bluuebunny/crossref_metadata_2025 huggingface dataset:
Language Task Difficulty Prediction Through LLM-Annotated Meta-Features
Authors: Moros-Daval Yael , Martínez-Plumed Fernando , Hernández-Orallo José
Published: October 2024

Assessing the capabilities of large language models (LLMs) is increasingly challenging due to their generality and uneven task performance. Often, we do not know how much of the success or failure on a particular task is due to the ‘loading’ of the language elements in the task, such as narrative understanding, or some other intrinsic (non-linguistic) components, such as domain-specific common sense or reasoning capabilities. Understanding what tasks are most loaded on language and determine the predictability of LLMs on these tasks is crucial for improving benchmarks, designing better LLMs, and ensuring their safe deployment. We present an innovative methodology that uses LLMs to annotate linguistic meta-features, allowing us to predict task difficulty and understand linguistic loadings more accurately than traditional readability scores. Using GPT-4 for automated annotation, we show strong predictability for a variety of tasks and language models (e.g., MMLU with R2 from 0.68 to 0.83), but observe limited predictability for other tasks (e.g., LSAT with R2 of -0.07).
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
Authors: Fourati Fares , Kharrat Salma , Aggarwal Vaneet , Alouini Mohamed-Slim , Canini Marco
Published: October 2024

Federated learning, an emerging machine learning paradigm, enables clients to collaboratively train a model without exchanging local data. Clients participating in the training process significantly impact the convergence rate, learning efficiency, and model generalization. We propose a novel approach, client filtering, to improve model generalization and optimize client participation and training. The proposed method periodically filters available clients to identify a subset that maximizes a combinatorial objective function with an efficient greedy filtering algorithm. Thus, the clients are assessed as a combination rather than individually. We theoretically analyze the convergence of federated learning with client filtering in heterogeneous settings and evaluate its performance across diverse vision and language tasks, including realistic scenarios with time-varying client availability. Our empirical results demonstrate several benefits of our approach, including improved learning efficiency, faster convergence, and up to 10% higher test accuracy than training without client filtering.
Benchmarking Adversarial Robustness in Speech Emotion Recognition: Insights into Low-Resource Romanian and German Languages
Authors: Echim Sebastian-Vasile , Smădu Răzvan-Alexandru , Cercel Dumitru-Clementin
Published: October 2024

Therapy, interviews, and emergency services assisted by artificial intelligence (AI) are applications where speech emotion recognition (SER) plays an essential role, for which performance and robustness are subject to improvement. Deep learning approaches have proven effective in SER; nevertheless, they can underperform when exposed to adversarial attacks. In this paper, we explore and enhance architectures, such as convolutional neural networks with long short-term memory (CNN-LSTM), AlexNet, VGG16, Convolutional Vision Transformer (CvT), Vision Transformer (ViT), and LeViT, by finding the suitable setup for SER models regarding speech processing, network hyperparameters, spectrogram augmentations, and adversarial examples. We apply our methodology to Romanian and German SER datasets and achieve state-of-the-art results, with 89.81% validation weighted accuracy and 98.09% average weighted accuracy on the trained models. Our highly robust models reach complete adversarial defense and up to 5.56% weighted accuracy improvement when attacked. We also show how adversarial attacks influence model behavior in SER through explainable AI techniques.
Coopetition in Heterogeneous Cross-Silo Federated Learning
Authors: Huang Chao , Dachille Justin , Liu Xin
Published: October 2024

In cross-silo federated learning (FL), companies collaboratively train a shared global model without sharing heterogeneous data. Prior related work focused on algorithm development to tackle data heterogeneity. However, the dual problem of coopetition, i.e., FL collaboration and market competition, remains under-explored. This paper studies the FL coopetition using a dynamic two-period game model. In period 1, an incumbent company trains a local model and provides model-based services at a chosen price to users. In period 2, an entrant company enters, and both companies decide whether to engage in FL collaboration and then compete in selling model-based services at different prices to users. Analyzing the two-period game is challenging due to data heterogeneity, and that the incumbent’s period one pricing has a temporal impact on coopetition in period 2, resulting in a non-concave problem. To address this issue, we decompose the problem into several concave sub-problems and develop an algorithm that achieves a global optimum. Numerical results on three public datasets show two interesting insights. First, FL training brings model performance gain as well as competition loss, and collaboration occurs only when the performance gain outweighs the loss. Second, data heterogeneity can incentivize the incumbent to limit market penetration in period 1 and promote price competition in period 2.
Adjacent Leader Decentralized Stochastic Gradient Descent
Authors: He Haoze , Wang Jing , Choromanska Anna
Published: October 2024

