bluuebunny/crossref_metadata_2025 huggingface dataset:
Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts
Authors: Paul Sheryl , Deshmukh Jyotirmoy V.
Published: October 2024

Reinforcement learning (RL) has been successfully applied to solve the problem of finding obstacle-free paths for autonomous agents operating in stochastic and uncertain environments. However, when the underlying stochastic dynamics of the environment experiences drastic distribution shifts, the optimal policy obtained in the trained environment may be sub-optimal or may entirely fail in helping find goal-reaching paths for the agent. Approaches like domain randomization and robust RL can provide robust policies, but typically assume minor (bounded) distribution shifts. For substantial distribution shifts, retraining (either with a warm-start policy or from scratch) is an alternative approach. In this paper, we develop a novel approach called Evolutionary Robust Policy Optimization (ERPO), an adaptive re-training algorithm inspired by evolutionary game theory (EGT). ERPO learns an optimal policy for the shifted environment iteratively using a temperature parameter that controls the trade off between exploration and adherence to the old optimal policy. The policy update itself is an instantiation of the replicator dynamics used in EGT. We show that under fairly common sparsity assumptions on rewards in such environments, ERPO converges to the optimal policy in the shifted environment. We empirically demonstrate that for path finding tasks in a number of environments, ERPO outperforms several popular RL and deep RL algorithms (PPO, A3C, DQN) in many scenarios and popular environments. This includes scenarios where the RL algorithms are allowed to train from scratch in the new environment, when they are retrained on the new environment, or when they are used in conjunction with domain randomization. ERPO shows faster policy adaptation, higher average rewards, and reduced computational costs in policy adaptation.
Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous Agents
Authors: Gordon Lucia , Rolf Esther , Tambe Milind
Published: October 2024

Stochastic multi-agent multi-armed bandits typically assume that the rewards from each arm follow a fixed distribution, regardless of which agent pulls the arm. However, in many real-world settings, rewards can depend on the sensitivity of each agent to their environment. In medical screening, disease detection rates can vary by test type; in preference matching, rewards can depend on user preferences; and in environmental sensing, observation quality can vary across sensors. Since past work does not specify how to allocate agents of heterogeneous but known sensitivity of these types in a stochastic bandit setting, we introduce a UCB-style algorithm, Min-Width, which aggregates information from diverse agents. In doing so, we address the joint challenges of (i) aggregating the rewards, which follow different distributions for each agent-arm pair, and (ii) coordinating the assignments of agents to arms. Min-Width facilitates efficient collaboration among heterogeneous agents, exploiting the known structure in the agents’ reward functions to weight their rewards accordingly. We analyze the regret of Min-Width and conduct pseudo-synthetic and fully synthetic experiments to study the performance of different levels of information sharing. Our results confirm that the gains to modeling agent heterogeneity tend to be greater when the sensitivities are more varied across agents, while combining more information does not always improve performance.
MindScope: Exploring Cognitive Biases in Large Language Models Through Multi-Agent Systems
Authors: Xie Zhentao , Zhao Jiabao , Wang Yilei , Shi Jinxin , Bai Yanhong , Wu Xingjiao , He Liang
Published: October 2024

Detecting cognitive biases in large language models (LLMs) is a fascinating task that aims to probe the existing cognitive biases within these models. Current methods for detecting cognitive biases in language models generally suffer from incomplete detection capabilities and a restricted range of detectable bias types. To address this issue, we introduced the ‘MindScope’ dataset, which distinctively integrates static and dynamic elements. The static component comprises 5,170 open-ended questions spanning 72 cognitive bias categories. The dynamic component leverages a rule-based, multi-agent communication framework to facilitate the generation of multi-round dialogues. This framework is flexible and readily adaptable for various psychological experiments involving LLMs. In addition, we introduce a multi-agent detection method applicable to a wide range of detection tasks, which integrates Retrieval-Augmented Generation (RAG), competitive debate, and a reinforcement learning-based decision module. Demonstrating substantial effectiveness, this method has shown to improve detection accuracy by as much as 35.10% compared to GPT-4. Codes and appendix are available at https://github.com/2279072142/MindScope.
Reducing Systemic Risk in Financial Networks through Donations
Authors: Tong Jinyun , De Keijzer Bart , Ventre Carmine
Published: October 2024

