pwc-archive/papers-with-abstracts huggingface dataset:
Normal forms in Virus Machines
Authors: A. Ramírez-de-Arellano, F. G. C. Cabarle, D. Orellana-Martín, M. J. Pérez-Jiménez
Date: 2024-09-05 00:00:00

In the present work, we further study the computational power of virus machines (VMs in short).VMs provide a computing paradigm inspired by the transmission and replication networks of viruses.VMs consist of process units (called hosts) structured by a directed graph whose arcs are called channels and an instruction graph that controls the transmissions of virus objects among hosts. The present work complements our understanding of the computing power of VMs by introducing normal forms; these expressions restrict the features in a given computing model.Some of the features that we restrict in our normal forms include (a) the number of hosts, (b) the number of instructions, and (c) the number of virus objects in each host. After we recall some known results on the computing power of VMs we give our series of normal forms, such as the size of the loops in the network, proving new characterisations of family of sets, such as finite sets, semilinear sets, or recursively enumerable sets (NRE).
GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding
Authors: Yukun Cao, Shuo Han, Zengyi Gao, Zezhong Ding, Xike Xie, S. Kevin Zhou
Date: 2024-09-05 00:00:00
Tasks: Attribute, RAG, Retrieval-augmented Generation

Although Large Language Models (LLMs) have demonstrated potential in processing graphs, they struggle with comprehending graphical structure information through prompts of graph description sequences, especially as the graph size increases. We attribute this challenge to the uneven memory performance of LLMs across different positions in graph description sequences, known as ''positional biases''. To address this, we propose GraphInsight, a novel framework aimed at improving LLMs' comprehension of both macro- and micro-level graphical information. GraphInsight is grounded in two key strategies: 1) placing critical graphical information in positions where LLMs exhibit stronger memory performance, and 2) investigating a lightweight external knowledge base for regions with weaker memory performance, inspired by retrieval-augmented generation (RAG). Moreover, GraphInsight explores integrating these two strategies into LLM agent processes for composite graph tasks that require multi-step reasoning. Extensive empirical studies on benchmarks with a wide range of evaluation tasks show that GraphInsight significantly outperforms all other graph description methods (e.g., prompting techniques and reordering strategies) in understanding graph structures of varying sizes.
Enhancing Healthcare LLM Trust with Atypical Presentations Recalibration
Authors: Jeremy Qin, Bang Liu, Quoc Dinh Nguyen
Date: 2024-09-05 00:00:00
Tasks: Decision Making, Medical Question Answering, Question Answering

Black-box large language models (LLMs) are increasingly deployed in various environments, making it essential for these models to effectively convey their confidence and uncertainty, especially in high-stakes settings. However, these models often exhibit overconfidence, leading to potential risks and misjudgments. Existing techniques for eliciting and calibrating LLM confidence have primarily focused on general reasoning datasets, yielding only modest improvements. Accurate calibration is crucial for informed decision-making and preventing adverse outcomes but remains challenging due to the complexity and variability of tasks these models perform. In this work, we investigate the miscalibration behavior of black-box LLMs within the healthcare setting. We propose a novel method, \textit{Atypical Presentations Recalibration}, which leverages atypical presentations to adjust the model's confidence estimates. Our approach significantly improves calibration, reducing calibration errors by approximately 60\% on three medical question answering datasets and outperforming existing methods such as vanilla verbalized confidence, CoT verbalized confidence and others. Additionally, we provide an in-depth analysis of the role of atypicality within the recalibration framework.
MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering
Authors: Mitchell DeHaven
Date: 2024-09-05 00:00:00
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Task 2

