Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilities of large language models (LLMs). However, is this effectiveness and cost-efficiency truly a free lunch? In this study, we comprehensively investigate the fairness costs associated with RAG by proposing a practical three-level threat model from the perspective of user awareness of fairness. Specifically, varying levels of user fairness awareness result in different degrees of fairness censorship on the external dataset. We examine the fairness implications of RAG using uncensored, partially censored, and fully censored datasets. Our experiments demonstrate that fairness alignment can be easily undermined through RAG without the need for fine-tuning or retraining. Even with fully censored and supposedly unbiased external datasets, RAG can lead to biased outputs. Our findings underscore the limitations of current alignment methods in the context of RAG-based LLMs and highlight the urgent need for new strategies to ensure fairness. We propose potential mitigations and call for further research to develop robust fairness safeguards in RAG-based LLMs.

The Learning Rate (LR) has a high impact on deep learning training performance. A common practice is to train a Deep Neural Network (DNN) multiple times with different LR policies to find the optimal LR policy, which has been widely recognized as a daunting and costly task. Moreover, multiple times of DNN training has not been effectively utilized. In practice, often only the optimal LR is adopted, which misses the opportunities to further enhance the overall accuracy of the deep learning system and results in a huge waste of both computing resources and training time. This paper presents a novel framework, LREnsemble, to effectively leverage effective learning rate tuning to boost deep ensemble performance. We make three original contributions. First, we show that the LR tuning with different LR policies can produce highly diverse DNNs, which can be supplied as base models for deep ensembles. Second, we leverage different ensemble selection algorithms to identify high-quality deep ensembles from the large pool of base models with significant accuracy improvements over the best single base model. Third, we propose LREnsemble, a framework that utilizes the synergy of LR tuning and deep ensemble techniques to enhance deep learning performance. The experiments on multiple benchmark datasets have demonstrated the effectiveness of LREnsemble, generating up to 2.34% accuracy improvements over well-optimized baselines.

Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence (AI) due to their brain-inspired and energy-efficient properties. In the current supervised learning domain of SNNs, compared to vanilla Spatial-Temporal Back-propagation (STBP) training, online training can effectively overcome the risk of GPU memory explosion and has received widespread academic attention. However, the current proposed online training methods cannot tackle the inseparability problem of temporal dependent gradients and merely aim to optimize the training memory, resulting in no performance advantages compared to the STBP training models in the inference phase. To address the aforementioned challenges, we propose Efficient Multi-Precision Firing (EM-PF) model, which is a family of advanced spiking models based on floating-point spikes and binary synaptic weights. We point out that EM-PF model can effectively separate temporal gradients and achieve full-stage optimization towards computation speed and memory footprint. Experimental results have demonstrated that EM-PF model can be flexibly combined with various techniques including random back-propagation, parallel computation and channel attention mechanism, to achieve state-of-the-art performance with extremely low computational overhead in the field of online learning.

The ability of large language models (LLMs) to execute complex instructions is essential for their real-world applications. However, several recent studies indicate that LLMs struggle with challenging instructions. In this paper, we propose Evolutionary Contrastive Distillation (ECD), a novel method for generating high-quality synthetic preference data designed to enhance the complex instruction-following capability of language models. ECD generates data that specifically illustrates the difference between a response that successfully follows a set of complex instructions and a response that is high-quality, but nevertheless makes some subtle mistakes. This is done by prompting LLMs to progressively evolve simple instructions to more complex instructions. When the complexity of an instruction is increased, the original successful response to the original instruction becomes a "hard negative" response for the new instruction, mostly meeting requirements of the new instruction, but barely missing one or two. By pairing a good response with such a hard negative response, and employing contrastive learning algorithms such as DPO, we improve language models' ability to follow complex instructions. Empirically, we observe that our method yields a 7B model that exceeds the complex instruction-following performance of current SOTA 7B models and is competitive even with open-source 70B models.