We examine the extent to which rescue strategies within a banking system can reduce systemic risk. We focus on donations from solvent banks to banks in distress, which can in principle reduce losses and prevent default cascades. We build an agent-based model to simulate the ensuing strategic game on a randomly generated financial network, where nodes represent banks and edges represent inter-bank liabilities. Each bank independently decides whether to rescue (and whom) to maximise their payoffs. We analyse the rescue strategies adopted by the banks at equilibrium, using empirical game-theoretic analysis. Our results show that donations can indeed reduce systemic risk when the equilibrium strategy profile is adopted. Individual donations can benefit multiple banks in the network. Our results also indicate that lower default costs and small-variance liabilities tend to decrease the incentives to donate. We furthermore examine the impact of the banks’ rationality on the effects of rescue, finding that banks behaving rationally use their funds for rescues more efficiently than banks that behave irrationally.
Parameterized Algorithms for Optimal Refugee Resettlement
Authors: Chen Jiehua , Schlotter Ildikó , Simola Sofia
Published: October 2024

We study variants of the Optimal Refugee Resettlement problem where a set F of refugee families need to be allocated to a set P of possible places of resettlement in a feasible and optimal way. Feasibility issues emerge from the assumption that each family requires certain services (such as accommodation, school seats, or medical assistance), while there is an upper and, possibly, a lower quota on the number of service units provided at a given place. Besides studying the problem of finding a feasible assignment, we also investigate two natural optimization variants. In the first one, we allow families to express preferences over P, and we aim for a Pareto-optimal assignment. In a more general setting, families can attribute utilities to each place in P, and the task is to find a feasible assignment with maximum total utilities. We study the computational complexity of all three variants in a multivariate fashion using the framework of parameterized complexity. We provide fixed-parameter algorithms for a handful of natural parameterizations, and complement these tractable cases with tight intractability results.
Accelerating Machine Learning for Trading Using Programmable Switches
Authors: Hong Xinpeng , Zheng Changgang , Zohren Stefan , Zilberman Noa
Published: October 2024

High-frequency trading (HFT) employs cutting-edge hardware for rapid decision-making and order execution but often relies on simpler algorithms that may miss deeper market trends. Conversely, lower-frequency algorithmic trading uses machine learning (ML) for better market predictions but higher latency can negate its strategic benefits. To achieve the best of both worlds, we present an in-network ML solution that embeds ML processes into programmable network devices, accelerating feature engineering and extraction as well as ML inference. In this paper, we design and develop a solution that supports both stock mid-price and volatility movement forecasting using commodity switches. Our approach achieves microsecond-scale, ultra-low latency, significantly lowering it by 64% to 97% compared to previous works, while upholding the same level of ML performance as server models. Additionally, by combining network hardware and servers, a hybrid deployment strategy can keep the misclassification rate change below 0.8% relative to the server baseline while processing 49% of the traffic directly on the switch and achieving a 45% average reduction in end-to-end latency.
Mechanism Design for Extending the Accessibility of Facilities
Authors: Chan Hau , Lin Jianan , Wang Chenhao , Xie Yanxi
Published: October 2024

We study a variation of facility location problems (FLPs) that aims to improve the accessibility of agents to the facility within the context of mechanism design without money. In such a variation, agents have preferences on the ideal locations of the facility on a real line, and the facility’s location is fixed in advance where (re)locating the facility is not possible due to various constraints (e.g., limited space and construction costs). To improve the accessibility of agents to facilities, existing mechanism design literature in FLPs has proposed to structurally modify the real line (e.g., by adding a new interval) or provide shuttle services between two points when structural modifications are not possible. In this paper, we focus on the latter approach and propose to construct an accessibility range to extend the accessibility of the facility. In the range, agents can receive accommodations (e.g., school buses, campus shuttles, or pickup services) to help reach the facility. Therefore, the cost of each agent is the distance from their ideal location to the facility (possibility) through the range. We focus on designing strategyproof mechanisms that elicit true ideal locations from the agents and construct accessibility ranges (intervals) to approximately minimize the social cost or the maximum cost of agents. For both social and maximum costs, we design group strategyproof mechanisms and strong group strategyproof mechanisms with (asymptotically) tight bounds on the approximation ratios.
Explaining the Lack of Locally Envy-Free Allocations
Authors: Beynier Aurélie , Mailly Jean-Guy , Maudet Nicolas , Wilczynski Anaëlle
Published: October 2024

In fair division, local envy-freeness is a desirable property which has been thoroughly studied in recent years. In this paper, we study explanations which can be given to explain that no allocation of items can satisfy this criterion, in the house allocation setting where agents receive a single item. While Minimal Unsatisfiable Subsets (MUSes) are key concepts to extract explanations, they cannot be used as such: (i) they highly depend on the initial encoding of the problem; (ii) they are flat structures which fall short of capturing the dynamics of explanations; (iii) they typically come in large number and exhibit great diversity. In this paper we provide two SAT encodings of the problem which allow us to extract MUS when instances are unsatisfiable. We build a dynamic graph structure which allows to follow step-by-step the explanation. Finally, we propose several criteria to select MUSes, some of them being based on the MUS structure, while others rely on this original graphical explanation structure. We give theoretical bounds on these metrics, showing that they can vary significantly for some instances. Experimental results on synthetic data complement these results and illustrate the impact of the encodings and the relevance of our metrics to select among the many MUSes.
Do We Care About Poll Manipulation in Political Elections?
Authors: Mousseau Vincent , Surugue Henri , Wilczynski Anaëlle
Published: October 2024