This work focuses on the decentralized deep learning optimization framework. We propose Adjacent Leader Decentralized Gradient Descent (AL-DSGD), for improving final model performance, accelerating convergence, and reducing the communication overhead of decentralized deep learning optimizers. AL-DSGD relies on two main ideas. Firstly, to increase the influence of the strongest learners on the learning system it assigns weights to different neighbor workers according to both their performance and the degree when averaging among them, and it applies a corrective force on the workers dictated by both the currently best-performing neighbor and the neighbor with the maximal degree. Secondly, to alleviate the problem of the deterioration of the convergence speed and performance of the nodes with lower degrees, AL-DSGD relies on dynamic communication graphs, which effectively allows the workers to communicate with more nodes while keeping the degrees of the nodes low. Experiments demonstrate that AL-DSGD accelerates the convergence of the decentralized state-of-the-art techniques and improves their test performance especially in the communication constrained environments. We also theoretically prove the convergence of the proposed scheme. Finally, we release to the community a highly general and concise PyTorch-based library for distributed training of deep learning models that supports easy implementation of any distributed deep learning approach ((a)synchronous, (de)centralized).
ANSPRE: Improving Question-Answering in Large Language Models with Answer-Prefix Generation
Authors: Le Nguyen-Khang , Nguyen Dieu-Hien , Nguyen Le Minh
Published: October 2024

Large language models (LLMs) and Retrieval-Augmented-Generation (RAG) show remarkable capabilities in Open-domain question-answering (ODQA). Despite the advancements, LLMs tend to generate verbose responses, of which only a small part is the answer phrase. Although the ability to produce the confidence score for the answer is essential when deploying LLMs in high-risk domains, sequence probabilities obtained from LLMs do not correlate well with the probabilities of correctness and thus fail to represent confidence scores. This study introduces Answer-prefix Generation (Anspre) to improve generation quality, allowing the LLMs to output answer phrases and produce highly reliable confidence scores. We guide the model in predicting the answer phrase using an answer prefix and design a ranking score that integrates parametric and non-parametric knowledge. The answer phrases and their corresponding scores enable Anspre to aggregate results from different documents and samplings to boost performance and produce confidence scores highly correlated with correctness. We show that Anspre can be applied to any LLM and present an approach called Self-Anspre to combine Anspre with Self-reflective RAG, a state-of-the-art framework based on reflection tokens. Empirical evaluation on popular ODQA benchmarks shows that Anspre and Self-Anspre significantly improve state-of-the-art LLMs and RAG frameworks. An in-depth analysis shows that confidence scores produced by Anspre are highly correlated to the likelihood of correctness.
Multi-View Prompt for Fine-Grained Multimodal Named Entity Recognition and Grounding
Authors: Liu Jintao , Liu Chenglong , Wei Kaiwen
Published: October 2024

Fine-Grained Multimodal Named Entity Recognition and Grounding (FMNERG) aims to extract entity name, fine-grained entity type, and its corresponding object from paired text and image. This task demands fundamental reasoning capability for complex language and multimodal comprehension. Despite encouraging results, existing methods face two critical issues: (1) Insufficient knowledge of the entity poses challenges to fine-grained entity recognition; (2) Limited correlations between entities and objects hinder the visual grounding of entities. To tackle these issues, we propose a Multi-View Prompt (MVP) method for the FMNERG task in this paper, which collaborates with Large Language Models (LLMs) and Visual Grounding Models (VGMs) for reasoning. Concretely, MVP constructs a knowledgeable prompt in a chain-of-thought format, progressively refining possible entity types from coarse-grained to fine-grained levels. It leverages a heuristic method to select demonstration examples, which could provide guiding knowledge about entities from LLMs. To establish correlations between entities and potential objects, MVP introduces a grounded prompt that exploits information from guiding knowledge and image caption, enabling VGMs to detect related objects. Experimental results indicate that MVP achieves state-of-the-art performance on the Twitter dataset.
Offline Model-Based Reinforcement Learning with Anti-Exploration
Authors: Srinivasan Padmanaba , Knottenbelt William
Published: October 2024

Model-based reinforcement learning (MBRL) algorithms learn a dynamics model from collected data and apply it to generate synthetic trajectories to enable faster learning. This is an especially promising paradigm in offline reinforcement learning (RL) where data may be limited in quantity, in addition to being deficient in coverage and quality. Practical approaches to offline MBRL usually rely on ensembles of dynamics models to prevent exploitation of any individual model and to extract uncertainty estimates that penalize values in states far from the dataset support. Uncertainty estimates from ensembles can vary greatly in scale, making it challenging to generalize hyperparameters well across even similar tasks. In this paper, we present Morse Model-based offline RL (MoMo), which extends the anti-exploration paradigm found in offline model-free RL to the model-based space. We develop model-free and model-based variants of MoMo and show how the model-free version can be extended to detect and deal with out-of-distribution (OOD) states using explicit uncertainty estimation without the need for large ensembles. MoMo performs offline MBRL using an anti-exploration bonus to counteract value overestimation in combination with a policy constraint, as well as a truncation function to terminate synthetic rollouts that are excessively OOD. Experimentally, we find that both model-free and model-based MoMo perform well, and the latter outperforms prior model-based and model-free baselines on the majority of D4RL datasets tested.
Spatially Constrained Transformer with Efficient Global Relation Modelling for Spatio-Temporal Prediction
Authors: Sao Ashutosh , Gottschalk Simon
Published: October 2024