In this paper we present a multi-adapter retrieval augmented generation system (MARAGS) for Meta's Comprehensive RAG (CRAG) competition for KDD CUP 2024. CRAG is a question answering dataset contains 3 different subtasks aimed at realistic question and answering RAG related tasks, with a diverse set of question topics, question types, time dynamic answers, and questions featuring entities of varying popularity. Our system follows a standard setup for web based RAG, which uses processed web pages to provide context for an LLM to produce generations, while also querying API endpoints for additional information. MARAGS also utilizes multiple different adapters to solve the various requirements for these tasks with a standard cross-encoder model for ranking candidate passages relevant for answering the question. Our system achieved 2nd place for Task 1 as well as 3rd place on Task 2.
MaterialBENCH: Evaluating College-Level Materials Science Problem-Solving Abilities of Large Language Models
Authors: Michiko Yoshitake, Yuta Suzuki, Ryo Igarashi, Yoshitaka Ushiku, Keisuke Nagato
Date: 2024-09-05 00:00:00
Tasks: Multiple-choice

A college-level benchmark dataset for large language models (LLMs) in the materials science field, MaterialBENCH, is constructed. This dataset consists of problem-answer pairs, based on university textbooks. There are two types of problems: one is the free-response answer type, and the other is the multiple-choice type. Multiple-choice problems are constructed by adding three incorrect answers as choices to a correct answer, so that LLMs can choose one of the four as a response. Most of the problems for free-response answer and multiple-choice types overlap except for the format of the answers. We also conduct experiments using the MaterialBENCH on LLMs, including ChatGPT-3.5, ChatGPT-4, Bard (at the time of the experiments), and GPT-3.5 and GPT-4 with the OpenAI API. The differences and similarities in the performance of LLMs measured by the MaterialBENCH are analyzed and discussed. Performance differences between the free-response type and multiple-choice type in the same models and the influence of using system massages on multiple-choice problems are also studied. We anticipate that MaterialBENCH will encourage further developments of LLMs in reasoning abilities to solve more complicated problems and eventually contribute to materials research and discovery.
Well, that escalated quickly: The Single-Turn Crescendo Attack (STCA)
Authors: Alan Aqrawi, Arian Abbasi
Date: 2024-09-04 00:00:00
Tasks: Text Generation

This paper introduces a new method for adversarial attacks on large language models (LLMs) called the Single-Turn Crescendo Attack (STCA). Building on the multi-turn crescendo attack method introduced by Russinovich, Salem, and Eldan (2024), which gradually escalates the context to provoke harmful responses, the STCA achieves similar outcomes in a single interaction. By condensing the escalation into a single, well-crafted prompt, the STCA bypasses typical moderation filters that LLMs use to prevent inappropriate outputs. This technique reveals vulnerabilities in current LLMs and emphasizes the importance of stronger safeguards in responsible AI (RAI). The STCA offers a novel method that has not been previously explored.
Quantification of stylistic differences in human- and ASR-produced transcripts of African American English
Authors: Annika Heuser, Tyler Kendall, Miguel Del Rio, Quinten McNamara, Nishchal Bhandari, Corey Miller, Migüel Jetté
Date: 2024-09-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Common measures of accuracy used to assess the performance of automatic speech recognition (ASR) systems, as well as human transcribers, conflate multiple sources of error. Stylistic differences, such as verbatim vs non-verbatim, can play a significant role in ASR performance evaluation when differences exist between training and test datasets. The problem is compounded for speech from underrepresented varieties, where the speech to orthography mapping is not as standardized. We categorize the kinds of stylistic differences between 6 transcription versions, 4 human- and 2 ASR-produced, of 10 hours of African American English (AAE) speech. Focusing on verbatim features and AAE morphosyntactic features, we investigate the interactions of these categories with how well transcripts can be compared via word error rate (WER). The results, and overall analysis, help clarify how ASR outputs are a function of the decisions made by the training data's human transcribers.
Oddballness: universal anomaly detection with language models
Authors: Filip Graliński, Ryszard Staruch, Krzysztof Jurkiewicz
Date: 2024-09-04 00:00:00
Tasks: Anomaly Detection, Grammatical Error Detection, Language Modeling, Language Modelling