We consider the problem of poll manipulation in political elections. In the context of strategic voting, we are interested in whether a polling institute can manipulate the information it communicates to voters in order to influence the outcome of the election. We start with a version of the problem where the polling institute is allowed to send any score to voters. Then, for realistic reasons, we investigate a restricted version in which the polling institute cannot announce scores which are too far from the truthful ones. While we show that both decision problems are computationally hard, we go beyond this worst-case complexity analysis by using probabilistic tools to address the possibility of successful and efficient manipulation in practice, w.r.t. several natural preference distributions.
A Single Online Agent Can Efficiently Learn Mean Field Games
Authors: Zhang Chenyu , Chen Xu , Di Xuan
Published: October 2024

Mean field games (MFGs) are a promising framework for modeling the behavior of large-population systems. However, solving MFGs can be challenging due to the coupling of forward population evolution and backward agent dynamics. Typically, obtaining mean field Nash equilibria (MFNE) involves an iterative approach where the forward and backward processes are solved alternately, known as fixed-point iteration (FPI). This method requires fully observed population propagation and agent dynamics over the entire spatial domain, which could be impractical in some real-world scenarios. To overcome this limitation, this paper introduces a novel online single-agent model-free learning scheme, which enables a single agent to learn MFNE using online samples, without prior knowledge of the state-action space, reward function, or transition dynamics. Specifically, the agent updates its policy through the value function (Q), while simultaneously evaluating the mean field state (M), using the same batch of observations. We develop two variants of this learning scheme: off-policy and on-policy QM iteration. We prove that they efficiently approximate FPI, and a sample complexity guarantee is provided. The efficacy of our methods is confirmed by numerical experiments.
Real-Time Goal Recognition Using Approximations in Euclidean Space
Authors: Antunes Tesch Douglas , Amado Leonardo , Meneguzzi Felipe
Published: October 2024

While recent work on online goal recognition efficiently infers goals under low observability, comparatively less work focuses on online goal recognition that works in both discrete and continuous domains. Online goal recognition approaches often rely on repeated calls to the planner at each new observation, incurring high computational costs. Recognizing goals online in continuous space quickly and reliably is critical for any trajectory planning problem since the real physical world is fast-moving, e.g. robot applications. We develop an efficient method for goal recognition that relies either on a single call to the planner for each possible goal in discrete domains or a simplified motion model that reduces the computational burden in continuous ones. The resulting approach performs the online component of recognition orders of magnitude faster than the current state of the art, making it the first online method effectively usable for robotics applications that require sub-second recognition.
Reaching New Heights in Multi-Agent Collective Construction
Authors: Rameš Martin , Surynek Pavel
Published: October 2024

We propose a new approach for multi-agent collective construction, based on the idea of reversible ramps. Our ReRamp algorithm utilizes reversible side-ramps to generate construction plans for ramped block structures higher and larger than was previously possible using state-of-the-art planning algorithms, given the same building area. We compare the ReRamp algorithm to similar state-of-the-art algorithms on a set of benchmark instances, where we demonstrate its superior computational speed. We also establish in our experiments that the ReRamp algorithm is capable of generating plans for a single-story house, an important milestone on the road to real-world multi-agent construction applications.
Comateformer: Combined Attention Transformer for Semantic Sentence Matching
Authors: Li Bo , Liang Di , Zhang Zixin
Published: October 2024

The Transformer-based model have made significant strides in semantic matching tasks by capturing connections between phrase pairs. However, to assess the relevance of sentence pairs, it is insufficient to just examine the general similarity between the sentences. It is crucial to also consider the tiny subtleties that differentiate them from each other. Regrettably, attention softmax operations in transformers tend to miss these subtle differences. To this end, in this work, we propose a novel semantic sentence matching model named Combined Attention Network based on Transformer model (Comateformer). In Comateformer model, we design a novel transformer-based quasi-attention mechanism with compositional properties. Unlike traditional attention mechanisms that merely adjust the weights of input tokens, our proposed method learns how to combine, subtract, or resize specific vectors when building a representation. Moreover, our proposed approach builds on the intuition of similarity and dissimilarity (negative affinity) when calculating dual affinity scores. This allows for a more meaningful representation of relationships between sentences. To evaluate the performance of our proposed model, we conducted extensive experiments on ten public real-world datasets and robustness testing. Experimental results show that our method achieves consistent improvements.
SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM
Authors: Wang Quandong , Yuan Yuxuan , Yang Xiaoyu , Zhang Ruike , Zhao Kang , Liu Wei , Luan Jian , Povey Daniel , Wang Bin
Published: October 2024

