OpenRaiser/Intern-Atlas huggingface dataset:
Decentralized Federated Learning Over Imperfect Communication Channels
Venue: arXiv cs.DC 2024
Year: 2024

This paper analyzes the impact of imperfect communication channels on decentralized federated learning (D-FL) and subsequently determines the optimal number of local aggregations per training round, adapting to the network topology and imperfect channels. We start by deriving the bias of locally aggregated D-FL models under imperfect channels from the ideal global models requiring perfect channels and aggregations. The bias reveals that excessive local aggregations can accumulate communication errors and degrade convergence. Another important aspect is that we analyze a convergence upper bound of D-FL based on the bias. By minimizing the bound, the optimal number of local aggregations is identified to balance a trade-off with accumulation of communication errors in the absence of knowledge of the channels. With this knowledge, the impact of communication errors can be alleviated, allowing the convergence upper bound to decrease throughout aggregations. Experiments validate our convergence analysis and also identify the optimal number of local aggregations on two widely considered image classification tasks. It is seen that D-FL, with an optimal number of local aggregations, can outperform its potential alternatives by over 10% in training accuracy.
Adversarial DPO: Harnessing Harmful Data for Reducing Toxicity with Minimal Impact on Coherence and Evasiveness in Dialogue Agents
Venue: arXiv cs.CL 2024
Year: 2024

Recent advancements in open-domain dialogue systems have been propelled by the emergence of high-quality large language models (LLMs) and various effective training methodologies. Nevertheless, the presence of toxicity within these models presents a significant challenge that can potentially diminish the user experience. In this study, we introduce an innovative training algorithm, an improvement upon direct preference optimization (DPO), called adversarial DPO (ADPO). The ADPO algorithm is designed to train models to assign higher probability distributions to preferred responses and lower distributions to unsafe responses, which are self-generated using the toxic control token. We demonstrate that ADPO enhances the model's resilience against harmful conversations while minimizing performance degradation. Furthermore, we illustrate that ADPO offers a more stable training procedure compared to the traditional DPO. To the best of our knowledge, this is the first adaptation of the DPO algorithm that directly incorporates harmful data into the generative model, thereby reducing the need to artificially create safe dialogue data.
Streamlining Software Reviews: Efficient Predictive Modeling with Minimal Examples
Venue: arXiv cs.SE 2024
Year: 2024

This paper proposes a new challenge problem for software analytics. In the process we shall call "software review", a panel of SMEs (subject matter experts) review examples of software behavior to recommend how to improve that's software's operation. SME time is usually extremely limited so, ideally, this panel can complete this optimization task after looking at just a small number of very informative, examples. To support this review process, we explore methods that train a predictive model to guess if some oracle will like/dislike the next example. Such a predictive model can work with the SMEs to guide them in their exploration of all the examples. Also, after the panelists leave, that model can be used as an oracle in place of the panel (to handle new examples, while the panelists are busy, elsewhere). In 31 case studies (ranging from from high-level decisions about software processes to low-level decisions about how to configure video encoding software), we show that such predictive models can be built using as few as 12 to 30 labels. To the best of our knowledge, this paper's success with only a handful of examples (and no large language model) is unprecedented. In accordance with the principles of open science, we offer all our code and data at https://github.com/timm/ez/tree/Stable-EMSE-paper so that others can repeat/refute/improve these results.
Panmodal Information Interaction
Venue: arXiv cs.IR 2024
Year: 2024

The emergence of generative artificial intelligence (GenAI) is transforming information interaction. For decades, search engines such as Google and Bing have been the primary means of locating relevant information for the general population. They have provided search results in the same standard format (the so-called "10 blue links"). The recent ability to chat via natural language with AI-based agents and have GenAI automatically synthesize answers in real-time (grounded in top-ranked results) is changing how people interact with and consume information at massive scale. These two information interaction modalities (traditional search and AI-powered chat) coexist in current search engines, either loosely coupled (e.g., as separate options/tabs) or tightly coupled (e.g., integrated as a chat answer embedded directly within a traditional search result page). We believe that the existence of these two different modalities, and potentially many others, is creating an opportunity to re-imagine the search experience, capitalize on the strengths of many modalities, and develop systems and strategies to support seamless flow between them. We refer to these as panmodal experiences. Unlike monomodal experiences, where only one modality is available and/or used for the task at hand, panmodal experiences make multiple modalities available to users (multimodal), directly support transitions between modalities (crossmodal), and seamlessly combine modalities to tailor task assistance (transmodal). While our focus is search and chat, with learnings from insights from a survey of over 100 individuals who have recently performed common tasks on these two modalities, we also present a more general vision for the future of information interaction using multiple modalities and the emergent capabilities of GenAI.
Differential Walk on Spheres
Venue: arXiv cs.GR 2024
Year: 2024