We present a new method to detect anomalies in texts (in general: in sequences of any data), using language models, in a totally unsupervised manner. The method considers probabilities (likelihoods) generated by a language model, but instead of focusing on low-likelihood tokens, it considers a new metric introduced in this paper: oddballness. Oddballness measures how ``strange'' a given token is according to the language model. We demonstrate in grammatical error detection tasks (a specific case of text anomaly detection) that oddballness is better than just considering low-likelihood events, if a totally unsupervised setup is assumed.
Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications
Authors: Tong Bu, Maohua Li, Zhaofei Yu
Date: 2024-09-05 00:00:00
Proceeding: CVPR 2025 1
Tasks: image-classification, Image Classification, object-detection, Object Detection, Semantic Segmentation, Video Classification

Spiking Neural Networks (SNNs) have emerged as a promising substitute for Artificial Neural Networks (ANNs) due to their advantages of fast inference and low power consumption. However, the lack of efficient training algorithms has hindered their widespread adoption. Even efficient ANN-SNN conversion methods necessitate quantized training of ANNs to enhance the effectiveness of the conversion, incurring additional training costs. To address these challenges, we propose an efficient ANN-SNN conversion framework with only inference scale complexity. The conversion framework includes a local threshold balancing algorithm, which enables efficient calculation of the optimal thresholds and fine-grained adjustment of the threshold value by channel-wise scaling. We also introduce an effective delayed evaluation strategy to mitigate the influence of the spike propagation delays. We demonstrate the scalability of our framework in typical computer vision tasks: image classification, semantic segmentation, object detection, and video classification. Our algorithm outperforms existing methods, highlighting its practical applicability and efficiency. Moreover, we have evaluated the energy consumption of the converted SNNs, demonstrating their superior low-power advantage compared to conventional ANNs. This approach simplifies the deployment of SNNs by leveraging open-source pre-trained ANN models, enabling fast, low-power inference with negligible performance reduction. Code is available at https://github.com/putshua/Inference-scale-ANN-SNN.
Pareto Set Prediction Assisted Bilevel Multi-objective Optimization
Authors: Bing Wang, Hemant K. Singh, Tapabrata Ray
Date: 2024-09-05 00:00:00
Tasks: Bilevel Optimization, Prediction

Bilevel optimization problems comprise an upper level optimization task that contains a lower level optimization task as a constraint. While there is a significant and growing literature devoted to solving bilevel problems with single objective at both levels using evolutionary computation, there is relatively scarce work done to address problems with multiple objectives (BLMOP) at both levels. For black-box BLMOPs, the existing evolutionary techniques typically utilize nested search, which in its native form consumes large number of function evaluations. In this work, we propose to reduce this expense by predicting the lower level Pareto set for a candidate upper level solution directly, instead of conducting an optimization from scratch. Such a prediction is significantly challenging for BLMOPs as it involves one-to-many mapping scenario. We resolve this bottleneck by supplementing the dataset using a helper variable and construct a neural network, which can then be trained to map the variables in a meaningful manner. Then, we embed this initialization within a bilevel optimization framework, termed Pareto set prediction assisted evolutionary bilevel multi-objective optimization (PSP-BLEMO). Systematic experiments with existing state-of-the-art methods are presented to demonstrate its benefit. The experiments show that the proposed approach is competitive across a range of problems, including both deceptive and non-deceptive problems
A New Forward Discriminant Analysis Framework Based On Pillai's Trace and ULDA
Authors: Siyu Wang, Kehui Yao
Date: 2024-09-05 00:00:00

Linear discriminant analysis (LDA), a traditional classification tool, suffers from limitations such as sensitivity to noise and computational challenges when dealing with non-invertible within-class scatter matrices. Traditional stepwise LDA frameworks, which iteratively select the most informative features, often exacerbate these issues by relying heavily on Wilks' $\Lambda$, potentially causing premature stopping of the selection process. This paper introduces a novel forward discriminant analysis framework that integrates Pillai's trace with Uncorrelated Linear Discriminant Analysis (ULDA) to address these challenges, and offers a unified and stand-alone classifier. Through simulations and real-world datasets, the new framework demonstrates effective control of Type I error rates and improved classification accuracy, particularly in cases involving perfect group separations. The results highlight the potential of this approach as a robust alternative to the traditional stepwise LDA framework.
HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps
Authors: Jizhou Huang, Haifeng Wang, Yibo Sun, Miao Fan, Zhengjie Huang, Chunyuan Yuan, Yawen Li
Date: 2024-09-05 00:00:00
Tasks: Graph Attention, point of interests, Retrieval