While Large Language Models (LLMs) have achieved remarkable success in various fields, the efficiency of training and inference remains a major challenge. To address this issue, we propose SUBLLM, short for Subsampling-Upsampling-Bypass Large Language Model, an innovative architecture that extends the core decoder-only framework by incorporating subsampling, upsampling, and bypass modules. The subsampling modules are responsible for shortening the sequence, while the upsampling modules restore the sequence length, and the bypass modules enhance convergence. In comparison to LLaMA, the proposed SUBLLM exhibits significant enhancements in both training and inference speeds as well as memory usage, while maintaining competitive few-shot performance. During training, SUBLLM increases speeds by 26% and cuts memory by 10GB per GPU. In inference, it boosts speeds by up to 37% and reduces memory by 1GB per GPU. The training and inference speeds can be enhanced by 34% and 52% respectively when the context window is expanded to 8192. Our code is available at https://github.com/XiaoMi/subllm.
SF-SER: An Efficient Speech-Only Model with Semantic Funnel for Speech Emotion Recognition
Authors: Li Xiaoke , Zhang Zufan
Published: October 2024

In practical life, the transcription of speech into text via automatic speech recognition (ASR) models has become very common due to the essential semantic information contained in speech. However, in speech emotion recognition, multimodal models that combine speech and text significantly outperform speech-only models. For this phenomenon, this paper provides an explanation that the existing speech emotion datasets are insufficient for speech-only models to effectively extract crucial semantic information, thereby affecting generalization capability. Based on this explanation, this paper proposes an efficient speech-only model, called semantic funnel speech emotion recognition (SF-SER) model, which excludes the textual input by introducing and integrating some parameters and structures from the ASR model, and then filters the valuable semantic information by the semantic funnel, thus achieving performance better than the speech+text multimodal model. Finally, experimental results show that the SF-SER model achieves significant performance on both the IEMOCAP and EMODB datasets.
Global Structural-Temporal Graph Network with Public Opinion for Online Rumor Detection
Authors: Sun Tiening , Qian Zhong , Li Peifeng , Zhu Qiaoming
Published: October 2024

Rumors on social media can spread rapidly and widely with the help of the Internet characteristics, causing serious negative impacts on social stability and public life. In order to distinguish rumors from non-rumors, most of the existing methods are based on neural units to encode and observe the content of claims, user comments and rumor propagation patterns. However, these methods only consider the event context information in a single conversation thread, ignoring the public opinion (global contextual information) corresponding to the event in the external news environment. Be aware that users are easily distracted by opinion leaders to false facts and induced to make supportive replies on false claims. In order to address the above-mentioned limitation, we propose a Global Structural-Temporal Graph Network (GSTGN) framework. Specifically, we first construct a multi-modal global opinion graph based on the conversation threads belonging to the same event to capture the external public opinion of the target event. Then to enhance representation learning, we design a Structural-Temporal (ST) unit to encode structural and temporal features of the local conversation graph, and utilize the structural feature of the local graph to guide the learning and encoding of the global opinion graph. Experimental results on two public benchmark datasets prove that our GSTGN method achieves better results than other state-of-the-art models.
Revisiting Under-Represented Knowledge of Latin American Literature in Large Language Models
Authors: Kim Jinsung , Koo Seonmin , Lim Heuiseok
Published: October 2024

With the advent of large language models (LLMs), concerns about knowledge bias have recently increased. Previously, prevalent research has focused on detecting the bias of model knowledge by providing explicit social terms, such as race, gender, and age, into inputs. However, revealing the subtle and implicit bias of the model knowledge requires verification utilizing language expressed in a more implied form, such as literary works. This is because literature implicitly contains subjective filters of individuals and their living regional culture. Accordingly, this study aims to probe a research question of whether LLMs have a knowledge under-representation problem between two different regions using the same language, Spain and Spanish-speaking countries in Latin America. To this end, we design an under-representation verification task, REGion and Literary Author prediction (REGLA) and dataset based on Spanish-written literary works. Inspired by the knowledge shortcut concept from a previous study, REGLA consists of two tasks to figure out meta-information of poems, i.e., region and author. Moreover, we explore various prompting methods that can unleash the knowledge observed to be under-represented within the verification process. According to the verification and prompt engineering results, knowledge about the literary works of Latin American countries appears to be more under-represented compared to those of Spain in LLMs. It is also observed that the task decomposition prompting method effectively lets under-represented knowledge be generated.
Text-Based Geolocation with a Byte-Level Language Model
Authors: Orzhenovskii Mikhail , Pak Alina , Kane Roman , Razmyslovich Arina
Published: October 2024

