pwc-archive/papers-with-abstracts huggingface dataset:
A safety realignment framework via subspace-oriented model fusion for large language models
Authors: Xin Yi, Shunfan Zheng, LinLin Wang, Xiaoling Wang, Liang He
Date: 2024-05-15 00:00:00
Tasks: Instruction Following, Math

The current safeguard mechanisms for large language models (LLMs) are indeed susceptible to jailbreak attacks, making them inherently fragile. Even the process of fine-tuning on apparently benign data for downstream tasks can jeopardize safety. One potential solution is to conduct safety fine-tuning subsequent to downstream fine-tuning. However, there's a risk of catastrophic forgetting during safety fine-tuning, where LLMs may regain safety measures but lose the task-specific knowledge acquired during downstream fine-tuning. In this paper, we introduce a safety realignment framework through subspace-oriented model fusion (SOMF), aiming to combine the safeguard capabilities of initially aligned model and the current fine-tuned model into a realigned model. Our approach begins by disentangling all task vectors from the weights of each fine-tuned model. We then identify safety-related regions within these vectors by subspace masking techniques. Finally, we explore the fusion of the initial safely aligned LLM with all task vectors based on the identified safety subspace. We validate that our safety realignment framework satisfies the safety requirements of a single fine-tuned model as well as multiple models during their fusion. Our findings confirm that SOMF preserves safety without notably compromising performance on downstream tasks, including instruction following in Chinese, English, and Hindi, as well as problem-solving capabilities in Code and Math.
A Japanese-Chinese Parallel Corpus Using Crowdsourcing for Web Mining
Authors: Masaaki Nagata, Makoto Morishita, Katsuki Chousa, Norihito Yasuda
Date: 2024-05-15 00:00:00
Tasks: Sentence, Translation, Word Translation

Using crowdsourcing, we collected more than 10,000 URL pairs (parallel top page pairs) of bilingual websites that contain parallel documents and created a Japanese-Chinese parallel corpus of 4.6M sentence pairs from these websites. We used a Japanese-Chinese bilingual dictionary of 160K word pairs for document and sentence alignment. We then used high-quality 1.2M Japanese-Chinese sentence pairs to train a parallel corpus filter based on statistical language models and word translation probabilities. We compared the translation accuracy of the model trained on these 4.6M sentence pairs with that of the model trained on Japanese-Chinese sentence pairs from CCMatrix (12.4M), a parallel corpus from global web mining. Although our corpus is only one-third the size of CCMatrix, we found that the accuracy of the two models was comparable and confirmed that it is feasible to use crowdsourcing for web mining of parallel data.
LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages
Authors: Jared Coleman, Bhaskar Krishnamachari, Khalil Iskarous, Ruben Rosales
Date: 2024-05-14 00:00:00
Tasks: Machine Translation, Sentence, Translation

We propose a new paradigm for machine translation that is particularly useful for no-resource languages (those without any publicly available bilingual or monolingual corpora): LLM-RBMT (LLM-Assisted Rule Based Machine Translation). Using the LLM-RBMT paradigm, we design the first language education/revitalization-oriented machine translator for Owens Valley Paiute (OVP), a critically endangered Indigenous American language for which there is virtually no publicly available data. We present a detailed evaluation of the translator's components: a rule-based sentence builder, an OVP to English translator, and an English to OVP translator. We also discuss the potential of the paradigm, its limitations, and the many avenues for future research that it opens up.
Phylotrack: C++ and Python libraries for in silico phylogenetic tracking
Authors: Emily Dolson, Santiago Rodriguez-Papa, Matthew Andres Moreno
Date: 2024-05-15 00:00:00
Tasks: Artificial Life

In silico evolution instantiates the processes of heredity, variation, and differential reproductive success (the three "ingredients" for evolution by natural selection) within digital populations of computational agents. Consequently, these populations undergo evolution, and can be used as virtual model systems for studying evolutionary dynamics. This experimental paradigm -- used across biological modeling, artificial life, and evolutionary computation -- complements research done using in vitro and in vivo systems by enabling experiments that would be impossible in the lab or field. One key benefit is complete, exact observability. For example, it is possible to perfectly record all parent-child relationships across simulation history, yielding complete phylogenies (ancestry trees). This information reveals when traits were gained or lost, and also facilitates inference of underlying evolutionary dynamics. The Phylotrack project provides libraries for tracking and analyzing phylogenies in in silico evolution. The project is composed of 1) Phylotracklib: a header-only C++ library, developed under the umbrella of the Empirical project, and 2) Phylotrackpy: a Python wrapper around Phylotracklib, created with Pybind11. Both components supply a public-facing API to attach phylogenetic tracking to digital evolution systems, as well as a stand-alone interface for measuring a variety of popular phylogenetic topology metrics. Underlying design and C++ implementation prioritizes efficiency, allowing for fast generational turnover for agent populations numbering in the tens of thousands. Several explicit features (e.g., phylogeny pruning and abstraction, etc.) are provided for reducing the memory footprint of phylogenetic information.
Analysis of the Geometric Structure of Neural Networks and Neural ODEs via Morse Functions
Authors: Christian Kuehn, Sara-Viola Kuntz
Date: 2024-05-15 00:00:00

