pwc-archive/papers-with-abstracts huggingface dataset:
Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models
Authors: Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Bin Gu, Mengfei Yang, Ge Li
Date: 2024-02-24 00:00:00
Tasks: HumanEval, Memorization

Recent statements about the impressive capabilities of large language models (LLMs) are usually supported by evaluating on open-access benchmarks. Considering the vast size and wide-ranging sources of LLMs' training data, it could explicitly or implicitly include test data, leading to LLMs being more susceptible to data contamination. However, due to the opacity of training data, the black-box access of models, and the rapid growth of synthetic training data, detecting and mitigating data contamination for LLMs faces significant challenges. In this paper, we propose CDD, which stands for Contamination Detection via output Distribution for LLMs. CDD necessitates only the sampled texts to detect data contamination, by identifying the peakedness of LLM's output distribution. To mitigate the impact of data contamination in evaluation, we also present TED: Trustworthy Evaluation via output Distribution, based on the correction of LLM's output distribution. To facilitate this study, we introduce two benchmarks, i.e., DetCon and ComiEval, for data contamination detection and contamination mitigation evaluation tasks. Extensive experimental results show that CDD achieves the average relative improvements of 21.8\%-30.2\% over other contamination detection approaches in terms of Accuracy, F1 Score, and AUC metrics, and can effectively detect implicit contamination. TED substantially mitigates performance improvements up to 66.9\% attributed to data contamination across various contamination setups. In real-world applications, we reveal that ChatGPT exhibits a high potential to suffer from data contamination on HumanEval benchmark.
Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA
Authors: Wentao Mo, Yang Liu
Date: 2024-02-24 00:00:00
Tasks: 3D Question Answering (3D-QA), Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In 3D Visual Question Answering (3D VQA), the scarcity of fully annotated data and limited visual content diversity hampers the generalization to novel scenes and 3D concepts (e.g., only around 800 scenes are utilized in ScanQA and SQA dataset). Current approaches resort supplement 3D reasoning with 2D information. However, these methods face challenges: either they use top-down 2D views that introduce overly complex and sometimes question-irrelevant visual clues, or they rely on globally aggregated scene/image-level representations from 2D VLMs, losing the fine-grained vision-language correlations. To overcome these limitations, our approach utilizes question-conditional 2D view selection procedure, pinpointing semantically relevant 2D inputs for crucial visual clues. We then integrate this 2D knowledge into the 3D-VQA system via a two-branch Transformer structure. This structure, featuring a Twin-Transformer design, compactly combines 2D and 3D modalities and captures fine-grained correlations between modalities, allowing them mutually augmenting each other. Integrating proposed mechanisms above, we present BridgeQA, that offers a fresh perspective on multi-modal transformer-based architectures for 3D-VQA. Experiments validate that BridgeQA achieves state-of-the-art on 3D-VQA datasets and significantly outperforms existing solutions. Code is available at $\href{https://github.com/matthewdm0816/BridgeQA}{\text{this URL}}$.
Decoding Intelligence: A Framework for Certifying Knowledge Comprehension in LLMs
Authors: Isha Chaudhary, Vedaant V. Jain, Gagandeep Singh
Date: 2024-02-24 00:00:00
Tasks: Benchmarking, Knowledge Graphs

Knowledge comprehension capability is an important aspect of human intelligence. As Large Language Models (LLMs) are being envisioned as superhuman agents, it is crucial for them to be proficient at knowledge comprehension. However, existing benchmarking studies do not provide consistent, generalizable, and formal guarantees on the knowledge comprehension capabilities of LLMs. In this work, we propose the first framework to certify knowledge comprehension in LLMs with formal probabilistic guarantees. Our certificates are quantitative -- they consist of high-confidence, tight bounds on the probability that a target LLM gives the correct answer on any knowledge comprehension prompt sampled from a distribution. We design and certify novel specifications that precisely represent distributions of knowledge comprehension prompts leveraging knowledge graphs. We certify SOTA LLMs for specifications over the Wikidata5m knowledge graph. We find that the knowledge comprehension capability improves significantly with scaling the size of the models.
MultiContrievers: Analysis of Dense Retrieval Representations
Authors: Seraphina Goldfarb-Tarrant, Pedro Rodriguez, Jane Dwivedi-Yu, Patrick Lewis
Date: 2024-02-24 00:00:00
Tasks: Retrieval, Sensitivity