We introduce a Monte Carlo method for computing derivatives of the solution to a partial differential equation (PDE) with respect to problem parameters (such as domain geometry or boundary conditions). Derivatives can be evaluated at arbitrary points, without performing a global solve or constructing a volumetric grid or mesh. The method is hence well suited to inverse problems with complex geometry, such as PDE-constrained shape optimization. Like other walk on spheres (WoS) algorithms, our method is trivial to parallelize, and is agnostic to boundary representation (meshes, splines, implicit surfaces, etc.), supporting large topological changes. We focus in particular on screened Poisson equations, which model diverse problems from scientific and geometric computing. As in differentiable rendering, we jointly estimate derivatives with respect to all parameters -- hence, cost does not grow significantly with parameter count. In practice, even noisy derivative estimates exhibit fast, stable convergence for stochastic gradient-based optimization, as we show through examples from thermal design, shape from diffusion, and computer graphics.
DuetRAG: Collaborative Retrieval-Augmented Generation
Venue: arXiv cs.CL 2024
Year: 2024

Retrieval-Augmented Generation (RAG) methods augment the input of Large Language Models (LLMs) with relevant retrieved passages, reducing factual errors in knowledge-intensive tasks. However, contemporary RAG approaches suffer from irrelevant knowledge retrieval issues in complex domain questions (e.g., HotPot QA) due to the lack of corresponding domain knowledge, leading to low-quality generations. To address this issue, we propose a novel Collaborative Retrieval-Augmented Generation framework, DuetRAG. Our bootstrapping philosophy is to simultaneously integrate the domain fintuning and RAG models to improve the knowledge retrieval quality, thereby enhancing generation quality. Finally, we demonstrate DuetRAG' s matches with expert human researchers on HotPot QA.
UCCIX: Irish-eXcellence Large Language Model
Venue: arXiv cs.CL 2024
Year: 2024

The development of Large Language Models (LLMs) has predominantly focused on high-resource languages, leaving extremely low-resource languages like Irish with limited representation. This work presents UCCIX, a pioneering effort on the development of an open-source Irish-based LLM. We propose a novel framework for continued pre-training of LLMs specifically adapted for extremely low-resource languages, requiring only a fraction of the textual data typically needed for training LLMs according to scaling laws. Our model, based on Llama 2-13B, outperforms much larger models on Irish language tasks with up to 12% performance improvement, showcasing the effectiveness and efficiency of our approach. We also contribute comprehensive Irish benchmarking datasets, including IrishQA, a question-answering dataset, and Irish version of MT-bench. These datasets enable rigorous evaluation and facilitate future research in Irish LLM systems. Our work aims to preserve and promote the Irish language, knowledge, and culture of Ireland in the digital era while providing a framework for adapting LLMs to other indigenous languages.
A survey on fairness of large language models in e-commerce: progress, application, and challenge
Venue: arXiv cs.CL 2024
Year: 2024

This survey explores the fairness of large language models (LLMs) in e-commerce, examining their progress, applications, and the challenges they face. LLMs have become pivotal in the e-commerce domain, offering innovative solutions and enhancing customer experiences. This work presents a comprehensive survey on the applications and challenges of LLMs in e-commerce. The paper begins by introducing the key principles underlying the use of LLMs in e-commerce, detailing the processes of pretraining, fine-tuning, and prompting that tailor these models to specific needs. It then explores the varied applications of LLMs in e-commerce, including product reviews, where they synthesize and analyze customer feedback; product recommendations, where they leverage consumer data to suggest relevant items; product information translation, enhancing global accessibility; and product question and answer sections, where they automate customer support. The paper critically addresses the fairness challenges in e-commerce, highlighting how biases in training data and algorithms can lead to unfair outcomes, such as reinforcing stereotypes or discriminating against certain groups. These issues not only undermine consumer trust, but also raise ethical and legal concerns. Finally, the work outlines future research directions, emphasizing the need for more equitable and transparent LLMs in e-commerce. It advocates for ongoing efforts to mitigate biases and improve the fairness of these systems, ensuring they serve diverse global markets effectively and ethically. Through this comprehensive analysis, the survey provides a holistic view of the current landscape of LLMs in e-commerce, offering insights into their potential and limitations, and guiding future endeavors in creating fairer and more inclusive e-commerce environments.
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text
Venue: arXiv cs.CL 2024
Year: 2024

