OpenRaiser/Intern-Atlas huggingface dataset:
InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct
Venue: arXiv cs.CL 2024
Year: 2024

Recent advancements in open-source code large language models (LLMs) have been driven by fine-tuning on the data generated from powerful closed-source LLMs, which are expensive to obtain. This paper explores whether it is possible to use a fine-tuned open-source model to generate additional data to augment its instruction-tuning dataset. We make two observations: (1) A code snippet can serve as the response to different instructions. (2) Instruction-tuned code LLMs perform better at translating code into instructions than the reverse. Based on these observations, we propose Inverse-Instruct, a data augmentation technique that uses a fine-tuned LLM to generate additional instructions of code responses from its own training dataset. The additional instruction-response pairs are added to the original dataset, and a stronger code LLM can be obtained by fine-tuning on the augmented dataset. We empirically validate Inverse-Instruct on a range of open-source code models (e.g. CodeLlama-Python and DeepSeek-Coder) and benchmarks (e.g., HumanEval(+), MBPP(+), DS-1000 and MultiPL-E), showing it consistently improves the base models.
Comparison of robot morphologies and base positioning for welding applications
Venue: arXiv cs.RO 2024
Year: 2024

This article undertakes a comprehensive examination of two distinct robot morphologies: the PUMA-type arm (Programmable Universal Machine for Assembly) and the UR-type robot (Universal Robots). The primary aim of this comparative analysis is to assess their respective performances within the specialized domain of welding, focusing on predefined industrial application scenarios. These scenarios encompass a range of geometrical components earmarked for welding, along with specified welding paths, spatial constraints, and welding methodologies reflective of real-world scenarios encountered by manual welders. The case studies presented in this research serve as illustrative examples of Weez-U Welding practices, providing insights into the practical implications of employing different robot morphologies. Moreover, this study distinguishes between various base positions for the robot, thereby aiding welders in selecting the optimal base placement aligned with their specific welding objectives. By offering such insights, this research facilitates the selection of the most suitable architecture for this particular range of trajectories, thus optimizing welding efficiency and effectiveness. A departure from conventional methodologies, this study goes beyond merely considering singularities and also delves into the analysis of collisions between the robot and its environment, contingent upon the robot's posture. This holistic approach offers a more nuanced understanding of the challenges and considerations inherent in deploying robotic welding systems, providing valuable insights for practitioners and researchers alike in the field of robotic welding technology.
TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery
Venue: arXiv cs.AI 2024
Year: 2024

As the primary mRNA delivery vehicles, ionizable lipid nanoparticles (LNPs) exhibit excellent safety, high transfection efficiency, and strong immune response induction. However, the screening process for LNPs is time-consuming and costly. To expedite the identification of high-transfection-efficiency mRNA drug delivery systems, we propose an explainable LNPs transfection efficiency prediction model, called TransMA. TransMA employs a multi-modal molecular structure fusion architecture, wherein the fine-grained atomic spatial relationship extractor named molecule 3D Transformer captures three-dimensional spatial features of the molecule, and the coarse-grained atomic sequence extractor named molecule Mamba captures one-dimensional molecular features. We design the mol-attention mechanism block, enabling it to align coarse and fine-grained atomic features and captures relationships between atomic spatial and sequential structures. TransMA achieves state-of-the-art performance in predicting transfection efficiency using the scaffold and cliff data splitting methods on the current largest LNPs dataset, including Hela and RAW cell lines. Moreover, we find that TransMA captures the relationship between subtle structural changes and significant transfection efficiency variations, providing valuable insights for LNPs design. Additionally, TransMA's predictions on external transfection efficiency data maintain a consistent order with actual transfection efficiencies, demonstrating its robust generalization capability. The code, model and data are made publicly available at https://github.com/wklix/TransMA/tree/master. We hope that high-accuracy transfection prediction models in the future can aid in LNPs design and initial screening, thereby assisting in accelerating the mRNA design process.
Do Multilingual Large Language Models Mitigate Stereotype Bias?
Venue: arXiv cs.CL 2024
Year: 2024