The increasing interest in international travel has raised the demand of retrieving point of interests in multiple languages. This is even superior to find local venues such as restaurants and scenic spots in unfamiliar languages when traveling abroad. Multilingual POI retrieval, enabling users to find desired POIs in a demanded language using queries in numerous languages, has become an indispensable feature of today's global map applications such as Baidu Maps. This task is non-trivial because of two key challenges: (1) visiting sparsity and (2) multilingual query-POI matching. To this end, we propose a Heterogeneous Graph Attention Matching Network (HGAMN) to concurrently address both challenges. Specifically, we construct a heterogeneous graph that contains two types of nodes: POI node and query node using the search logs of Baidu Maps. To alleviate challenge \#1, we construct edges between different POI nodes to link the low-frequency POIs with the high-frequency ones, which enables the transfer of knowledge from the latter to the former. To mitigate challenge \#2, we construct edges between POI and query nodes based on the co-occurrences between queries and POIs, where queries in different languages and formulations can be aggregated for individual POIs. Moreover, we develop an attention-based network to jointly learn node representations of the heterogeneous graph and further design a cross-attention module to fuse the representations of both types of nodes for query-POI relevance scoring. Extensive experiments conducted on large-scale real-world datasets from Baidu Maps demonstrate the superiority and effectiveness of HGAMN. In addition, HGAMN has already been deployed in production at Baidu Maps, and it successfully keeps serving hundreds of millions of requests every day.
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search
Authors: Miao Fan, Jiacheng Guo, Shuai Zhu, Shuo Miao, Mingming Sun, Ping Li
Date: 2024-09-05 00:00:00
Tasks: Active Learning

Baidu runs the largest commercial web search engine in China, serving hundreds of millions of online users every day in response to a great variety of queries. In order to build a high-efficiency sponsored search engine, we used to adopt a three-layer funnel-shaped structure to screen and sort hundreds of ads from billions of ad candidates subject to the requirement of low response latency and the restraints of computing resources. Given a user query, the top matching layer is responsible for providing semantically relevant ad candidates to the next layer, while the ranking layer at the bottom concerns more about business indicators (e.g., CPM, ROI, etc.) of those ads. The clear separation between the matching and ranking objectives results in a lower commercial return. The Mobius project has been established to address this serious issue. It is our first attempt to train the matching layer to consider CPM as an additional optimization objective besides the query-ad relevance, via directly predicting CTR (click-through rate) from billions of query-ad pairs. Specifically, this paper will elaborate on how we adopt active learning to overcome the insufficiency of click history at the matching layer when training our neural click networks offline, and how we use the SOTA ANN search technique for retrieving ads more efficiently (Here ``ANN'' stands for approximate nearest neighbor search). We contribute the solutions to Mobius-V1 as the first version of our next generation query-ad matching system.
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation
Authors: Li Wang, Qiang Wu, Min Xu
Date: 2024-09-05 00:00:00
Tasks: Contrastive Learning, Federated Learning, Privacy Preserving, Transfer Learning