Besides classical feed-forward neural networks, also neural ordinary differential equations (neural ODEs) have gained particular interest in recent years. Neural ODEs can be interpreted as an infinite depth limit of feed-forward or residual neural networks. We study the input-output dynamics of finite and infinite depth neural networks with scalar output. In the finite depth case, the input is a state associated with a finite number of nodes, which maps under multiple non-linear transformations to the state of one output node. In analogy, a neural ODE maps an affine linear transformation of the input to an affine linear transformation of its time-$T$ map. We show that depending on the specific structure of the network, the input-output map has different properties regarding the existence and regularity of critical points, which can be characterized via Morse functions. We prove that critical points cannot exist if the dimension of the hidden layer is monotonically decreasing or the dimension of the phase space is smaller or equal to the input dimension. In the case that critical points exist, we classify their regularity depending on the specific architecture of the network. We show that except for a Lebesgue measure zero set in the weight space, each critical point is non-degenerate, if for finite depth neural networks the underlying graph has no bottleneck, and if for neural ODEs, the affine linear transformations used have full rank. For each type of architecture, the proven properties are comparable in the finite and the infinite depth case. The established theorems allow us to formulate results on universal embedding, i.e., on the exact representation of maps by neural networks and neural ODEs. Our dynamical systems viewpoint on the geometric structure of the input-output map provides a fundamental understanding of why certain architectures perform better than others.
Influence Maximization in Hypergraphs Using A Genetic Algorithm with New Initialization and Evaluation Methods
Authors: Xilong Qu, Wenbin Pei, Yingchao Yang, Xirong Xu, Renquan Zhang, Qiang Zhang
Date: 2024-05-15 00:00:00
Tasks: Marketing

Influence maximization (IM) is a crucial optimization task related to analyzing complex networks in the real world, such as social networks, disease propagation networks, and marketing networks. Publications to date about the IM problem focus mainly on graphs, which fail to capture high-order interaction relationships from the real world. Therefore, the use of hypergraphs for addressing the IM problem has been receiving increasing attention. However, identifying the most influential nodes in hypergraphs remains challenging, mainly because nodes and hyperedges are often strongly coupled and correlated. In this paper, to effectively identify the most influential nodes, we first propose a novel hypergraph-independent cascade model that integrates the influences of both node and hyperedge failures. Afterward, we introduce genetic algorithms (GA) to identify the most influential nodes that leverage hypergraph collective influences. In the GA-based method, the hypergraph collective influence is effectively used to initialize the population, thereby enhancing the quality of initial candidate solutions. The designed fitness function considers the joint influences of both nodes and hyperedges. This ensures the optimal set of nodes with the best influence on both nodes and hyperedges to be evaluated accurately. Moreover, a new mutation operator is designed by introducing factors, i.e., the collective influence and overlapping effects of nodes in hypergraphs, to breed high-quality offspring. In the experiments, several simulations on both synthetic and real hypergraphs have been conducted, and the results demonstrate that the proposed method outperforms the compared methods.
The DNA of Calabi-Yau Hypersurfaces
Authors: Nate MacFadden, Andreas Schachner, Elijah Sheridan
Date: 2024-05-14 00:00:00
Tasks: Bayesian Optimization

We implement Genetic Algorithms for triangulations of four-dimensional reflexive polytopes which induce Calabi-Yau threefold hypersurfaces via Batryev's construction. We demonstrate that such algorithms efficiently optimize physical observables such as axion decay constants or axion-photon couplings in string theory compactifications. For our implementation, we choose a parameterization of triangulations that yields homotopy inequivalent Calabi-Yau threefolds by extending fine, regular triangulations of two-faces, thereby eliminating exponentially large redundancy factors in the map from polytope triangulations to Calabi-Yau hypersurfaces. In particular, we discuss how this encoding renders the entire Kreuzer-Skarke list amenable to a variety of optimization strategies, including but not limited to Genetic Algorithms. To achieve optimal performance, we tune the hyperparameters of our Genetic Algorithm using Bayesian optimization. We find that our implementation vastly outperforms other sampling and optimization strategies like Markov Chain Monte Carlo or Simulated Annealing. Finally, we showcase that our Genetic Algorithm efficiently performs optimization even for the maximal polytope with Hodge numbers $h^{1,1} = 491$, where we use it to maximize axion-photon couplings.
Multi-Source Conformal Inference Under Distribution Shift
Authors: Yi Liu, Alexander W. Levis, Sharon-Lise Normand, Larry Han
Date: 2024-05-15 00:00:00
Tasks: Decision Making, Length-of-Stay prediction, Prediction Intervals, Uncertainty Quantification, valid