Accurate spatio-temporal prediction is crucial for the sustainable development of smart cities. However, current approaches often struggle to capture important spatio-temporal relationships, particularly overlooking global relations among distant city regions. Most existing techniques predominantly rely on Convolutional Neural Networks (CNNs) to capture global relations. However, CNNs exhibit neighbourhood bias, making them insufficient for capturing distant relations. To address this limitation, we propose ST-SampleNet, a novel transformer-based architecture that combines CNNs with self-attention mechanisms to capture both local and global relations effectively. Moreover, as the number of regions increases, the quadratic complexity of self-attention becomes a challenge. To tackle this issue, we introduce a lightweight region sampling strategy that prunes non-essential regions and enhances the efficiency of our approach. Furthermore, we introduce a spatially constrained position embedding that incorporates spatial neighbourhood information into the self-attention mechanism, aiding in semantic interpretation and improving the performance of ST-SampleNet. Our experimental evaluation on three real-world datasets demonstrates the effectiveness of ST-SampleNet. Additionally, our efficient variant achieves a 40% reduction in computational costs with only a marginal compromise in performance, approximately 1%.
Explaining an Agent’s Future Beliefs Through Temporally Decomposing Future Reward Estimators
Authors: Towers Mark , Du Yali , Freeman Christopher , Norman Timothy J.
Published: October 2024

Future reward estimation is a core component of reinforcement learning agents; i.e., Q-value and state-value functions, predicting an agent’s sum of future rewards. Their scalar output, however, obfuscates when or what individual future rewards an agent may expect to receive. We address this by modifying an agent’s future reward estimator to predict their next N expected rewards, referred to as Temporal Reward Decomposition (TRD). This unlocks novel explanations of agent behaviour. Through TRD we can: estimate when an agent may expect to receive a reward, the value of the reward and the agent’s confidence in receiving it; measure an input feature’s temporal importance to the agent’s action decisions; and predict the influence of different actions on future rewards. Furthermore, we show that DQN agents trained on Atari environments can be efficiently retrained to incorporate TRD with minimal impact on performance.
On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement
Authors: Khan Ahmad Saeed , Schaffernicht Erik , Stork Johannes Andreas
Published: October 2024

Estimating treatment effects from observational data is paramount in healthcare, education, and economics, but current deep disentanglement-based methods to address selection bias are insufficiently handling irrelevant variables. We demonstrate in experiments that this leads to prediction errors. We disentangle pre-treatment variables with a deep embedding method and explicitly identify and represent irrelevant variables, additionally to instrumental, confounding and adjustment latent factors. To this end, we introduce a reconstruction objective and create an embedding space for irrelevant variables using an attached autoencoder. Instead of relying on serendipitous suppression of irrelevant variables as in previous deep disentanglement approaches, we explicitly force irrelevant variables into this embedding space and employ orthogonalization to prevent irrelevant information from leaking into the latent space representations of the other factors. Our experiments with synthetic and real-world benchmark datasets show that we can better identify irrelevant variables and more precisely predict treatment effects than previous methods, while prediction quality degrades less when additional irrelevant variables are introduced.
Laner-GNN: Adapting to Long-Tail Degree Distribution with Latent Neighborhood Restorers
Authors: Xu Lingjun , Li Haowen , Song Guojie , Wang Liang , Zheng Bo
Published: October 2024

Graph Neural Networks (GNNs) learn the correlation between local graph neighborhoods and node properties, with the requirement of adequate complete and clean neighborhoods. However, the intrinsic structural characteristics of the long-tail node degree distribution in most real-world graphs violate the assumption. Numerous low-degree nodes lack accurate representation due to limited connections, while bits of high-degree nodes include redundancy and errors in observed neighborhoods, harming the performance of GNNs. In this paper, we propose Laner-GNN, which performs latent neighborhood restoration adapting to aforesaid structural characteristics and improves representation capacity. Specifically, to reduce computational complexity, latent disentangled neighborhood factors are extracted and then manipulated for restoration instead of explicitly altering the entire adjacency matrix. A Degree-Adaptive Restorer captures the dependency of restoration on node degrees, while a Label-Informed Restorer and the Structured Field Augmented Priori enhance restoration with information from the prediction target and the graph structure. Extensive experiments on real-world datasets in different domains verify that our model excels existing SOTAs in different degree bins, thus uplifting the model performance in the whole dataset.
TED: Accelerate Model Training by Internal Generalization
Authors: Xiao Jinying , Li Ping , Nie Jie
Published: October 2024