Cross-domain recommendation (CDR) aims to improve recommendation accuracy in sparse domains by transferring knowledge from data-rich domains. However, existing CDR approaches often assume that user-item interaction data across domains is publicly available, neglecting user privacy concerns. Additionally, they experience performance degradation with sparse overlapping users due to their reliance on a large number of fully shared users for knowledge transfer. To address these challenges, we propose a Federated Prototype-based Contrastive Learning (CL) framework for Privacy Preserving CDR, called FedPCL-CDR. This approach utilizes non-overlapping user information and differential prototypes to improve model performance within a federated learning framework. FedPCL-CDR comprises two key modules: local domain (client) learning and global server aggregation. In the local domain, FedPCL-CDR first clusters all user data and utilizes local differential privacy (LDP) to learn differential prototypes, effectively utilizing non-overlapping user information and protecting user privacy. It then conducts knowledge transfer by employing both local and global prototypes returned from the server in a CL manner. Meanwhile, the global server aggregates differential prototypes sent from local domains to learn both local and global prototypes. Extensive experiments on four CDR tasks across Amazon and Douban datasets demonstrate that FedPCL-CDR surpasses SOTA baselines. We release our code at https://github.com/Lili1013/FedPCL CDR
IMGCN: Interpretable Masked Graph Convolution Network for Pedestrian Trajectory Prediction
Authors: Wangxing Chen, Haifeng Sang, Jinyu Wang, Zishan Zhao
Date: 2024-08-13 00:00:00
Proceeding: Transportmetrica B: Transport Dynamics 2024 8
Tasks: Autonomous Driving, Pedestrian Trajectory Prediction, Prediction, Trajectory Prediction

Pedestrian trajectory prediction holds significant research value in various fields, such as autonomous driving, autonomous service robots, and human flow monitoring. Two key challenges in pedestrian trajectory prediction are the modeling of pedestrian social interactions and movement factors. Previous methods have not utilized interpretable information to explore complex situations when modeling social interactions. These methods also focus too much on temporal interactions at each moment when modeling movement factors and are therefore susceptible to slight motion changes. To solve the above problems, we propose an Interpretable Masked Graph Convolution Network (IMGCN) for pedestrian trajectory prediction. The IMGCN utilizes interpretable information such as the pedestrian view area, distance, and motion direction to intelligently mask interaction features, resulting in more precise modeling of social interaction and movement factors. Specifically, we design a spatial and a temporal branch to model pedestrians' social interaction and movement factors, respectively. Within the spatial branch, the view-distance mask module masks pedestrian social interaction by determining whether the pedestrian is within a certain distance and view area to achieve more accurate interaction modeling. In the temporal branch, the motion offset mask module masks pedestrian temporal interaction according to the offset degree of their motion direction to achieve accurate modeling of movement factors. Ultimately, the 2D Gaussian distribution parameters of future trajectory points are predicted by the temporal convolution networks for multi-modal trajectory prediction. On the ETH, UCY and SDD datasets, our proposed method outperforms the baseline models in terms of average displacement error and final displacement error. The code is publicly available at https://github.com/Chenwangxing/IMGCN_master.
SpCQL: A Semantic Parsing Dataset for Converting Natural Language into Cypher
Authors: Aibo Guo, Xinyi Li, Guanchen Xiao, Zhen Tan, Xiang Zhao
Date: 2022-10-17 00:00:00
Proceeding: CIKM 2022 10
Tasks: Natural Language Queries, Semantic Parsing, text-to-Cypher, Text to SQL, Text-To-SQL

The Neo4j query language Cypher enables efficient querying for graphs and has become the most popular graph database language. Due to its complexities, semantic parsing (similar to Text-to-SQL) that translates natural language queries to Cypher becomes highly desirable. We propose the first Text-to-CQL dataset, SpCQL, which contains one Neo4j graph database, 10,000 manually annotated natural language queries and the matching Cypher queries (CQL). Correspondingly, based on this dataset, we define a new semantic parsing task Text-to-CQL. The Text-to-CQL task differs from the traditional Text-to-SQL task due to CQL being more flexible and versatile, especially for schema queries, which brings precedented challenges for the translation process. Although current SOTA Text-to-SQL models utilize SQL schema and contents, they do not scale up to large-scale graph databases. Besides, due to the absence of the primary and foreign keys in Cypher, which are essential for the multi-table Text-to-SQL task, existing Text-to-SQL models are rendered ineffective in this new task and have to be adapted to work. We propose three baselines based on the Seq2Seq framework and conduct experiments on the SpCQL dataset. The experiments yield undesirable results for existing models, hence pressing for subsequent research that considers the characteristics of SQL. The dataset is available at https://github.com/Guoaibo/Text-to-CQL.