In-hand manipulation of tools using dexterous hands in real-world is an underexplored problem in the literature. In addition to more complex geometry and larger size of the tools compared to more commonly used objects like cubes or cylinders, task oriented in-hand tool manipulation involves many sub-tasks to be performed sequentially. This may involve reaching to the tool, picking it up, reorienting it in hand with or without regrasping to reach to a desired final grasp appropriate for the tool usage, and carrying the tool to the desired pose. Research on long-horizon manipulation using dexterous hands is rather limited and the existing work focus on learning the individual sub-tasks using a method like reinforcement learning (RL) and combine the policies for different subtasks to perform a long horizon task. However, in general a single method may not be the best for all the sub-tasks, and this can be more pronounced when dealing with multi-fingered hands manipulating objects with complex geometry like tools. In this paper, we investigate the use of a mixed-method approach to solve for the long-horizon task of tool usage and we use imitation learning, reinforcement learning and model based control. We also discuss a new RL-based teacher-student framework that combines real world data into offline training. We show that our proposed approach for each subtask outperforms the commonly adopted reinforcement learning approach across different subtasks and in performing the long horizon task in simulation. Finally we show the successful transferability to real world.

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models excessive dependence on the low-level attributes used to train them, which reduces the models portability. To solve this issue, the use of high level attributes with more semantic meaning, such as ontologies, may be very useful. Along this line, we propose the utilization of an ontology that uses a taxonomy of actions that summarises students interactions with the Moodle learning management system. We compare the results of this proposed approach against our previous results when we used low-level raw attributes obtained directly from Moodle logs. The results indicate that the use of the proposed ontology improves the portability of the models in terms of predictive accuracy. The main contribution of this paper is to show that the ontological models obtained in one source course can be applied to other different target courses with similar usage levels without losing prediction accuracy.

In this work, we aim to simultaneously enhance the effectiveness and efficiency of Mixture-of-Experts (MoE) methods. To achieve this, we propose MoE++, a general and heterogeneous MoE framework that integrates both Feed-Forward Network (FFN) and zero-computation experts. Specifically, we introduce three types of zero-computation experts: the zero expert, copy expert, and constant expert, which correspond to discard, skip, and replace operations, respectively. This design offers three key advantages: (i) Low Computing Overhead: Unlike the uniform mixing mechanism for all tokens within vanilla MoE, MoE++ allows each token to engage with a dynamic number of FFNs, be adjusted by constant vectors, or even skip the MoE layer entirely. Moreover, we leverage gating residuals, enabling each token to consider the pathway taken in the previous layer when selecting the appropriate experts. Extensive experimental results demonstrate that MoE++ achieves better performance while delivering 1.1 2.1 expert forward throughput Large Language Models (LLMs) (Brown et al., 2020; OpenAI, 2022; Ouyang et al., 2022; Chowdhery et al., 2023; Achiam et al., 2023) have achieved substantial advancements, primarily attributed to the expansion of training data and a significant increase in model parameters. Therefore, the Mixtureof-Experts (MoE) architecture (Jacobs et al., 1991; Zhou et al., 2022; Roller et al., 2021), which allows for parameter scaling while keeping computational costs manageable, has become a preferred solution. The recent incorporation of MoE architectures into Transformers (Vaswani et al., 2017) has enabled the effective scaling of language models to impressive sizes, resulting in exceptional performance (Team, 2024; Dai et al., 2024; Jiang et al., 2024; Shen et al., 2024; Wei et al., 2024). These achievements underscore the significant potential and promise of MoE language models. Most existing Mixture-of-Experts (MoE) methods (Du et al., 2022; Fedus et al., 2022; Lewis et al., 2021; Rajbhandari et al., 2022) typically activate a fixed number of Feed-Forward Networks (FFNs) for all tokens. In many works (Lepikhin et al., 2021; Xue et al., 2024), each token selects the top We define expert throughput as the throughput of FFN experts and zero-computation experts (if present).