Geotagging, the process of assigning geographical coordinates to text, has found a wide range of applications, from disaster management to public health surveillance. Contrary to the prior focus on user-level predictions and platform-specific data, this study emphasizes tweet-level geolocation using text content. We present a method combining hierarchical agglomerative clustering with classification, employing a byte-level transformer model trained on a multi-lingual dataset. Our approach, evaluated for its efficacy in selecting the top 10% of geographically relevant tweets, achieves a Median Error of 15 km and a Mean Error of 403 km, significantly surpassing existing methods by margins of 560 km and 2314 km, respectively, as demonstrated in Table 2. Moreover, we investigate uncertainty estimation techniques in multi-class classification for transformer models, an underexplored area in geotagging research. Although some methods show potential, achieving oracle-level accuracy remains a challenge. Our work extends the field of geotagging by focusing on text-only tweet-level predictions and provides valuable insights into uncertainty estimation methods for transformer models in this domain.
Assessing Privacy Risks of Attribute Inference Attacks Against Speech-Based Depression Detection System
Authors: Alsenani Basmah , Esposito Anna , Vinciarelli Alessandro , Guha Tanaya
Published: October 2024

Many AI applications now attempt to infer users’ mental health conditions, such as depression, from their speech data. In addition to the spoken words, the speech audio contains information about speaker’s identity and demographic attributes, exposing users to serious privacy risks. Previous efforts have primarily focused on developing deep models that preserve privacy; however, there have been few attempts to systematically assess and quantify privacy risks in such systems. We present the first framework for systematically assessing privacy risks in a multimodal (audio-lexical) depression detection system particularly looking at attribute inference attacks. Unlike past works that considered only white-box gender inference attacks against unimodal systems, our framework designs novel white-box and black-box attacks across multiple modalities against three protected speaker attributes: gender, age and education level. We present extensive results on a large, clinically validated dataset, demonstrating critical vulnerability of depression detection systems, where an adversary can infer speaker attributes with 59% - 68% accuracy even for inputs as short as 10 seconds of speech. Our results offer insights and guidelines to inform the development and benchmarking of privacy-preserving models for speech-based depression detection systems. Our code and data are available at: https://github.com/apr-aia/privacy_risks
Leveraging Foundation Models for Zero-Shot IoT Sensing
Authors: Xue Dinghao , Fan Xiaoran , Chen Tao , Lan Guohao , Song Qun
Published: October 2024

Deep learning models are increasingly deployed on edge Internet of Things (IoT) devices. However, these models typically operate under supervised conditions and fail to recognize unseen classes different from training. To address this, zero-shot learning (ZSL) aims to classify data of unseen classes with the help of semantic information. Foundation models (FMs) trained on web-scale data have shown impressive ZSL capability in natural language processing and visual understanding. However, leveraging FMs’ generalized knowledge for zero-shot IoT sensing using signals such as mmWave, IMU, and Wi-Fi has not been fully investigated. In this work, we align the IoT data embeddings with the semantic embeddings generated by an FM’s text encoder for zero-shot IoT sensing. To utilize the physics principles governing the generation of IoT sensor signals to derive more effective prompts for semantic embedding extraction, we propose to use cross-attention to combine a learnable soft prompt that is optimized automatically on training data and an auxiliary hard prompt that encodes domain knowledge of the IoT sensing task. To address the problem of IoT embeddings biasing to seen classes due to the lack of unseen class data during training, we propose using data augmentation to synthesize unseen class IoT data for fine-tuning the IoT feature extractor and embedding projector. We evaluate our approach on multiple IoT sensing tasks. Results show that our approach achieves superior open-set detection and generalized zero-shot learning performance compared with various baselines. Our code is available at https://github.com/schrodingho/FM_ZSL_IoT.
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection
Authors: Ma Xingyu , Tian Xin , Wu Lingxiang , Wang Xuepeng , Tang Xueming , Wang Jinqiao
Published: October 2024

Text-to-SQL is a subtask in semantic parsing that has seen rapid progress with the evolution of Large Language Models (LLMs). However, LLMs face challenges due to hallucination issues and a lack of domain-specific database knowledge(such as table schema and cell values). As a result, they can make errors in generating table names, columns, and matching values to the correct columns in SQL statements. This paper introduces a method of knowledge injection to enhance LLMs’ ability to understand schema contents by incorporating prior knowledge. This approach improves their performance in Text-to-SQL tasks. Experimental results show that pre-training LLMs on domain-specific database knowledge and fine-tuning them on downstream Text-to-SQL tasks significantly improves the Execution Match (EX) and Exact Match (EM) metrics across various models. This effectively reduces errors in generating column names and matching values to the columns. Furthermore, the knowledge-injected models can be applied to many downstream Text-to-SQL tasks, demonstrating the generalizability of the approach presented in this paper.
A Hybrid Approach towards Chinese Spelling and Splitting Error Correction
Authors: Liang Junhong , Zhu Junnan , Zhai Feifei , Cheng Nanchang , Zong Chengqing , Zhou Yu
Published: October 2024