Anomaly detection (AD) is a fast growing and popular domain among established applications like vision and time series. We observe a rich literature for these applications, but anomaly detection in text is only starting to blossom. Recently, self-supervised methods with self-attention mechanism have been the most popular choice. While recent works have proposed a working ground for building and benchmarking state of the art approaches, we propose two principal contributions in this paper: contextual anomaly contamination and a novel ensemble-based approach. Our method, Textual Anomaly Contamination (TAC), allows to contaminate inlier classes with either independent or contextual anomalies. In the literature, it appears that this distinction is not performed. For finding contextual anomalies, we propose RoSAE, a Robust Subspace Local Recovery Autoencoder Ensemble. All autoencoders of the ensemble present a different latent representation through local manifold learning. Benchmark shows that our approach outperforms recent works on both independent and contextual anomalies, while being more robust. We also provide 8 dataset comparison instead of only relying to Reuters and 20 Newsgroups corpora.
Human-Generative AI Collaborative Problem Solving Who Leads and How Students Perceive the Interactions
Venue: arXiv cs.HC 2024
Year: 2024

This research investigates distinct human-generative AI collaboration types and students' interaction experiences when collaborating with generative AI (i.e., ChatGPT) for problem-solving tasks and how these factors relate to students' sense of agency and perceived collaborative problem solving. By analyzing the surveys and reflections of 79 undergraduate students, we identified three human-generative AI collaboration types: even contribution, human leads, and AI leads. Notably, our study shows that 77.21% of students perceived they led or had even contributed to collaborative problem-solving when collaborating with ChatGPT. On the other hand, 15.19% of the human participants indicated that the collaborations were led by ChatGPT, indicating a potential tendency for students to rely on ChatGPT. Furthermore, 67.09% of students perceived their interaction experiences with ChatGPT to be positive or mixed. We also found a positive correlation between positive interaction experience and a sense of positive agency. The results of this study contribute to our understanding of the collaboration between students and generative AI and highlight the need to study further why some students let ChatGPT lead collaborative problem-solving and how to enhance their interaction experience through curriculum and technology design.
SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations
Venue: arXiv cs.CL 2024
Year: 2024

The ability to understand emotions is an essential component of human-like artificial intelligence, as emotions greatly influence human cognition, decision making, and social interactions. In addition to emotion recognition in conversations, the task of identifying the potential causes behind an individual's emotional state in conversations, is of great importance in many application scenarios. We organize SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, which aims at extracting all pairs of emotions and their corresponding causes from conversations. Under different modality settings, it consists of two subtasks: Textual Emotion-Cause Pair Extraction in Conversations (TECPE) and Multimodal Emotion-Cause Pair Extraction in Conversations (MECPE). The shared task has attracted 143 registrations and 216 successful submissions. In this paper, we introduce the task, dataset and evaluation settings, summarize the systems of the top teams, and discuss the findings of the participants.
Towards Contactless Elevators with TinyML using CNN-based Person Detection and Keyword Spotting
Venue: arXiv cs.HC 2024
Year: 2024

This study presents a proof of concept for a contactless elevator operation system aimed at minimizing human intervention while enhancing safety, intelligence, and efficiency. A microcontroller-based edge device executing tiny Machine Learning (tinyML) inferences is developed for elevator operation. Using person detection and keyword spotting algorithms, the system offers cost-effective and robust units requiring minimal infrastructural changes. The design incorporates preprocessing steps and quantized convolutional neural networks in a multitenant framework to optimize accuracy and response time. Results show a person detection accuracy of 83.34% and keyword spotting efficacy of 80.5%, with an overall latency under 5 seconds, indicating effectiveness in real-world scenarios. Unlike current high-cost and inconsistent contactless technologies, this system leverages tinyML to provide a cost-effective, reliable, and scalable solution, enhancing user safety and operational efficiency without significant infrastructural changes. The study highlights promising results, though further exploration is needed for scalability and integration with existing systems. The demonstrated energy efficiency, simplicity, and safety benefits suggest that tinyML adoption could revolutionize elevator systems, serving as a model for future technological advancements. This technology could significantly impact public health and convenience in multi-floor buildings by reducing physical contact and improving operational efficiency, particularly relevant in the context of pandemics or hygiene concerns.
Digital Health and Indoor Air Quality: An IoT-Driven Human-Centred Visualisation Platform for Behavioural Change and Technology Acceptance
Venue: arXiv cs.HC 2024
Year: 2024

The detrimental effects of air pollutants on human health have prompted increasing concerns regarding indoor air quality (IAQ). The emergence of digital health interventions and citizen science initiatives has provided new avenues for raising awareness, improving IAQ, and promoting behavioural changes. The Technology Acceptance Model (TAM) offers a theoretical framework to understand user acceptance and adoption of IAQ technology. This paper presents a case study using the COM-B model and Internet of Things (IoT) technology to design a human-centred digital visualisation platform, leading to behavioural changes and improved IAQ. The study also investigates users' acceptance and adoption of the technology, focusing on their experiences, expectations, and the impact on IAQ. Integrating IAQ sensing, digital health-related interventions, citizen science, and the TAM model offers opportunities to address IAQ challenges, enhance public health, and foster sustainable indoor environments. The analytical results show that factors such as human behaviour, indoor activities, and awareness play crucial roles in shaping IAQ.
ReALLM: A general framework for LLM compression and fine-tuning
Venue: arXiv cs.LG 2024
Year: 2024

