pwc-archive/papers-with-abstracts huggingface dataset:
LLMs Can Patch Up Missing Relevance Judgments in Evaluation
Authors: Shivani Upadhyay, Ehsan Kamalloo, Jimmy Lin
Date: 2024-05-08 00:00:00
Tasks: Information Retrieval, Retrieval

Unjudged documents or holes in information retrieval benchmarks are considered non-relevant in evaluation, yielding no gains in measuring effectiveness. However, these missing judgments may inadvertently introduce biases into the evaluation as their prevalence for a retrieval model is heavily contingent on the pooling process. Thus, filling holes becomes crucial in ensuring reliable and accurate evaluation. Collecting human judgment for all documents is cumbersome and impractical. In this paper, we aim at leveraging large language models (LLMs) to automatically label unjudged documents. Our goal is to instruct an LLM using detailed instructions to assign fine-grained relevance judgments to holes. To this end, we systematically simulate scenarios with varying degrees of holes by randomly dropping relevant documents from the relevance judgment in TREC DL tracks. Our experiments reveal a strong correlation between our LLM-based method and ground-truth relevance judgments. Based on our simulation experiments conducted on three TREC DL datasets, in the extreme scenario of retaining only 10% of judgments, our method achieves a Kendall tau correlation of 0.87 and 0.92 on an average for Vicu\~na-7B and GPT-3.5 Turbo respectively.
Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue
Authors: Nhat Tran, Diane Litman
Date: 2024-05-07 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Response Generation, Retrieval

Knowledge retrieval is one of the major challenges in building a knowledge-grounded dialogue system. A common method is to use a neural retriever with a distributed approximate nearest-neighbor database to quickly find the relevant knowledge sentences. In this work, we propose an approach that utilizes topic modeling on the knowledge base to further improve retrieval accuracy and as a result, improve response generation. Additionally, we experiment with a large language model, ChatGPT, to take advantage of the improved retrieval performance to further improve the generation results. Experimental results on two datasets show that our approach can increase retrieval and generation performance. The results also indicate that ChatGPT is a better response generator for knowledge-grounded dialogue when relevant knowledge is provided.
Generalization of the Alpha-Stable Distribution with the Degree of Freedom
Authors: Stephen H. Lihn
Date: 2024-05-07 00:00:00

A Wright function based framework is proposed to combine and extend several distribution families. The $\alpha$-stable distribution is generalized by adding the degree of freedom parameter. The PDF of this two-sided super distribution family subsumes those of the original $\alpha$-stable, Student's t distributions, as well as the exponential power distribution and the modified Bessel function of the second kind. Its CDF leads to a fractional extension of the Gauss hypergeometric function. The degree of freedom makes possible for valid variance, skewness, and kurtosis, just like Student's t. The original $\alpha$-stable distribution is viewed as having one degree of freedom, that explains why it lacks most of the moments. A skew-Gaussian kernel is derived from the characteristic function of the $\alpha$-stable law, which maximally preserves the law in the new framework. To facilitate such framework, the stable count distribution is generalized as the fractional extension of the generalized gamma distribution. It provides rich subordination capabilities, one of which is the fractional $\chi$ distribution that supplies the needed 'degree of freedom' parameter. Hence, the "new" $\alpha$-stable distribution is a "ratio distribution" of the skew-Gaussian kernel and the fractional $\chi$ distribution. Mathematically, it is a new form of higher transcendental function under the Wright function family. Last, the new univariate symmetric distribution is extended to the multivariate elliptical distribution successfully.
Beyond classical Hamilton's Rule. State distribution asymmetry and the dynamics of altruism
Authors: Krzysztof Argasinski, Ryszard Rudnicki
Date: 2019-12-01 00:00:00
Tasks: counterfactual, Wrong PDF attached

This paper analyzes the relationships between demographic and state-based evolutionary games and Hamilton's rule. It is shown that the classical Hamilton's rule (counterfactual method), combined with demographic payoffs, leads to easily testable models. It works well when the roles of donor and receiver are randomly drawn during each interaction event. This is illustrated by the alarm call example. However, we can imagine situations in which role-switching results from external mechanism, such as, fluxes of individuals between the border and the interior of the habitat, when only border individuals may spot the threat and warn their neighbors. To cover these cases, a new model is extended to the case with explicit dynamics of the role distributions among carriers of different strategies, driven by some general mechanisms. It is shown that even in the case when fluxes between roles are driven by neutral mechanisms (acting in the same way on all strategies), differences in mortality in the focal interaction lead to different distributions of roles for different strategies. This leads to a more complex rule for cooperation than the classical Hamilton's rule. In addition to the cost and benefit components, the new rule contains a third component weighted by the difference in proportions of the donors among carriers of both strategies. Depending on the sign, this component can be termed the "survival surplus", when the donors survival have greater survival than receivers, or the "sacrifice cost" (when it decreases the benefit), when the receiver's survival exceeds that of the helping donor. When we allow different role-switching rates for different strategies, cooperators can win even in the case when the assortment mechanism is inefficient (i.e., the probability of receiving help for noncooperators is slightly greater than for cooperators), which is impossible in classical Hamilton's rule.
HCC Is All You Need: Alignment-The Sensible Kind Anyway-Is Just Human-Centered Computing
Authors: Eric Gilbert
Date: 2024-04-30 00:00:00
Tasks: All