Dense retrievers compress source documents into (possibly lossy) vector representations, yet there is little analysis of what information is lost versus preserved, and how it affects downstream tasks. We conduct the first analysis of the information captured by dense retrievers compared to the language models they are based on (e.g., BERT versus Contriever). We use 25 MultiBert checkpoints as randomized initialisations to train MultiContrievers, a set of 25 contriever models. We test whether specific pieces of information -- such as gender and occupation -- can be extracted from contriever vectors of wikipedia-like documents. We measure this extractability via information theoretic probing. We then examine the relationship of extractability to performance and gender bias, as well as the sensitivity of these results to many random initialisations and data shuffles. We find that (1) contriever models have significantly increased extractability, but extractability usually correlates poorly with benchmark performance 2) gender bias is present, but is not caused by the contriever representations 3) there is high sensitivity to both random initialisation and to data shuffle, suggesting that future retrieval research should test across a wider spread of both.
Predicting Outcomes in Video Games with Long Short Term Memory Networks
Authors: Kittimate Chulajata, Sean Wu, Fabien Scalzo, Eun Sang Cha
Date: 2024-02-24 00:00:00
Tasks: Decision Making, Time Series, Time Series Forecasting

Forecasting winners in E-sports with real-time analytics has the potential to further engage audiences watching major tournament events. However, making such real-time predictions is challenging due to unpredictable variables within the game involving diverse player strategies and decision-making. Our work attempts to enhance audience engagement within video game tournaments by introducing a real-time method of predicting wins. Our Long Short Term Memory Network (LSTMs) based approach enables efficient predictions of win-lose outcomes by only using the health indicator of each player as a time series. As a proof of concept, we evaluate our model's performance within a classic, two-player arcade game, Super Street Fighter II Turbo. We also benchmark our method against state of the art methods for time series forecasting; i.e. Transformer models found in large language models (LLMs). Finally, we open-source our data set and code in hopes of furthering work in predictive analysis for arcade games.
Explainable Contrastive and Cost-Sensitive Learning for Cervical Cancer Classification
Authors: Ashfiqun Mustari, Rushmia Ahmed, Afsara Tasnim, Jakia Sultana Juthi, G M Shahariar
Date: 2024-02-24 00:00:00
Tasks: Cancer Classification, Contrastive Learning

This paper proposes an efficient system for classifying cervical cancer cells using pre-trained convolutional neural networks (CNNs). We first fine-tune five pre-trained CNNs and minimize the overall cost of misclassification by prioritizing accuracy for certain classes that have higher associated costs or importance. To further enhance the performance of the models, supervised contrastive learning is included to make the models more adept at capturing important features and patterns. Extensive experimentation are conducted to evaluate the proposed system on the SIPaKMeD dataset. The experimental results demonstrate the effectiveness of the developed system, achieving an accuracy of 97.29%. To make our system more trustworthy, we have employed several explainable AI techniques to interpret how the models reached a specific decision. The implementation of the system can be found at - https://github.com/isha-67/CervicalCancerStudy.
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
Authors: Guangyu Zhu, Yiqin Deng, Xianhao Chen, Haixia Zhang, Yuguang Fang, Tan F. Wong
Date: 2024-02-24 00:00:00
Tasks: Federated Learning

Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet challenging problem. In this paper, we propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server under a split federated learning framework with heterogeneous end devices (EDs). By splitting the model into different submodels between the server and EDs, our approach jointly optimizes user-side workload and server-side computing resource allocation by considering users' heterogeneity. We formulate the whole optimization problem as a mixed-integer non-linear program, which is an NP-hard problem, and develop an iterative approach to obtain an approximate solution efficiently. Extensive simulations have been conducted to validate the significantly increased efficiency of our ESFL approach compared with standard federated learning, split learning, and splitfed learning.
Statistical Games
Authors: Jozsef Konczer
Date: 2024-02-24 00:00:00
Tasks: Decision Making

This work contains the mathematical exploration of a few prototypical games in which central concepts from statistics and probability theory naturally emerge. The first two kinds of games are termed Fisher and Bayesian games, which are connected to Frequentist and Bayesian statistics, respectively. Later, a more general type of game is introduced, termed Statistical game, in which a further parameter, the players' relative risk aversion, can be set. In this work, we show that Fisher and Bayesian games can be viewed as limiting cases of Statistical games. Therefore, Statistical games can be viewed as a unified framework, incorporating both Frequentist and Bayesian statistics. Furthermore, a philosophical framework is (re-)presented -- often referred to as minimax regret criterion -- as a general approach to decision making. The main motivation for this work was to embed Bayesian statistics into a broader decision-making framework, where, based on collected data, actions with consequences have to be made, which can be translated to utilities (or rewards/losses) of the decision-maker. The work starts with the simplest possible toy model, related to hypothesis testing and statistical inference. This choice has two main benefits: i.) it allows us to determine (conjecture) the behaviour of the equilibrium strategies in various limiting cases ii.) this way, we can introduce Statistical games without requiring additional stochastic parameters. The work contains game theoretical methods related to two-player, non-cooperative games to determine and prove equilibrium strategies of Fisher, Bayesian and Statistical games. It also relies on analytical tools for derivations concerning various limiting cases.
Multiple Instance Learning for Glioma Diagnosis using Hematoxylin and Eosin Whole Slide Images: An Indian Cohort Study
Authors: Ekansh Chauhan, Amit Sharma, Megha S Uppin, C. V. Jawahar, P. K. Vinod
Date: 2024-02-24 00:00:00
Tasks: Decision Making, Diagnostic, Management, Multiple Instance Learning, whole slide images