Large language models have demonstrated strong performance in recent years, but the high cost of training drives the need for efficient methods to compress dataset sizes. We propose TED pruning, a method that addresses the challenge of overfitting under high pruning ratios by quantifying the model’s ability to improve performance on pruned data while fitting retained data, known as Internal Generalization (IG). TED uses an optimization objective based on Internal Generalization Distance (IGD), measuring changes in IG before and after pruning to align with true generalization performance and achieve implicit regularization. The IGD optimization objective was verified to allow the model to achieve the smallest upper bound on generalization error. The impact of small mask fluctuations on IG is studied through masks and Taylor approximation, and fast estimation of IGD is enabled. In analyzing continuous training dynamics, the prior effect of IGD is validated, and a progressive pruning strategy is proposed. Experiments on image classification, natural language understanding, and large language model fine-tuning show TED achieves lossless performance with 60-70% of the data. Upon acceptance, our code will be made publicly available.
Discovering Bayesian Networks when Few Variables Matter
Authors: Kundu Madhumita , Parviainen Pekka , Saurabh Saket
Published: October 2024

Learning the structure of a Bayesian network from data is one of the key problems in probabilistic graphical models. Unfortunately, the problem is NP-hard and this has motivated recent works where the problem has been studied from the perspective of algorithmic paradigms meant for coping with hardness, such as parameterized complexity. We contribute to this area by designing fixed parameter tractable algorithms (FPT) to learn the Bayesian network structure when only a few variables are important. In particular, we study score-based structure learning where each graph is given with a score, based on how well it fits to the data, and the goal is to select the acyclic directed graph (DAG) that maximizes the score. Typically, one uses decomposable scores, where the score of a DAG is the sum of local scores for node-parent set pairs. We study a variant of this problem in which our objective is to find a k-heavy DAG, which is a DAG whose k most scoring nodes have a total score of at least some target value ℓ. We show that 1. if there is a k-heavy DAG with a maximum degree of d, then we can learn it in time f(k,d)nO(d) and 2. if there is a k-heavy DAG whose moralized graph has a treewidth of t and a maximum degree of t, then we can learn it in time f(k,t)nO(t). These algorithms leverage the color-coding technique from the field of Parameterized Complexity in a non-trivial manner.
Does Serendipity Enhance Recommendation Quality? Measuring Accuracy and Beyond-Accuracy Objectives of Serendipitous POI Suggestions
Authors: Ben Sassi Imen , Järv Priit , Ben Yahia Sadok
Published: October 2024

Point of Interest (POI) recommender systems (RSs) play a primary role in improving Location-based Social Networks’ user experience. This paper studies the potential usefulness of serendipity in POI recommendations. We first introduce a new POI RS, called Discovery, that attempts to improve the accuracy-serendipity trade-off. The proposed RS aims to recommend POIs that provide a pleasant surprise, allowing users to discover new venues known as serendipitous POIs. We then look closely at how serendipity affects the quality of POI suggestions by contrasting the outcomes of Discovery with those of three cutting-edge non-serendipitous POI RSs. We use two real-world datasets—Foursquare and Flickr—along with a variety of metrics to test our ideas. These include (i) accuracy, which checks the precision, recall, and f-measure of Top-N recommendations; and (ii) beyond-accuracy, which checks the categorical and geographical diversity, explainability, and coverage in terms of POIs. The reported experimental observations show that serendipity boosts POI recommendation accuracy and favors geographically proximate and explainable POIs. However, standard POI baselines outperform Discovery in terms of categorical diversity and coverage.
Revisiting the Dataset Bias Problem from a Statistical Perspective
Authors: Do Kien , Nguyen Dung , Le Hung , Le Thao , Nguyen Dang , Harikumar Haripriya , Tran Truyen , Rana Santu , Venkatesh Svetha
Published: October 2024

In this paper, we study the “dataset bias” problem from a statistical standpoint, and identify the main cause of the problem as the strong correlation between a class attribute u and a non-class attribute b in the input x, represented by p(u|b) differing significantly from p(u). Since p(u|b) appears as part of the sampling distributions in the standard maximum log-likelihood (MLL) objective, a model trained on a biased dataset via MLL inherently incorporates such correlation into its parameters, leading to poor generalization to unbiased test data. From this observation, we propose to mitigate dataset bias via either weighting the objective of each sample n by 1 / p(un|bn) or sampling that sample with a weight proportional to 1 / p(un|bn). While both methods are statistically equivalent, the former proves more stable and effective in practice. Additionally, we establish a connection between our debiasing approach and causal reasoning, reinforcing our method’s theoretical foundation. However, when the bias label is unavailable, computing p(u|b) exactly is difficult. To overcome this challenge, we propose to approximate 1 / p(u|b) using a biased classifier trained with “bias amplification” losses. Extensive experiments on various biased datasets demonstrate the superiority of our method over existing debiasing techniques in most settings, validating our theoretical analysis.
Mixup Your Own Latent: Efficient and Robust Self-Supervised Learning on Small Images
Authors: Yang Eugene , Chen Hao , Kang Seokho
Published: October 2024