While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to monolingual ones, a comprehensive understanding of the effect of multilingual training on bias mitigation, is lacking. This study addresses this gap by systematically training six LLMs of identical size (2.6B parameters) and architecture: five monolingual models (English, German, French, Italian, and Spanish) and one multilingual model trained on an equal distribution of data across these languages, all using publicly available data. To ensure robust evaluation, standard bias benchmarks were automatically translated into the five target languages and verified for both translation quality and bias preservation by human annotators. Our results consistently demonstrate that multilingual training effectively mitigates bias. Moreover, we observe that multilingual models achieve not only lower bias but also superior prediction accuracy when compared to monolingual models with the same amount of training data, model architecture, and size.
Multi-agent Reinforcement Learning-based Network Intrusion Detection System
Venue: arXiv cs.CR 2024
Year: 2024

Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. Machine learning has emerged as a popular approach for intrusion detection due to its ability to analyze and detect patterns in large volumes of data. However, current ML-based IDS solutions often struggle to keep pace with the ever-changing nature of attack patterns and the emergence of new attack types. Additionally, these solutions face challenges related to class imbalance, where the number of instances belonging to different classes (normal and intrusions) is significantly imbalanced, which hinders their ability to effectively detect minor classes. In this paper, we propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection. To enhance the capabilities of the proposed model, we have improved the DQN algorithm by implementing the weighted mean square loss function and employing cost-sensitive learning techniques. Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns. Experimental results realized using CIC-IDS-2017 dataset, demonstrate that our approach can effectively handle the class imbalance problem and provide a fine grained classification of attacks with a very low false positive rate. In comparison to the current state-of-the-art works, our solution demonstrates a significant superiority in both detection rate and false positive rate.
Hecaton: Training Large Language Models with Scalable Chiplet Systems
Venue: arXiv cs.AR 2024
Year: 2024

Large Language Models (LLMs) have achieved remarkable success in various fields, but their training and finetuning require massive computation and memory, necessitating parallelism which introduces heavy communication overheads. Driven by advances in packaging, the chiplet architecture emerges as a potential solution, as it can integrate computing power, as well as utilize on-package links with better signal integrity, higher bandwidth, and lower energy consumption. However, most existing chiplet-related works focus on DNN inference. Directly porting them to LLM training introduces significantly large quantities of DRAM access and network-on-package (NoP) overheads which make state-of-the-art chiplet designs fail, highlighting a research gap. This work proposes Hecaton, a scalable and cost-effective chiplet system for LLM training. We first provide a chiplet architecture with tailored scheduling that can largely reduce DRAM accesses. We further design an efficient distributed training method that reduces NoP communication complexity and relieves constraints on SRAM capacity and layout. Theoretical analysis shows that the entire system achieves weak scaling: as the workload and hardware resources grow proportionally, the computation-to-communication ratio remains nearly constant. Experiments with various workloads and hardware configurations verify the property, and Hecaton achieves $5.29\times$ performance improvement and $3.46\times$ energy reduction on Llama3.1-405B, compared to the tensor parallelism in Megatron. To the best of our knowledge, we propose the first chiplet architecture specifically used for LLM training or finetuning, with guaranteed performance regardless of the problem scale.
Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets
Venue: arXiv cs.AI 2024
Year: 2024

In imperfect information games, the evaluation of a game state not only depends on the observable world but also relies on hidden parts of the environment. As accessing the obstructed information trivialises state evaluations, one approach to tackle such problems is to estimate the value of the imperfect state as a combination of all states in the information set, i.e., all possible states that are consistent with the current imperfect information. In this work, the goal is to learn a function that maps from the imperfect game information state to its expected value. However, constructing a perfect training set, i.e. an enumeration of the whole information set for numerous imperfect states, is often infeasible. To compute the expected values for an imperfect information game like \textit{Reconnaissance Blind Chess}, one would need to evaluate thousands of chess positions just to obtain the training target for a single state. Still, the expected value of a state can already be approximated with appropriate accuracy from a much smaller set of evaluations. Thus, in this paper, we empirically investigate how a budget of perfect information game evaluations should be distributed among training samples to maximise the return. Our results show that sampling a small number of states, in our experiments roughly 3, for a larger number of separate positions is preferable over repeatedly sampling a smaller quantity of states. Thus, we find that in our case, the quantity of different samples seems to be more important than higher target quality.
Multi-clue Consistency Learning to Bridge Gaps Between General and Oriented Object in Semi-supervised Detection
Venue: arXiv cs.CV 2024
Year: 2024

