tasks: Articles, Large Language Model, Misinformation

While it is often assumed that searching for information to evaluate misinformation will help identify false claims, recent work suggests that search behaviours can instead reinforce belief in misleading news, particularly when users generate queries using vocabulary from the source articles. Our research explores how different query generation strategies affect news verification and whether the way people search influences the accuracy of their information evaluation. A mixed-methods approach was used, consisting of three parts: (1) an analysis of existing data to understand how search behaviour influences trust in fake news, (2) a simulation of query generation strategies using a Large Language Model (LLM) to assess the impact of different query formulations on search result quality, and (3) a user study to examine how 'Boost' interventions in interface design can guide users to adopt more effective query strategies. The results show that search behaviour significantly affects trust in news, with successful searches involving multiple queries and yielding higher-quality results. Queries inspired by different parts of a news article produced search results of varying quality, and weak initial queries improved when reformulated using full SERP information. Although 'Boost' interventions had limited impact, the study suggests that interface design encouraging users to thoroughly review search results can enhance query formulation. This study highlights the importance of query strategies in evaluating news and proposes that interface design can play a key role in promoting more effective search practices, serving as one component of a broader set of interventions to combat misinformation.
Modelling Immunity in Agent-based Models
Authors: Gray Manicom, Emily Harvey, Joshua Looker, David Wu, Oliver Maclaren, Dion O' Neale
Date: 2025-04-18 00:00:00

Vaccination policies play a central role in public health interventions and models are often used to assess the effectiveness of these policies. Many vaccines are leaky, in which case the observed vaccine effectiveness depends on the force of infection. Within models, the immunity parameters required for agent-based models to achieve observed vaccine effectiveness values are further influenced by model features such as its transmission algorithm, contact network structure, and approach to simulating vaccination. We present a method for determining parameters in agent-based models such that a set of target immunity values is achieved. We construct a dataset of desired population-level immunity values against various disease outcomes considering both vaccination and prior infection from COVID-19. This dataset incorporates immunological data, data collection methodologies, immunity models, and biological insights. We then describe how we choose minimal parameters for continuous waning immunity curves that result in those target values being realized in simulations. We use simulations of the household secondary attack rates to establish a relationship between the protection per infection attempt and overall immunity, thus accounting for the dependence of protection from acquisition on model features and the force of infection.