Self-supervised learning has emerged as a powerful technique in computer vision, demonstrating remarkable performance in various downstream tasks by leveraging unlabeled data. Among these methods, contrastive learning has proven particularly promising by effectively learning image representations. However, its high reliance on large computational resources poses significant practical challenges. To address this issue, there is a pressing need to improve efficiency without compromising generalization performance and robustness. In this paper, we propose Mixup Your Own Latent (MYOL), a regularization method to improve the generalization performance and robustness of Bootstrap Your Own Latent (BYOL), particularly for small images under limited computational resources. MYOL achieves this using the Mixup of the representations of two input images as the target representation of the Mixup of those images. Through experiments conducted in a single GPU environment, we demonstrate that MYOL outperforms BYOL and other regularization methods across various downstream tasks on small-image datasets. The high resilience of MYOL to small batch sizes and its robustness to adversarial attacks further highlight its effectiveness in mitigating the limitations of BYOL. The source code is available at https://github.com/cneyang/MYOL-MixupYourOwnLatent.
Contextually Adaptive Algorithms for Gaussian Process Bandit Optimization Under Heavy-Tailed Noise
Authors: Park Hyeonjun , Lee Kyungjae
Published: October 2024

We consider a Gaussian process (GP) bandit optimization problem when the objective function lives in a reproducing kernel Hilbert space (RKHS), assuming that the payoffs follow a heavy-tailed distribution with a bounded (1+ϵ)-th moment for some ϵ∈(0,1]. Existing algorithms for this setting face practical challenges due to their significant computational demands and inconsistent theoretical guarantee to translation of noise distribution. To address these issues, we introduce two robust algorithms. The first algorithm utilizes a truncation estimator, achieving the same regret bound as that of the existing algorithm up to logarithmic terms with reduced time complexity. The second algorithm employs a median-of-means estimator and achieves more stable regret bound to alteration of noise distribution with lower time and space complexities compared to existing methods. Finally, we empirically validate the performance of our proposed algorithms against previous methods in both synthetic and real-world datasets.
Unlocking Efficiency: Adaptive Masking for Gene Transformer Models
Authors: Roy Soumyadeep , Sural Shamik , Ganguly Niloy
Published: October 2024

Gene transformer models such as Nucleotide Transformer, DNABert, and LOGO are trained to learn optimal gene sequence representations by using the Masked Language Modeling (MLM) training objective over the complete Human Reference Genome. However, the typical tokenization methods employ a basic sliding window of tokens, such as k-mers, that fail to utilize gene-centric semantics. This could result in the (trivial) masking of easily predictable sequences, leading to inefficient MLM training. Time-variant training strategies are known to improve pretraining efficiency in both language and vision tasks. In this work, we focus on using curriculum masking where we systematically increase the difficulty of masked token prediction task by using a Pointwise Mutual Information-based difficulty criterion, as gene sequences lack well-defined semantic units similar to words or sentences of NLP domain. Our proposed Curriculum Masking-based Gene Masking Strategy (CM-GEMS) demonstrates superior representation learning capabilities compared to baseline masking approaches when evaluated on downstream gene sequence classification tasks. We perform extensive evaluation in both few-shot (five datasets) and full dataset settings (Genomic Understanding Evaluation benchmark consisting of 27 tasks). Our findings reveal that CM-GEMS outperforms state-of-the-art models (DNABert-2, Nucleotide transformer, DNABert) trained at 120K steps, achieving similar results in just 10K and 1K steps. We also demonstrate that Curriculum-Learned LOGO (a 2-layer DNABert-like model) can achieve nearly 90% of the state-of-the-art model performance of 120K steps. We will make the models and codes publicly available at https://github.com/roysoumya/curriculum-GeneMask.
Matching Gains with Pays: Effective and Fair Learning in Multi-Agent Public Goods Dilemmas
Authors: Chen Yitian , Liu Xuan , Zhang Shigeng , Chen Xinning , Guo Song
Published: October 2024

The training of multi-agent reinforcement learning (MARL) tasks with the public goods dilemma (PGD) is difficult because the selfish actions of individual agents for high personal rewards may reduce the collective utility of the whole group. Existing solutions to this problem, e.g., reward gifting or intrinsic rewards, although inducing cooperation among agents in small groups, cannot guarantee fairness among agents’ policies and fail to achieve optimal group utility in large-scale systems. In this paper, we propose F4PGD, an effective method to train large-scale MARL tasks with PGD in a decentralized manner, which is inspired by Adam’s equity theory that the match between a person’s payoff and his contribution is the key incentive for people to contribute to the common good. In F4PGD, a mechanism is designed to match an agent’s reward with its contribution, which suppresses agents from taking a free ride and meanwhile encourages well-learned agents to contribute to public goods. Experimental results show that F4PGD effectively learns optimal policies for the whole group and guarantees fairness among agents in several typical MARL tasks with PGD.
Interpretable Image Classification Through an Argumentative Dialog Between Encoders
Authors: Thauvin Dao , Herbin Stéphane , Ouerdane Wassila , Hudelot Céline
Published: October 2024