Existing Chinese spelling check (CSC) methods have limitations in correcting variable-length error characters, requiring the input and output to be the same length. They mainly focus on modelling Chinese characters’ phonetic information and generating candidates for each position. In contrast, few approaches delve into the intricacies of splitting Chinese characters to address glyph errors and splitting variable-length corrections. We define the Chinese Splitting Error Correction (CSEC) task and develop CSEC datasets in news and social media domains to address this issue. We then propose Soft-Masked Multi-feature Error Correction (SoMu) model, which first generates semantic, phonetic, graphic, and unique Chinese Wubi embeddings, then integrates those features through selective gating fusion, followed by a soft-mask strategy to filter incorrect tokens and finally use transformer layers to predict the correct ones. This model effectively addresses both spelling and splitting errors. Extensive analysis shows that our model significantly improves character-splitting information modelling for CSEC. Our dataset is available at https://github.com/Skywalker-Harrison/SoMu.
SETTP: Style Extraction and Tunable Inference via Dual-Level Transferable Prompt Learning
Authors: Jin Chunzhen , Huang Yongfeng , Wang Yaqi , Cao Peng , Zaïane Osmar
Published: October 2024

Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24%.
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models
Authors: Zhang Jiawen , Chen Kejia , Feng Zunlei , Lou Jian , Song Mingli
Published: October 2024

With the growing popularity of LLMs among the general public users, privacy-preserving and adversarial robustness have become two pressing demands for LLM-based services, which have largely been pursued separately but rarely jointly. In this paper, to the best of our knowledge, we are among the first attempts towards robust and private LLM inference by tightly integrating two disconnected fields: private inference and prompt ensembling. The former protects users’ privacy by encrypting inference data transmitted and processed by LLMs, while the latter enhances adversarial robustness by yielding an aggregated output from multiple prompted LLM responses. Although widely recognized as effective individually, private inference for prompt ensembling together entails new challenges that render the naive combination of existing techniques inefficient. To overcome the hurdles, we propose SecPE, which designs efficient fully homomorphic encryption (FHE) counterparts for the core algorithmic building blocks of prompt ensembling. We conduct extensive experiments on 8 tasks to evaluate the accuracy, robustness, and efficiency of SecPE. The results show that SecPE  maintains high clean accuracy and offers better robustness at the expense of merely 2.5% efficiency overhead compared to baseline private inference methods, indicating a satisfactory “accuracy-robustness-efficiency” tradeoff. For the efficiency of the encrypted Argmax operation that incurs major slowdown for prompt ensembling, SecPE  is 35.4 times faster than the state-of-the-art peers, which can be of independent interest beyond this work.
Diversity-Enhanced Learning for Unsupervised Syntactically Controlled Paraphrase Generation
Authors: Wu Shaojuan , Li Jitong , Sun Yue , Zhang Xiaowang , Feng Zhiyong
Published: October 2024

Syntactically controlled paraphrase generation is to generate diverse sentences that have the same semantics as the given original sentence but conform to the target syntactic structure. An optimal opportunity to enhance diversity is to make word substitutions during rephrasing based on syntactic control. Existing unsupervised methods have made great progress in syntactic control, but the generated paraphrases rarely have substitutions due to the limitation of training data. In this paper, we propose a Diversity syntactically controlled Paraphrase generation framework (DiPara), in which a novel training strategy is designed to obtain semantic sentences while using the given sentence as training objects. As diverse words vary the syntactic structure around them, we propose a phrase-aware attention mechanism to capture the syntactic structure associated with the current word. To achieve it, the linearized triple sequence is introduced to represent structure singly. Experiment results on two datasets show that DiPara outperforms strong baselines, especially diversity (Self-BLEU4) is improved by 10.18% in ParaNMT-Small.
FltLM: An Intergrated Long-Context Large Language Model for Effective Context Filtering and Understanding
Authors: Deng Jingyang , Shen Zhengyang , Wang Boyang , Su Lixin , Cheng Suqi , Nie Ying , Wang Junfeng , Yin Dawei , Ma Jinwen
Published: October 2024