This article argues that AI Alignment is a type of Human-Centered Computing.
LayerPlexRank: Exploring Node Centrality and Layer Influence through Algebraic Connectivity in Multiplex Networks
Authors: Hao Ren, Jiaojiao Jiang
Date: 2024-05-09 00:00:00

As the calculation of centrality in complex networks becomes increasingly vital across technological, biological, and social systems, precise and scalable ranking methods are essential for understanding these networks. This paper introduces LayerPlexRank, an algorithm that simultaneously assesses node centrality and layer influence in multiplex networks using algebraic connectivity metrics. This method enhances the robustness of the ranking algorithm by effectively assessing structural changes across layers using random walk, considering the overall connectivity of the graph. We substantiate the utility of LayerPlexRank with theoretical analyses and empirical validations on varied real-world datasets, contrasting it with established centrality measures.
Characterization of the Autonomic Nervous System Activity in Females Classified According to Mood Scores During the Follicular Phase
Authors: Makiko Aok, Mai Nishimura, Masato Suzuki, Eiriko Terasawa, Hisayo Okayama
Date: 2024-05-08 00:00:00

Many sexually mature females suffer from premenstrual syndrome (PMS), but effective coping methods for PMS are limited due to the complexity of symptoms and unclear pathogenesis. Awareness has shown promise in alleviating PMS symptoms but faces challenges in long-term recording and consistency. Our research goal is to establish a convenient and simple method to make individual female aware of their own psychological, and autonomic conditions. In previous research, we demonstrated that participants could be classified into non-PMS and PMS groups based on mood scores obtained during the follicular phase. However, the properties of neurophysiological activity in the participants classified by mood scores have not been elucidated. This study aimed to classify participants based on their scores on a mood questionnaire during the follicular phase and to evaluate their autonomic nervous system (ANS) activity using a simple device that measures pulse waves from the earlobe. Participants were grouped into Cluster I (high positive mood) and Cluster II (low mood). Cluster II participants showed reduced parasympathetic nervous system activity from the follicular to the menstrual phase, indicating potential PMS symptoms. The study demonstrates the feasibility of using mood scores to classify individuals into PMS and non-PMS groups and monitor ANS changes across menstrual phases. Despite limitations such as sample size and device variability, the findings highlight a promising avenue for convenient PMS self-monitoring.
Towards Invariant Time Series Forecasting in Smart Cities
Authors: Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield
Date: 2024-05-08 00:00:00
Tasks: Management, Time Series, Time Series Forecasting

In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban planning, sustainability, and economic growth. The advancement of deep neural networks has significantly improved forecasting performance. However, a notable challenge lies in the ability of these models to generalize well to out-of-distribution (OOD) time series data. The inherent spatial heterogeneity and domain shifts across urban environments create hurdles that prevent models from adapting and performing effectively in new urban environments. To tackle this problem, we propose a solution to derive invariant representations for more robust predictions under different urban environments instead of relying on spurious correlation across urban environments for better generalizability. Through extensive experiments on both synthetic and real-world data, we demonstrate that our proposed method outperforms traditional time series forecasting models when tackling domain shifts in changing urban environments. The effectiveness and robustness of our method can be extended to diverse fields including climate modeling, urban planning, and smart city resource management.
Similarity Guided Multimodal Fusion Transformer for Semantic Location Prediction in Social Media
Authors: Zhizhen Zhang, Ning Wang, Haojie Li, Zhihui Wang
Date: 2024-05-09 00:00:00
Tasks: Language Modelling