We address the problem of designing interpretable algorithms for image classification. Modern computer vision algorithms implement classification in two phases: feature extraction - the encoding - that relies on deep neural networks (DNN), followed by a task-oriented decision - the decoding - often also using a DNN. We propose to formulate this last phase as an argumentative DialoguE Between two agents relying on visual ATtributEs and Similarity to prototypes (DEBATES). DEBATES represents the combination of information provided by two encoders in a transparent and interpretable way. It relies on a dual process that combines similarity to prototypes and visual attributes, each extracted from an encoder. DEBATES makes explicit the agreements and conflicts between the two encoders managed by the two agents, reveals the causes of unintended behaviors, and helps identify potential corrective actions to improve performance. The approach is demonstrated on two problems of fine-grained image classification.
Scale-Invariant Variations of Max Regret
Authors: Wilson Nic
Published: October 2024

Max regret is frequently used, in situations when there is uncertainty about a user preference model in a multi-objective optimisation problem, as a measure of how close an alternative is to being necessarily optimal. It is used in a termination condition for a dialogue with a user, and for recommending a compromise solution, and in different ways of generating informative queries. In this paper we consider linear user preference models based on simple weighted sums of the objectives. Unfortunately, max regret lacks a desirable scale-invariance property: changing the units (or the linear scaling) of the objectives can significantly alter the relative values of max regret between alternatives, even though the choice of units is often somewhat arbitrary. In this paper we define variations of max regret in which the regret, of an alternative given a particular user model, is divided by a function expressing a range of utility values. This leads to scale-invariance, and maintains important properties of max regret such as translation-invariance (in contrast with max relative regret). We show how linear programming and extreme points algorithms can be used for computation.
An Efficient Modular Algorithm for Connected Multi-Agent Path Finding
Authors: Desbois Victorien , Sankur Ocan , Schwarzentruber François
Published: October 2024

We present a new algorithm for solving the connected multi-agent path finding problem (connected MAPF) which consists in finding paths for a set of agents that avoid collisions but also ensure connectivity between agents during the mission. Our algorithm is based on heuristic search and combines ODrM*, a well known algorithm without connectivity constraints, and an efficient but incomplete solver for the connected MAPF from the literature. We present a formal analysis of the termination and completeness of our algorithm, and present an experimental evaluation, showing a significant improvement over the state of the art.
Analyzing Incentives and Fairness in Ordered Weighted Average for Facility Location Games
Authors: Yoshida Kento , Kimura Kei , Todo Taiki , Yokoo Makoto
Published: October 2024

Facility location games provide an abstract model of mechanism design. In such games, a mechanism takes a profile of n single-peaked preferences over an interval as an input and determines the location of a facility on the interval. In this paper, we restrict our attention to distance-based single-peaked preferences and focus on a well-known class of parameterized mechanisms called ordered weighted average methods, which is proposed by Yager [38] and contains several practical implementations such as the standard average and the Olympic average. We comprehensively analyze their performance in terms of both incentives and fairness. More specifically, we provide necessary and sufficient conditions on their parameters to achieve strategy-proofness, non-obvious manipulability, individual fair share, and proportional fairness, respectively.
Distribution of Chores with Information Asymmetry
Authors: Hosseini Hadi , Kavner Joshua , Wąs Tomasz , Xia Lirong
Published: October 2024

A well-regarded fairness notion when dividing indivisible chores is envy-freeness up to one item (EF1), which requires that pairwise envy can be eliminated by the removal of a single item. While an EF1 and Pareto optimal (PO) allocation of goods can always be found via well-known algorithms, even the existence of such solutions for chores remains open, to date. We take an epistemic approach utilizing information asymmetry by introducing dubious chores–items that inflict no cost on receiving agents but are perceived costly by others. On a technical level, dubious chores provide a more fine-grained approximation of envy-freeness than EF1. We show that finding allocations with minimal number of dubious chores is computationally hard. Nonetheless, we prove the existence of envy-free and fractional PO allocations for n agents with only 2n−2 dubious chores and strengthen it to n−1 dubious chores in four special classes of valuations. Our experimental analysis demonstrates that often only a few dubious chores are needed to achieve envy-freeness.
Revisiting Vacuous Reduct Semantics for Abstract Argumentation
Authors: Blümel Lydia , Thimm Matthias
Published: October 2024