We introduce ReALLM, a novel approach for compression and memory-efficient adaptation of pre-trained language models that encompasses most of the post-training quantization and fine-tuning methods for a budget of <4 bits. Pre-trained matrices are decomposed into a high-precision low-rank component and a vector-quantized latent representation (using an autoencoder). During the fine-tuning step, only the low-rank components are updated. Our results show that pre-trained matrices exhibit different patterns. ReALLM adapts the shape of the encoder (small/large embedding, high/low bit VQ, etc.) to each matrix. ReALLM proposes to represent each matrix with a small embedding on $b$ bits and a neural decoder model $\mathcal{D}_ϕ$ with its weights on $b_ϕ$ bits. The decompression of a matrix requires only one embedding and a single forward pass with the decoder. Our weight-only quantization algorithm yields the best results on language generation tasks (C4 and WikiText-2) for a budget of $3$ bits without any training. With a budget of $2$ bits, ReALLM achieves state-of-the art performance after fine-tuning on a small calibration dataset.
FEATHER: A Reconfigurable Accelerator with Data Reordering Support for Low-Cost On-Chip Dataflow Switching
Venue: arXiv cs.AR 2024
Year: 2024

The inference of ML models composed of diverse structures, types, and sizes boils down to the execution of different dataflows (i.e. different tiling, ordering, parallelism, and shapes). Using the optimal dataflow for every layer of workload can reduce latency by up to two orders of magnitude over a suboptimal dataflow. Unfortunately, reconfiguring hardware for different dataflows involves on-chip data layout reordering and datapath reconfigurations, leading to non-trivial overhead that hinders ML accelerators from exploiting different dataflows, resulting in suboptimal performance. To address this challenge, we propose FEATHER, an innovative accelerator that leverages a novel spatial array termed Nest and a novel multi-stage reduction network called BIRRD for performing flexible data reduction with layout reordering under the hood, enabling seamless switching between optimal dataflows with negligible latency and resources overhead. For systematically evaluating the performance interaction between dataflows and layouts, we enhance Timeloop, a state-of-the-art dataflow cost modeling and search framework, with layout assessment capabilities, and term it as Layoutloop. We model FEATHER into Layoutloop and also deploy FEATHER end-to-end on the edge ZCU104 FPGA. FEATHER delivers 1.27~2.89x inference latency speedup and 1.3~6.43x energy efficiency improvement compared to various SoTAs like NVDLA, SIGMA and Eyeriss under ResNet-50 and MobiletNet-V3 in Layoutloop. On practical FPGA devices, FEATHER achieves 2.65/3.91x higher throughput than Xilinx DPU/Gemmini. Remarkably, such performance and energy efficiency enhancements come at only 6% area over a fixed-dataflow Eyeriss-like accelerator. Our code is released at https://github.com/maeri-project/FEATHER.
A Workbench for Autograding Retrieve/Generate Systems
Venue: arXiv cs.IR 2024
Year: 2024

This resource paper addresses the challenge of evaluating Information Retrieval (IR) systems in the era of autoregressive Large Language Models (LLMs). Traditional methods relying on passage-level judgments are no longer effective due to the diversity of responses generated by LLM-based systems. We provide a workbench to explore several alternative evaluation approaches to judge the relevance of a system's response that incorporate LLMs: 1. Asking an LLM whether the response is relevant; 2. Asking the LLM which set of nuggets (i.e., relevant key facts) is covered in the response; 3. Asking the LLM to answer a set of exam questions with the response. This workbench aims to facilitate the development of new, reusable test collections. Researchers can manually refine sets of nuggets and exam questions, observing their impact on system evaluation and leaderboard rankings. Resource available at https://github.com/TREMA-UNH/autograding-workbench
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
Venue: arXiv cs.CL 2024
Year: 2024

This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techniques. Our Retrieval Augmented Generation (RAG) solution, enhanced by a reranking method, utilizes multiple knowledge sources to ensure the precision and pertinence of lay summaries. Additionally, our Reinforcement Learning for Readability Control (RLRC) strategy improves readability, making scientific content comprehensible to non-specialists. Evaluations using the publicly accessible PLOS and eLife datasets show that our methods surpass Plain Gemini model, demonstrating a 20% increase in readability scores, a 15% improvement in ROUGE-2 relevance scores, and a 10% enhancement in factual accuracy. The RAG-RLRC-LaySum framework effectively democratizes scientific knowledge, enhancing public engagement with biomedical discoveries.