Semantic location prediction aims to derive meaningful location insights from multimodal social media posts, offering a more contextual understanding of daily activities than using GPS coordinates. This task faces significant challenges due to the noise and modality heterogeneity in "text-image" posts. Existing methods are generally constrained by inadequate feature representations and modal interaction, struggling to effectively reduce noise and modality heterogeneity. To address these challenges, we propose a Similarity-Guided Multimodal Fusion Transformer (SG-MFT) for predicting the semantic locations of users from their multimodal posts. First, we incorporate high-quality text and image representations by utilizing a pre-trained large vision-language model. Then, we devise a Similarity-Guided Interaction Module (SIM) to alleviate modality heterogeneity and noise interference by incorporating both coarse-grained and fine-grained similarity guidance for improving modality interactions. Specifically, we propose a novel similarity-aware feature interpolation attention mechanism at the coarse-grained level, leveraging modality-wise similarity to mitigate heterogeneity and reduce noise within each modality. At the fine-grained level, we utilize a similarity-aware feed-forward block and element-wise similarity to further address the issue of modality heterogeneity. Finally, building upon pre-processed features with minimal noise and modal interference, we devise a Similarity-aware Fusion Module (SFM) to fuse two modalities with a cross-attention mechanism. Comprehensive experimental results clearly demonstrate the superior performance of our proposed method.
ClothPPO: A Proximal Policy Optimization Enhancing Framework for Robotic Cloth Manipulation with Observation-Aligned Action Spaces
Authors: Libing Yang, Yang Li, Long Chen
Date: 2024-05-05 00:00:00
Tasks: Language Modelling, Large Language Model

Vision-based robotic cloth unfolding has made great progress recently. However, prior works predominantly rely on value learning and have not fully explored policy-based techniques. Recently, the success of reinforcement learning on the large language model has shown that the policy gradient algorithm can enhance policy with huge action space. In this paper, we introduce ClothPPO, a framework that employs a policy gradient algorithm based on actor-critic architecture to enhance a pre-trained model with huge 10^6 action spaces aligned with observation in the task of unfolding clothes. To this end, we redefine the cloth manipulation problem as a partially observable Markov decision process. A supervised pre-training stage is employed to train a baseline model of our policy. In the second stage, the Proximal Policy Optimization (PPO) is utilized to guide the supervised model within the observation-aligned action space. By optimizing and updating the strategy, our proposed method increases the garment's surface area for cloth unfolding under the soft-body manipulation task. Experimental results show that our proposed framework can further improve the unfolding performance of other state-of-the-art methods.
Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation
Authors: Mo Guan, Yan Wang, Guangkun Ma, Jiarui Liu, Mingzu Sun
Date: 2024-05-09 00:00:00
Tasks: Sign Language Recognition, Sign Language Translation, Translation

Sign language serves as a non-vocal means of communication, transmitting information and significance through gestures, facial expressions, and bodily movements. The majority of current approaches for sign language recognition (SLR) and translation rely on RGB video inputs, which are vulnerable to fluctuations in the background. Employing a keypoint-based strategy not only mitigates the effects of background alterations but also substantially diminishes the computational demands of the model. Nevertheless, contemporary keypoint-based methodologies fail to fully harness the implicit knowledge embedded in keypoint sequences. To tackle this challenge, our inspiration is derived from the human cognition mechanism, which discerns sign language by analyzing the interplay between gesture configurations and supplementary elements. We propose a multi-stream keypoint attention network to depict a sequence of keypoints produced by a readily available keypoint estimator. In order to facilitate interaction across multiple streams, we investigate diverse methodologies such as keypoint fusion strategies, head fusion, and self-distillation. The resulting framework is denoted as MSKA-SLR, which is expanded into a sign language translation (SLT) model through the straightforward addition of an extra translation network. We carry out comprehensive experiments on well-known benchmarks like Phoenix-2014, Phoenix-2014T, and CSL-Daily to showcase the efficacy of our methodology. Notably, we have attained a novel state-of-the-art performance in the sign language translation task of Phoenix-2014T. The code and models can be accessed at: https://github.com/sutwangyan/MSKA.
Natural Language Processing RELIES on Linguistics
Authors: Juri Opitz, Shira Wein, Nathan Schneider
Date: 2024-05-09 00:00:00

Large Language Models (LLMs) have become capable of generating highly fluent text in certain languages, without modules specially designed to capture grammar or semantic coherence. What does this mean for the future of linguistic expertise in NLP? We highlight several aspects in which NLP (still) relies on linguistics, or where linguistic thinking can illuminate new directions. We argue our case around the acronym RELIES that encapsulates six major facets where linguistics contributes to NLP: Resources, Evaluation, Low-resource settings, Interpretability, Explanation, and the Study of language. This list is not exhaustive, nor is linguistics the main point of reference for every effort under these themes; but at a macro level, these facets highlight the enduring importance of studying machine systems vis-\`a-vis systems of human language.
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask
Authors: Zineb Senane, Lele Cao, Valentin Leonhard Buchner, Yusuke Tashiro, Lei You, Pawel Herman, Mats Nordahl, Ruibo Tu, Vilhelm von Ehrenheim
Date: 2024-05-09 00:00:00
Tasks: Anomaly Detection, Imputation, Representation Learning, Self-Supervised Learning, Time Series