We consider the notion of a vacuous reduct semantics for abstract argumentation frameworks, which, given two abstract argumentation semantics σ and τ, refines σ (base condition) by accepting only those σ-extensions that have no non-empty τ-extension in their reduct (vacuity condition). We give a systematic overview on vacuous reduct semantics resulting from combining different admissibility-based and conflict-free semantics and present a principle-based analysis of vacuous reduct semantics in general. We provide criteria for the inheritance of principle satisfaction by a vacuous reduct semantics from its base and vacuity condition for established as well as recently introduced principles in the context of weak argumentation semantics. We also conduct a principle-based analysis for the special case of undisputed semantics.
Abductive and Contrastive Explanations for Scoring Rules in Voting
Authors: Contet Clément , Grandi Umberto , Mengin Jérôme
Published: October 2024

We view voting rules as classifiers that assign a winner (a class) to a profile of voters’ preferences (an instance). We propose to apply techniques from formal explainability, most notably abductive and contrastive explanations, to identify minimal subsets of a preference profile that either imply the current winner or explain why a different candidate was not elected. Formal explanations turn out to have strong connections with classical problems studied in computational social choice such as bribery, possible and necessary winner identification, and preference learning. We design algorithms for computing abductive and contrastive explanations for scoring rules. For the Borda rule, we find a lower bound on the size of the smallest abductive explanations, and we conduct simulations to identify correlations between properties of preference profiles and the size of their smallest abductive explanations.
Channel Randomisation Methods for Zero-Shot Communication
Authors: Cope Dylan , Schoots Nandi
Published: October 2024

When agents learn to communicate via self-play the result is typically brittle communication strategies that only work with agents encountered during training. To alleviate this and train agents that can communicate with agents outside of their training communities we introduce two training-time interventions that apply to the messages sent between agents. These methods are: (1) message mutation, where messages are randomly changed; and (2) channel permutation, where random permutations are applied to the message space. These proposals are tested using a simple two-player sequential referential game in which the agents are given the opportunity to establish communicative conventions within a single episode. After training multiple sets of agents we analyse the performance of these agents when they are matched with a ‘stranger’ from another training run, i.e. their zero-shot communication performance. We find that both message mutation and channel permutation positively influence performance, and we discuss their effects.
EthiX: A Dataset for Argument Scheme Classification in Ethical Debates
Authors: Bezou-Vrakatseli Elfia , Cocarascu Oana , Modgil Sanjay
Published: October 2024

Argument schemes represent stereotypical patterns of reasoning that capture the inferences from premise(s) to conclusion. Despite their usefulness in argument mining, argument scheme classification remains a largely understudied task in NLP. In this paper, we present EthiX, a novel dataset for classifying argument schemes, comprising arguments spanning 22 ethical topics which are manually annotated with argument schemes following Walton’s taxonomy. We evaluate pre-trained models fine-tuned on our dataset and propose a baseline to the community.
SAIE Framework: Support Alone Isn’t Enough - Advancing LLM Training with Adversarial Remarks
Authors: Loem Mengsay , Kaneko Masahiro , Okazaki Naoaki
Published: October 2024

Large Language Models (LLMs) can justify or critique their predictions through discussions with other models or humans, thereby enriching their intrinsic understanding of instances. While proactive discussions in the inference phase have been shown to boost performance, such interactions have not been extensively explored during the training phase. We hypothesize that incorporating interactive discussions into the training process can enhance the models’ understanding and improve their reasoning and verbal expression abilities during inference. This work introduces the SAIE framework, facilitating supportive and adversarial discussions between learner and partner models. The learner model receives responses from the partner, and its parameters are then updated based on this discussion. This dynamic adjustment process continues throughout the training phase, responding to the evolving outputs of the learner model. Our empirical evaluation across various tasks, including math word problems, commonsense reasoning, and multi-domain knowledge, demonstrates that models fine-tuned with the SAIE framework outperform those trained with conventional fine-tuning approaches. Furthermore, our method enhances the models’ reasoning capabilities, improving both individual and multi-agent inference performance. Our code is available at https://github.com/loem-ms/saie.
Generative LLMs for Multilingual Temporal Expression Normalization
Authors: Sánchez de Castro Alejandro , Araujo Lourdes , Martinez-Romo Juan
Published: October 2024

Assigning a numerical value to a temporal expression (TE), known as temporal expression normalization, is a crucial process for tasks like timeline creation and temporal reasoning. Rule-based and classical deep-learning normalization systems lack versatility because they are limited to specific domains and languages, while current Large Language Models (LLMs) solutions are relatively unexplored. To overcome the current limitations in adaptability, we suggest utilizing five of the latest generative Large Language Models (LLMs) - Mistral 7B, Gemma 7B, Gemma 2B, Phi-2, and Llama-3 8B. We have explored various performance enhancement strategies, including using different prompts, contexts, and training techniques like Neftune. Our proposed models demonstrate the ability to adapt to diverse domains (news and biomedical) and multiple languages (Spanish, English, Italian, French, Portuguese, Catalan, and Basque) simultaneously. These models can handle expressions in various domains and languages, making them more versatile and useful for a wide range of applications. As a result, our approach offers significant performance improvements when compared to existing LLM-based and rule-based solutions for TE normalization and a promising solution for the challenges of temporal normalization.
Audience Persona Knowledge-Aligned Prompt Tuning Method for Online Debate
Authors: Chan Chunkit , Cheng Jiayang , Liu Xin , Yim Yauwai , Jiang Yuxin , Deng Zheye , Li Haoran , Song Yangqiu , Wong Ginny Y , See Simon
Published: October 2024