The effective management of brain tumors relies on precise typing, subtyping, and grading. This study advances patient care with findings from rigorous multiple instance learning experimentations across various feature extractors and aggregators in brain tumor histopathology. It establishes new performance benchmarks in glioma subtype classification across multiple datasets, including a novel dataset focused on the Indian demographic (IPD- Brain), providing a valuable resource for existing research. Using a ResNet-50, pretrained on histopathology datasets for feature extraction, combined with the Double-Tier Feature Distillation (DTFD) feature aggregator, our approach achieves state-of-the-art AUCs of 88.08 on IPD-Brain and 95.81 on the TCGA-Brain dataset, respectively, for three-way glioma subtype classification. Moreover, it establishes new benchmarks in grading and detecting IHC molecular biomarkers (IDH1R132H, TP53, ATRX, Ki-67) through H&E stained whole slide images for the IPD-Brain dataset. The work also highlights a significant correlation between the model decision-making processes and the diagnostic reasoning of pathologists, underscoring its capability to mimic professional diagnostic procedures.
Reward Design for Justifiable Sequential Decision-Making
Authors: Aleksa Sukovic, Goran Radanovic
Date: 2024-02-24 00:00:00
Tasks: Decision Making, Sequential Decision Making

Equipping agents with the capacity to justify made decisions using supporting evidence represents a cornerstone of accountable decision-making. Furthermore, ensuring that justifications are in line with human expectations and societal norms is vital, especially in high-stakes situations such as healthcare. In this work, we propose the use of a debate-based reward model for reinforcement learning agents, where the outcome of a zero-sum debate game quantifies the justifiability of a decision in a particular state. This reward model is then used to train a justifiable policy, whose decisions can be more easily corroborated with supporting evidence. In the debate game, two argumentative agents take turns providing supporting evidence for two competing decisions. Given the proposed evidence, a proxy of a human judge evaluates which decision is better justified. We demonstrate the potential of our approach in learning policies for prescribing and justifying treatment decisions of septic patients. We show that augmenting the reward with the feedback signal generated by the debate-based reward model yields policies highly favored by the judge when compared to the policy obtained solely from the environment rewards, while hardly sacrificing any performance. Moreover, in terms of the overall performance and justifiability of trained policies, the debate-based feedback is comparable to the feedback obtained from an ideal judge proxy that evaluates decisions using the full information encoded in the state. This suggests that the debate game outputs key information contained in states that is most relevant for evaluating decisions, which in turn substantiates the practicality of combining our approach with human-in-the-loop evaluations. Lastly, we showcase that agents trained via multi-agent debate learn to propose evidence that is resilient to refutations and closely aligns with human preferences.
Cooperation and Control in Delegation Games
Authors: Oliver Sourbut, Lewis Hammond, Harriet Wood
Date: 2024-02-24 00:00:00
Tasks: Autonomous Vehicles

Many settings of interest involving humans and machines -- from virtual personal assistants to autonomous vehicles -- can naturally be modelled as principals (humans) delegating to agents (machines), which then interact with each other on their principals' behalf. We refer to these multi-principal, multi-agent scenarios as delegation games. In such games, there are two important failure modes: problems of control (where an agent fails to act in line their principal's preferences) and problems of cooperation (where the agents fail to work well together). In this paper we formalise and analyse these problems, further breaking them down into issues of alignment (do the players have similar preferences?) and capabilities (how competent are the players at satisfying those preferences?). We show -- theoretically and empirically -- how these measures determine the principals' welfare, how they can be estimated using limited observations, and thus how they might be used to help us design more aligned and cooperative AI systems.
DART: Depth-Enhanced Accurate and Real-Time Background Matting
Authors: Hanxi Li, Guofeng Li, Bo Li, Lin Wu, Yan Cheng
Date: 2024-02-24 00:00:00
Tasks: Bayesian Inference, Edge-computing, Image Matting