Recent years have experienced increasing utilization of complex machine learning models across multiple sources of data to inform more generalizable decision-making. However, distribution shifts across data sources and privacy concerns related to sharing individual-level data, coupled with a lack of uncertainty quantification from machine learning predictions, make it challenging to achieve valid inferences in multi-source environments. In this paper, we consider the problem of obtaining distribution-free prediction intervals for a target population, leveraging multiple potentially biased data sources. We derive the efficient influence functions for the quantiles of unobserved outcomes in the target and source populations, and show that one can incorporate machine learning prediction algorithms in the estimation of nuisance functions while still achieving parametric rates of convergence to nominal coverage probabilities. Moreover, when conditional outcome invariance is violated, we propose a data-adaptive strategy to upweight informative data sources for efficiency gain and downweight non-informative data sources for bias reduction. We highlight the robustness and efficiency of our proposals for a variety of conformal scores and data-generating mechanisms via extensive synthetic experiments. Hospital length of stay prediction intervals for pediatric patients undergoing a high-risk cardiac surgical procedure between 2016-2022 in the U.S. illustrate the utility of our methodology.
Dynamical systems and complex networks: A Koopman operator perspective
Authors: Stefan Klus, Nataša Djurdjevac Conrad
Date: 2024-05-14 00:00:00

The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept$\unicode{x2013}$representing highly nonlinear dynamical systems by infinite-dimensional linear operators$\unicode{x2013}$has been known for a long time, the availability of large data sets and efficient machine learning algorithms for estimating the Koopman operator from data make this framework extremely powerful and popular. Koopman operator theory allows us to gain insights into the characteristic global properties of a system without requiring detailed mathematical models. We will show how these methods can also be used to analyze complex networks and highlight relationships between Koopman operators and graph Laplacians.
Thermodynamic limit in learning period three
Authors: Yuichiro Terasaki, Kohei Nakajima
Date: 2024-05-12 00:00:00
Tasks: Diversity

A continuous one-dimensional map with period three includes all periods. This raises the following question: Can we obtain any types of periodic orbits solely by learning three data points? In this paper, we report the answer to be yes. Considering a random neural network in its thermodynamic limit, we first show that almost all learned periods are unstable, and each network has its own characteristic attractors (which can even be untrained ones). The latently acquired dynamics, which are unstable within the trained network, serve as a foundation for the diversity of characteristic attractors and may even lead to the emergence of attractors of all periods after learning. When the neural network interpolation is quadratic, a universal post-learning bifurcation scenario appears, which is consistent with a topological conjugacy between the trained network and the classical logistic map. In addition to universality, we explore specific properties of certain networks, including the singular behavior of the scale of weight at the infinite limit, the finite-size effects, and the symmetry in learning period three.
Liouville Flow Importance Sampler
Authors: Yifeng Tian, Nishant Panda, Yen Ting Lin
Date: 2024-05-03 00:00:00

We present the Liouville Flow Importance Sampler (LFIS), an innovative flow-based model for generating samples from unnormalized density functions. LFIS learns a time-dependent velocity field that deterministically transports samples from a simple initial distribution to a complex target distribution, guided by a prescribed path of annealed distributions. The training of LFIS utilizes a unique method that enforces the structure of a derived partial differential equation to neural networks modeling velocity fields. By considering the neural velocity field as an importance sampler, sample weights can be computed through accumulating errors along the sample trajectories driven by neural velocity fields, ensuring unbiased and consistent estimation of statistical quantities. We demonstrate the effectiveness of LFIS through its application to a range of benchmark problems, on many of which LFIS achieved state-of-the-art performance.
Diffusion-based Contrastive Learning for Sequential Recommendation
Authors: Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, Chen Ma
Date: 2024-05-15 00:00:00
Tasks: Contrastive Learning, Sequential Recommendation

Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals. Most existing approaches generate augmented views of the same user sequence through random augmentation and subsequently maximize their agreement in the representation space. However, these methods often neglect the rationality of the augmented samples. Due to significant uncertainty, random augmentation can disrupt the semantic information and interest evolution patterns inherent in the original user sequences. Moreover, pulling semantically inconsistent sequences closer in the representation space can render the user sequence embeddings insensitive to variations in user preferences, which contradicts the primary objective of sequential recommendation. To address these limitations, we propose the Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec. The core idea is to leverage context information to generate more reasonable augmented views. Specifically, CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence. These generated items are aligned with their respective context information and can effectively replace the corresponding original items, thereby generating a positive view of the original sequence. By considering two different augmentations of the same user sequence, we can construct a pair of positive samples for contrastive learning. To ensure representation cohesion, we train the entire framework in an end-to-end manner, with shared item embeddings between the diffusion model and the recommendation model. Extensive experiments on five benchmark datasets demonstrate the advantages of our proposed method over existing baselines.
Words Blending Boxes. Obfuscating Queries in Information Retrieval using Differential Privacy
Authors: Francesco Luigi De Faveri, Guglielmo Faggioli, Nicola Ferro
Date: 2024-05-15 00:00:00
Tasks: Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity

Ensuring the effectiveness of search queries while protecting user privacy remains an open issue. When an Information Retrieval System (IRS) does not protect the privacy of its users, sensitive information may be disclosed through the queries sent to the system. Recent improvements, especially in NLP, have shown the potential of using Differential Privacy to obfuscate texts while maintaining satisfactory effectiveness. However, such approaches may protect the user's privacy only from a theoretical perspective while, in practice, the real user's information need can still be inferred if perturbed terms are too semantically similar to the original ones. We overcome such limitations by proposing Word Blending Boxes, a novel differentially private mechanism for query obfuscation, which protects the words in the user queries by employing safe boxes. To measure the overall effectiveness of the proposed WBB mechanism, we measure the privacy obtained by the obfuscation process, i.e., the lexical and semantic similarity between original and obfuscated queries. Moreover, we assess the effectiveness of the privatized queries in retrieving relevant documents from the IRS. Our findings indicate that WBB can be integrated effectively into existing IRSs, offering a key to the challenge of protecting user privacy from both a theoretical and a practical point of view.
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
Authors: Yi Zhang, Lei Sang, Yiwen Zhang
Date: 2024-05-15 00:00:00
Tasks: Collaborative Filtering, Graph Generation, Graph Reconstruction, Recommendation Systems

Intent modeling has attracted widespread attention in recommender systems. As the core motivation behind user selection of items, intent is crucial for elucidating recommendation results. The current mainstream modeling method is to abstract the intent into unknowable but learnable shared or non-shared parameters. Despite considerable progress, we argue that it still confronts the following challenges: firstly, these methods only capture the coarse-grained aspects of intent, ignoring the fact that user-item interactions will be affected by collective and individual factors (e.g., a user may choose a movie because of its high box office or because of his own unique preferences); secondly, modeling believable intent is severely hampered by implicit feedback, which is incredibly sparse and devoid of true semantics. To address these challenges, we propose a novel recommendation framework designated as Bilateral Intent-guided Graph Collaborative Filtering (BIGCF). Specifically, we take a closer look at user-item interactions from a causal perspective and put forth the concepts of individual intent-which signifies private preferences-and collective intent-which denotes overall awareness. To counter the sparsity of implicit feedback, the feature distributions of users and items are encoded via a Gaussian-based graph generation strategy, and we implement the recommendation process through bilateral intent-guided graph reconstruction re-sampling. Finally, we propose graph contrastive regularization for both interaction and intent spaces to uniformize users, items, intents, and interactions in a self-supervised and non-augmented paradigm. Experimental results on three real-world datasets demonstrate the effectiveness of BIGCF compared with existing solutions.
Evaluating Supply Chain Resilience During Pandemic Using Agent-based Simulation
Authors: Teddy Lazebnik
Date: 2024-05-13 00:00:00

Recent pandemics have highlighted vulnerabilities in our global economic systems, especially supply chains. Possible future pandemic raises a dilemma for businesses owners between short-term profitability and long-term supply chain resilience planning. In this study, we propose a novel agent-based simulation model integrating extended Susceptible-Infected-Recovered (SIR) epidemiological model and supply and demand economic model to evaluate supply chain resilience strategies during pandemics. Using this model, we explore a range of supply chain resilience strategies under pandemic scenarios using in silico experiments. We find that a balanced approach to supply chain resilience performs better in both pandemic and non-pandemic times compared to extreme strategies, highlighting the importance of preparedness in the form of a better supply chain resilience. However, our analysis shows that the exact supply chain resilience strategy is hard to obtain for each firm and is relatively sensitive to the exact profile of the pandemic and economic state at the beginning of the pandemic. As such, we used a machine learning model that uses the agent-based simulation to estimate a near-optimal supply chain resilience strategy for a firm. The proposed model offers insights for policymakers and businesses to enhance supply chain resilience in the face of future pandemics, contributing to understanding the trade-offs between short-term gains and long-term sustainability in supply chain management before and during pandemics.