Debate is the process of exchanging viewpoints or convincing others on a particular issue. Recent research has provided empirical evidence that the persuasiveness of an argument is determined not only by language usage but also by communicator characteristics. Researchers have paid much attention to aspects of languages, such as linguistic features and discourse structures, but combining argument persuasiveness and impact with the social personae of the audience has not been explored due to the difficulty and complexity. We have observed the impressive simulation and personification capability of ChatGPT, indicating a giant pre-trained language model may function as an individual to provide personae and exert unique influences based on diverse background knowledge. Therefore, we propose a persona knowledge-aligned framework for argument quality assessment tasks from the audience side. This is the first work that leverages the emergence of ChatGPT and injects such audience personae knowledge into smaller language models via prompt tuning. The performance of our pipeline demonstrates significant and consistent improvement compared to competitive architectures.
CoTran: An LLM-Based Code Translator Using Reinforcement Learning with Feedback from Compiler and Symbolic Execution
Authors: Jana Prithwish , Jha Piyush , Ju Haoyang , Kishore Gautham , Mahajan Aryan , Ganesh Vijay
Published: October 2024

In this paper, we present an LLM-based code translation method and an associated tool called CoTran, that translates whole-programs from one high-level programming language to another. Existing LLM-based code translation methods lack training to ensure that the translated code reliably compiles or bears substantial functional equivalence to the input code. In our work, we fine-tune an LLM using reinforcement learning, incorporating compiler feedback, and symbolic execution (symexec)-based testing feedback to assess functional equivalence between the input and output programs. The idea is to guide an LLM during fine-tuning, via compiler and symexec-based testing feedback, by letting it know how far it is from producing perfect translations. We conduct extensive experiments comparing CoTran with 14 other code translation tools, including human-written transpilers, LLM-based translation tools, and ChatGPT. Using a benchmark of over 57,000 code pairs in Java and Python, we demonstrate that CoTran outperforms the other tools on relevant metrics such as compilation accuracy (CompAcc) and functional equivalence accuracy (FEqAcc). For example, in Python-to-Java translation, CoTran achieves 48.68% FEqAcc and 76.98% CompAcc, whereas the nearest competing tool (PLBART-base) gets 38.26% and 75.77% respectively. Additionally, CoTran, built on top of CodeT5, improves FEqAcc by +14.89% and CompAcc by +8.14% for Python-to-Java (resp., +12.94% and +4.30% for Java-to-Python).
Generating Fair Solutions of Minimal Cost
Authors: Bederina Mohammed Bachir , Chaabane Djamal , Lust Thibaut
Published: October 2024

In this work, we consider combinatorial multi-agent optimization problems, i.e., problems presenting a combinatorial set of solutions, and each solution is evaluated through a vector. An element of the vector corresponds to the utility that an individual agent receives from the solution. Given potential conflicts, it is improbable that a single feasible solution will be optimal for all agents. Consequently, a relevant objective is to identify solutions that are fair to all agents. There are several approaches to defining fairness in the context of optimization problems, and here we focus on Lorenz-optimal solutions. However, in some cases, in addition to the search for a fair solution, an economic criterion comes into play, i.e., we also seek to find a least-cost solution. Since the optimal solution for the cost function is not necessarily fair (i.e., Lorenz-optimal in our case), our aim is to generate a Lorenz-optimal solution with minimum cost. We propose a new exact method to solve this problem, and apply it to the multi-agent assignment problem and to the multi-agent knapsack problem. Results show that the new method is much more efficient than a method based on a complete enumeration of Lorenz-optimal solutions.
NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning Problems
Authors: Núñez-Molina Carlos , Mesejo Pablo , Fernández-Olivares Juan
Published: October 2024

In the field of Automated Planning there is often the need for a set of planning problems from a particular domain, e.g., to be used as training data for Machine Learning or as benchmarks in planning competitions. In most cases, these problems are created either by hand or by a domain-specific generator, putting a burden on the human designers. In this paper we propose NeSIG, to the best of our knowledge the first domain-independent method for automatically generating planning problems that are valid, diverse and difficult to solve. We formulate problem generation as a Markov Decision Process and train two generative policies with Deep Reinforcement Learning to generate problems with the desired properties. We conduct experiments on three classical domains, comparing our approach against handcrafted, domain-specific instance generators and various ablations. Results show NeSIG is able to automatically generate valid and diverse problems of much greater difficulty (15.5 times more on geometric average) than domain-specific generators, while simultaneously reducing human effort when compared to them. Additionally, it can generalize to larger problems than those seen during training.