The development of Long-Context Large Language Models (LLMs) has markedly advanced natural language processing by facilitating the process of textual data across long documents and multiple corpora. However, Long-Context LLMs still face two critical challenges: The lost in the middle phenomenon, where crucial middle-context information is likely to be missed, and the distraction issue that the models lose focus due to overly extended contexts. To address these challenges, we propose the Context Filtering Language Model (FltLM), a novel integrated Long-Context LLM which enhances the ability of the model on multi-document question-answering (QA) tasks. Specifically, FltLM innovatively incorporates a context filter with a soft mask mechanism, identifying and dynamically excluding irrelevant content to concentrate on pertinent information for better comprehension and reasoning. Our approach not only mitigates these two challenges, but also enables the model to operate conveniently in a single forward pass. Experimental results demonstrate that FltLM significantly outperforms supervised fine-tuning and retrieval-based methods in complex QA scenarios, suggesting a promising solution for more accurate and reliable long-context natural language understanding applications.
BRECS: Enhanced Binary Representation of Word Embeddings via Cosine Similarity
Authors: Sarkar Rajdeep , Dutta Sourav , McCrae John
Published: October 2024

Word representations like GloVe and Word2Vec encapsulate semantic and syntactic attributes and constitute the fundamental building block in diverse Natural Language Processing (NLP) applications. Such vector embeddings are typically stored in float32 format, and for a substantial vocabulary size, they impose considerable memory and computational demands due to the resource-intensive float32 operations. Thus, representing words via binary embeddings has emerged as a promising but challenging solution. In this paper, we introduce BRECS, an autoencoder-based Siamese framework for the generation of enhanced binary word embeddings (from the original embeddings). We propose the use of the novel Binary Cosine Similarity (BCS) regularisation in BRECS, which enables it to learn the semantics and structure of the vector space spanned by the original word embeddings, leading to better binary representation generation. We further show that our framework is tailored with independent parameters within the various components, thereby providing it with better learning capability. Extensive experiments across multiple datasets and tasks demonstrate the effectiveness of BRECS, compared to existing baselines for static and contextual binary word embedding generation. The source code is available at https://github.com/rajbsk/brecs.
Enhancing Stance Detection on Social Media via Core Views Discovery
Authors: Yan Yu , Shen Yinghan , Liu Teli , Jiang Xuhui , Yin Dechun
Published: October 2024

Stance detection aims to identify the expressed attitude towards a target from the text, which is significant for learning public cognition from social media. The short and implicit nature of social media users’ expressions potentially results in the stance understanding bias of the model. To address this problem, introducing external background information is helpful to mitigate these biases and enhance explainability. The core view, reflecting the motivations and reasons behind an individual’s stance toward the target, can be summarized and extracted from collective tweets, which can serve as a reference for stance detection. In this study, we propose the Stance Detection via Core View Discovery (SD-CVM), where the core views are used for background information modeling. Specifically, we construct a joint classifier combining the semantic understanding of tweets and their relevant core views from the public. We utilize the Large Language Model (LLM) to extract core views with stances from tweets and use these core views as background references for tweets. To further optimize the tweet understanding, we develop the contrastive and rebalancing mechanism by incorporating stance supervision signals for training. Experiments on two representative datasets demonstrate the excellent performance of our method.
REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning
Authors: Qureshi Rameez , Es-Sebbani Naïm , Galárraga Luis , Graham Yvette , Couceiro Miguel , Bouraoui Zied
Published: October 2024

With the introduction of (large) language models, there has been significant concern about the unintended bias such models may inherit from their training data. A number of studies have shown that such models propagate gender stereotypes, as well as geographical and racial bias, among other biases. While existing works tackle this issue by preprocessing data and debiasing embeddings, the proposed methods require a lot of computational resources and annotation effort while being limited to certain types of biases. To address these issues, we introduce REFINE-LM, a debiasing method that uses reinforcement learning to handle different types of biases without any fine-tuning. By training a simple model on top of the word probability distribution of a LM, our bias agnostic reinforcement learning method enables model debiasing without human annotations or significant computational resources. Experiments conducted on a wide range of models, including several LMs, show that our method (i) significantly reduces stereotypical biases while preserving LMs performance; (ii) is applicable to different types of biases, generalizing across contexts such as gender, ethnicity, religion, and nationality-based biases; and (iii) it is not expensive to train.
Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic
Authors: An Ziyan , Baier Hendrik , Dubey Abhishek , Mukhopadhyay Ayan , Ma Meiyi
Published: October 2024

Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation and transit planning. Despite its strong performance in real-world deployment, the inherent complexity of MCTS makes it challenging to understand for users without technical background. This paper considers the use of MCTS in transportation routing services, where the algorithm is integrated to develop optimized route plans. These plans are required to meet a range of constraints and requirements simultaneously, further complicating the task of explaining the algorithm’s operation in real-world contexts. To address this critical research gap, we introduce a novel computation tree logic-based explainer for MCTS. Our framework begins by taking user-defined requirements and translating them into rigorous logic specifications through the use of language templates. Then, our explainer incorporates a logic verification and quantitative evaluation module that validates the states and actions traversed by the MCTS algorithm. The outcomes of this analysis are then rendered into human-readable descriptive text using a second set of language templates. The user satisfaction of our approach was assessed through a survey with 82 participants. The results indicated that our explanatory approach significantly outperforms other baselines in user preference.
Iterative Oversubscription Planning with Goal-Conflict Explanations: Scaling Up Through Policy-Guidance Approximation
Authors: Eifler Rebecca , Fišer Daniel , Siji Aleena , Hoffmann Jörg
Published: October 2024

In oversubscription planning (OSP), not all goals can be achieved. If a global optimization objective is difficult to fix, then an iterative planning process in which users refine their objective based on sample plans is suitable. Recent work has shown that, in such a process, explanations of plan trade-offs based on goal conflicts – minimal unsolvable goal subsets (MUGS) – are useful. A fundamental limitation of this approach is scalability. Computing MUGS is feasible only in relatively small planning instances; sometimes plan generation in iterative planning also is a limiting factor as users tend to be impatient. Here we address both these limitations by restricting the space of plans considered. We assume that an action policy π for the OSP task has been learned. We restrict both plan generation and MUGS analysis to the action sequences within a given radius r around π, so that r controls the tradeoff between scalability and the degree of approximation. We instantiate this idea with two different kinds of radii around a policy. We experimentally analyze performance as a function of r, for Action Schema Network policies. The results confirm that our approach can scale up further than prior work, and results on instances small enough to compute MUGS exactly indicate that we obtain informative MUGS even with limited runtime and memory.
Cube-Based Isomorph-Free Finite Model Finding
Authors: Chow Choiwah , Janota Mikoláš , Araújo João
Published: October 2024

Complete enumeration of finite models of first-order logic (FOL) formulas is pivotal to universal algebra, which studies and catalogs algebraic structures. Efficient finite model enumeration is highly challenging because the number of models grows rapidly with their size but at the same time, we are only interested in models modulo isomorphism. While isomorphism cuts down the number of models of interest, it is nontrivial to take that into account computationally. This paper develops a novel algorithm that achieves isomorphism-free enumeration by employing isomorphic graph detection algorithm nauty, cube-based search space splitting, and compact model representations. We name our algorithm cube-based isomorph-free finite model finding algorithm (CBIF). Our approach contrasts with the traditional two-step algorithms, which first enumerate (possibly isomorphic) models and then filter the isomorphic ones out in the second stage. The experimental results show that CBIF is many orders of magnitude faster than the traditional two-step algorithms. CBIF enables us to calculate new results that are not found in the literature, including the extension of two existing OEIS sequences, thereby advancing the state of the art.
Learning Interpretable Classifiers for PDDL Planning
Authors: Lequen Arnaud
Published: October 2024

We consider the problem of synthesizing interpretable models that recognize the behaviour of an agent compared to other agents, on a whole set of similar planning tasks expressed in PDDL. Our approach consists in learning logical formulas, from a small set of examples that show how an agent solved small planning instances. These formulas are expressed in a version of First-Order Temporal Logic (FTL) tailored to our planning formalism. Such formulas are human-readable, serve as (partial) descriptions of an agent’s policy, and generalize to unseen instances. We show that learning such formulas is computationally intractable, as it is an NP-hard problem. As such, we propose to learn these behaviour classifiers through a topology-guided compilation to MaxSAT, which allows us to generate a wide range of different formulas. Experiments show that interesting and accurate formulas can be learned in reasonable time.
Finding Optimal Deterministic Policies for Constrained Stochastic Shortest Path Problems
Authors: Schmalz Johannes , Trevizan Felipe
Published: October 2024

Constrained Stochastic Shortest Path problems (CSSPs) are a modelling framework for probabilistic problems with a primary cost and constraints over secondary costs such as fuel consumption or monetary budget. While the optimal solution for a CSSP is usually a stochastic policy, practical considerations often demand deterministic solutions, for instance, in aviation and multi-agent systems. Previous works have addressed this issue for special cases of CSSPs; in this work, we show the technical issues in generalising these results and show how they can be addressed. Then, using these methods, we extend the state-of-the-art heuristic search method for finding optimal stochastic policies to efficiently find deterministic policies for CSSPs. We show experimentally that our algorithm competes with the state-of-the-art, and is able to solve the class of problems with difficult-to-satisfy constraints on which the state-of-the-art fails.