Time Series Representation Learning (TSRL) focuses on generating informative representations for various Time Series (TS) modeling tasks. Traditional Self-Supervised Learning (SSL) methods in TSRL fall into four main categories: reconstructive, adversarial, contrastive, and predictive, each with a common challenge of sensitivity to noise and intricate data nuances. Recently, diffusion-based methods have shown advanced generative capabilities. However, they primarily target specific application scenarios like imputation and forecasting, leaving a gap in leveraging diffusion models for generic TSRL. Our work, Time Series Diffusion Embedding (TSDE), bridges this gap as the first diffusion-based SSL TSRL approach. TSDE segments TS data into observed and masked parts using an Imputation-Interpolation-Forecasting (IIF) mask. It applies a trainable embedding function, featuring dual-orthogonal Transformer encoders with a crossover mechanism, to the observed part. We train a reverse diffusion process conditioned on the embeddings, designed to predict noise added to the masked part. Extensive experiments demonstrate TSDE's superiority in imputation, interpolation, forecasting, anomaly detection, classification, and clustering. We also conduct an ablation study, present embedding visualizations, and compare inference speed, further substantiating TSDE's efficiency and validity in learning representations of TS data.
Federated Combinatorial Multi-Agent Multi-Armed Bandits
Authors: Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal
Date: 2024-05-09 00:00:00
Tasks: Combinatorial Optimization, Data Summarization, Federated Learning, Multi-Armed Bandits

This paper introduces a federated learning framework tailored for online combinatorial optimization with bandit feedback. In this setting, agents select subsets of arms, observe noisy rewards for these subsets without accessing individual arm information, and can cooperate and share information at specific intervals. Our framework transforms any offline resilient single-agent $(\alpha-\epsilon)$-approximation algorithm, having a complexity of $\tilde{\mathcal{O}}(\frac{\psi}{\epsilon^\beta})$, where the logarithm is omitted, for some function $\psi$ and constant $\beta$, into an online multi-agent algorithm with $m$ communicating agents and an $\alpha$-regret of no more than $\tilde{\mathcal{O}}(m^{-\frac{1}{3+\beta}} \psi^\frac{1}{3+\beta} T^\frac{2+\beta}{3+\beta})$. This approach not only eliminates the $\epsilon$ approximation error but also ensures sublinear growth with respect to the time horizon $T$ and demonstrates a linear speedup with an increasing number of communicating agents. Additionally, the algorithm is notably communication-efficient, requiring only a sublinear number of communication rounds, quantified as $\tilde{\mathcal{O}}\left(\psi T^\frac{\beta}{\beta+1}\right)$. Furthermore, the framework has been successfully applied to online stochastic submodular maximization using various offline algorithms, yielding the first results for both single-agent and multi-agent settings and recovering specialized single-agent theoretical guarantees. We empirically validate our approach to a stochastic data summarization problem, illustrating the effectiveness of the proposed framework, even in single-agent scenarios.
Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness
Authors: Siyuan Li, Xi Lin, Yaju Liu, Xiang Chen, Jianhua Li
Date: 2024-05-09 00:00:00
Tasks: Fairness, Sentiment Analysis

AI-generated content (AIGC) models, represented by large language models (LLM), have revolutionized content creation. High-speed next-generation communication technology is an ideal platform for providing powerful AIGC network services. At the same time, advanced AIGC techniques can also make future network services more intelligent, especially various online content generation services. However, the significant untrustworthiness concerns of current AIGC models, such as robustness, security, and fairness, greatly affect the credibility of intelligent network services, especially in ensuring secure AIGC services. This paper proposes TrustGAIN, a trustworthy AIGC framework that incorporates robust, secure, and fair network services. We first discuss the robustness to adversarial attacks faced by AIGC models in network systems and the corresponding protection issues. Subsequently, we emphasize the importance of avoiding unsafe and illegal services and ensuring the fairness of the AIGC network services. Then as a case study, we propose a novel sentiment analysis-based detection method to guide the robust detection of unsafe content in network services. We conduct our experiments on fake news, malicious code, and unsafe review datasets to represent LLM application scenarios. Our results indicate that TrustGAIN is an exploration of future networks that can support trustworthy AIGC network services.