Matting with a static background, often referred to as ``Background Matting" (BGM), has garnered significant attention within the computer vision community due to its pivotal role in various practical applications like webcasting and photo editing. Nevertheless, achieving highly accurate background matting remains a formidable challenge, primarily owing to the limitations inherent in conventional RGB images. These limitations manifest in the form of susceptibility to varying lighting conditions and unforeseen shadows. In this paper, we leverage the rich depth information provided by the RGB-Depth (RGB-D) cameras to enhance background matting performance in real-time, dubbed DART. Firstly, we adapt the original RGB-based BGM algorithm to incorporate depth information. The resulting model's output undergoes refinement through Bayesian inference, incorporating a background depth prior. The posterior prediction is then translated into a "trimap," which is subsequently fed into a state-of-the-art matting algorithm to generate more precise alpha mattes. To ensure real-time matting capabilities, a critical requirement for many real-world applications, we distill the backbone of our model from a larger and more versatile BGM network. Our experiments demonstrate the superior performance of the proposed method. Moreover, thanks to the distillation operation, our method achieves a remarkable processing speed of 33 frames per second (fps) on a mid-range edge-computing device. This high efficiency underscores DART's immense potential for deployment in mobile applications}
Empowering Large Language Model Agents through Action Learning
Authors: Haiteng Zhao, Chang Ma, Guoyin Wang, Jing Su, Lingpeng Kong, Jingjing Xu, Zhi-Hong Deng, Hongxia Yang
Date: 2024-02-24 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, model

Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behavior. In this work, we argue that the capacity to learn new actions from experience is fundamental to the advancement of learning in LLM agents. While humans naturally expand their action spaces and develop skills through experiential learning, LLM agents typically operate within fixed action spaces, limiting their potential for growth. To address these challenges, our study explores open-action learning for language agents. We introduce a framework LearnAct with an iterative learning strategy to create and improve actions in the form of Python functions. In each iteration, LLM revises and updates the currently available actions based on the errors identified in unsuccessful training tasks, thereby enhancing action effectiveness. Our experimental evaluations across Robotic Planning and Alfworld environments reveal that after learning on a few training task instances, our approach to open-action learning markedly improves agent performance for the type of task (by 32 percent in AlfWorld compared to ReAct+Reflexion, for instance) highlighting the importance of experiential action learning in the development of more intelligent LLM agents.
Optimal Zero-Shot Detector for Multi-Armed Attacks
Authors: Federica Granese, Marco Romanelli, Pablo Piantanida
Date: 2024-02-24 00:00:00

This paper explores a scenario in which a malicious actor employs a multi-armed attack strategy to manipulate data samples, offering them various avenues to introduce noise into the dataset. Our central objective is to protect the data by detecting any alterations to the input. We approach this defensive strategy with utmost caution, operating in an environment where the defender possesses significantly less information compared to the attacker. Specifically, the defender is unable to utilize any data samples for training a defense model or verifying the integrity of the channel. Instead, the defender relies exclusively on a set of pre-existing detectors readily available "off the shelf". To tackle this challenge, we derive an innovative information-theoretic defense approach that optimally aggregates the decisions made by these detectors, eliminating the need for any training data. We further explore a practical use-case scenario for empirical evaluation, where the attacker possesses a pre-trained classifier and launches well-known adversarial attacks against it. Our experiments highlight the effectiveness of our proposed solution, even in scenarios that deviate from the optimal setup.
Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data
Authors: Yong Wang, Yanlin Zhou, Huan Ji, Zheng He, Xinyu Shen
Date: 2024-02-24 00:00:00
Tasks: Autonomous Driving

In recent years, the rapid development of high-precision map technology combined with artificial intelligence has ushered in a new development opportunity in the field of intelligent vehicles. High-precision map technology is an important guarantee for intelligent vehicles to achieve autonomous driving. However, due to the lack of research on high-precision map technology, it is difficult to rationally use this technology in the field of intelligent vehicles. Therefore, relevant researchers studied a fast and effective algorithm to generate high-precision GPS data from a large number of low-precision GPS trajectory data fusion, and generated several key data points to simplify the description of GPS trajectory, and realized the "crowdsourced update" model based on a large number of social vehicles for map data collection came into being. This kind of algorithm has the important significance to improve the data accuracy, reduce the measurement cost and reduce the data storage space. On this basis, this paper analyzes the implementation form of crowdsourcing map, so as to improve the various information data in the high-precision map according to the actual situation, and promote the high-precision map can be reasonably applied to the intelligent car.