--Decentralized Federated Learning has emerged as an alternative to centralized architectures due to its faster training, privacy preservation, and reduced communication overhead. In decentralized communication, the server aggregation phase in Centralized Federated Learning shifts to the client side, which means that clients connect with each other in a peer-to-peer manner . However, compared to the centralized mode, data heterogeneity in Decentralized Federated Learning will cause larger variances between aggregated models, which leads to slow convergence in training and poor generalization performance in tests. T o address these issues, we introduce Catalyst Acceleration and propose an acceleration Decentralized Federated Learning algorithm called DFedCata. It consists of two main components: the Moreau envelope function, which primarily addresses parameter inconsistencies among clients caused by data heterogeneity, and Nesterov's extrapolation step, which accelerates the aggregation phase. Theoretically, We prove the optimization error bound and generalization error bound of the algorithm, providing a further understanding of the nature of the algorithm and the theoretical perspectives on the hyperparameter choice. Empirically, we demonstrate the advantages of the proposed algorithm in both convergence speed and generalization performance on CIF AR10/100 with various non-iid data distributions. Furthermore, we also experimentally verify the theoretical properties of DFedCata. EDERA TED Learning (FL) is a new distributed machine learning paradigm that prioritizes privacy protection [1]- [3]. It enables multiple clients to collaborate on training models without sharing their raw data. Nowadays, much of the research [4]-[9] focus on Centralized Federated Learning (CFL), but the central server in CFL brings various challenges on communication burden, single point of failure [10], privacy breaches [11] and so on. In contrast, Decentralized Federated Learning (DFL) centralizes both the local update and aggregation steps on the client, which offers enhanced privacy protection [12], faster model training [13], and robustness to slow client devices [14]. Therefore, DFL has become a popular alternative solution [10], [13]. Qinglun Li, Miao Zhang, and Quanjun Yin are with the College of Systems Engineering, National University of Defense Technology. Yingqi Liu, Li Shen, and Xiaochun Cao are with the School of Cy-ber Science and Technology, Shenzhen Campus of Sun Y at-sen University, Shenzhen 518107, China. The optimization process diagrams for two clients under the DFedAvg and DFedCata algorithms are simulated. The primary improvements include two aspects.

Syllables are compositional units of spoken language that play a crucial role in human speech perception and production. However, current neural speech representations lack structure, resulting in dense token sequences that are costly to process. To bridge this gap, we propose a new model, Sylber, that produces speech representations with clean and robust syllabic structure. Specifically, we propose a self-supervised model that regresses features on syllabic segments distilled from a teacher model which is an exponential moving average of the model in training. This results in a highly structured representation of speech features, offering three key benefits: 1) a fast, linear-time syllable segmentation algorithm, 2) efficient syllabic tokenization with an average of 4.27 tokens per second, and 3) syllabic units better suited for lexical and syntactic understanding. We also train token-to-speech generative models with our syllabic units and show that fully intelligible speech can be reconstructed from these tokens. Lastly, we observe that categorical perception, a linguistic phenomenon of speech perception, emerges naturally in our model, making the embedding space more categorical and sparse than previous self-supervised learning approaches. Together, we present a novel self-supervised approach for representing speech as syllables, with significant potential for efficient speech tokenization and spoken language modeling.

The use of small language models (SLMs), herein defined as models with less than three billion parameters, is increasing across various domains and applications. Due to their ability to run on more accessible hardware and preserve user privacy, SLMs possess the potential to democratize access to language models for individuals of different socioeconomic status and with different privacy preferences. This study assesses several state-of-the-art SLMs (e.g., Apple's OpenELM, Microsoft's Phi, Google's Gemma, and the Tinyllama project) for use in the financial domain to support the development of financial literacy LMs. Democratizing access to quality financial information for those who are financially under educated is greatly needed in society, particularly as new financial markets and products emerge and participation in financial markets increases due to ease of access. We are the first to examine the use of open-source SLMs to democratize access to financial question answering capabilities for individuals and students. To this end, we provide an analysis of the memory usage, inference time, similarity comparisons to ground-truth answers, and output readability of prominent SLMs to determine which models are most accessible and capable of supporting access to financial information. We analyze zero-shot and few-shot learning variants of the models. The results suggest that some off-the-shelf SLMs merit further exploration and fine-tuning to prepare them for individual use, while others may have limits to their democratization.