While existing semi-supervised object detection (SSOD) methods perform well in general scenes, they encounter challenges in handling oriented objects in aerial images. We experimentally find three gaps between general and oriented object detection in semi-supervised learning: 1) Sampling inconsistency: the common center sampling is not suitable for oriented objects with larger aspect ratios when selecting positive labels from labeled data. 2) Assignment inconsistency: balancing the precision and localization quality of oriented pseudo-boxes poses greater challenges which introduces more noise when selecting positive labels from unlabeled data. 3) Confidence inconsistency: there exists more mismatch between the predicted classification and localization qualities when considering oriented objects, affecting the selection of pseudo-labels. Therefore, we propose a Multi-clue Consistency Learning (MCL) framework to bridge gaps between general and oriented objects in semi-supervised detection. Specifically, considering various shapes of rotated objects, the Gaussian Center Assignment is specially designed to select the pixel-level positive labels from labeled data. We then introduce the Scale-aware Label Assignment to select pixel-level pseudo-labels instead of unreliable pseudo-boxes, which is a divide-and-rule strategy suited for objects with various scales. The Consistent Confidence Soft Label is adopted to further boost the detector by maintaining the alignment of the predicted results. Comprehensive experiments on DOTA-v1.5 and DOTA-v1.0 benchmarks demonstrate that our proposed MCL can achieve state-of-the-art performance in the semi-supervised oriented object detection task.
Towards Optimizing and Evaluating a Retrieval Augmented QA Chatbot using LLMs with Human in the Loop
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models have found application in various mundane and repetitive tasks including Human Resource (HR) support. We worked with the domain experts of SAP SE to develop an HR support chatbot as an efficient and effective tool for addressing employee inquiries. We inserted a human-in-the-loop in various parts of the development cycles such as dataset collection, prompt optimization, and evaluation of generated output. By enhancing the LLM-driven chatbot's response quality and exploring alternative retrieval methods, we have created an efficient, scalable, and flexible tool for HR professionals to address employee inquiries effectively. Our experiments and evaluation conclude that GPT-4 outperforms other models and can overcome inconsistencies in data through internal reasoning capabilities. Additionally, through expert analysis, we infer that reference-free evaluation metrics such as G-Eval and Prometheus demonstrate reliability closely aligned with that of human evaluation.
Dynamic single-input control of multi-state multi-transition soft robotic actuator
Venue: arXiv cs.RO 2024
Year: 2024

Soft robotics is an attractive and rapidly emerging field, in which actuation is coupled with the elastic response of the robot's structure to achieve complex deformation patterns. A crucial challenge is the need for multiple control inputs, which adds significant complication to the system. We propose a novel concept of single-input control of an actuator composed of interconnected bi-stable elements. Dynamic response of the actuator and pre-designed differences between the elements are exploited to facilitate any desired multi-state transition, using a single dynamic input. We show formulation and analysis of the control system's dynamics and pre-design of its multiple equilibrium states, as well as their stability. Then we fabricate and demonstrate experimentally on single-input control of two- and four-element actuators, where the latter can achieve transitions between up to 48 desired states. Our work paves the way for next-generation soft robotic actuators with minimal actuation and maximal dexterity.
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
Venue: arXiv cs.LG 2024
Year: 2024

The robustness of supervised deep learning-based medical image classification is significantly undermined by label noise. Although several methods have been proposed to enhance classification performance in the presence of noisy labels, they face some challenges: 1) a struggle with class-imbalanced datasets, leading to the frequent overlooking of minority classes as noisy samples; 2) a singular focus on maximizing performance using noisy datasets, without incorporating experts-in-the-loop for actively cleaning the noisy labels. To mitigate these challenges, we propose a two-phase approach that combines Learning with Noisy Labels (LNL) and active learning. This approach not only improves the robustness of medical image classification in the presence of noisy labels, but also iteratively improves the quality of the dataset by relabeling the important incorrect labels, under a limited annotation budget. Furthermore, we introduce a novel Variance of Gradients approach in LNL phase, which complements the loss-based sample selection by also sampling under-represented samples. Using two imbalanced noisy medical classification datasets, we demonstrate that that our proposed technique is superior to its predecessors at handling class imbalance by not misidentifying clean samples from minority classes as mostly noisy samples.
Exploring Human-LLM Conversations: Mental Models and the Originator of Toxicity
Venue: arXiv cs.HC 2024
Year: 2024

This study explores real-world human interactions with large language models (LLMs) in diverse, unconstrained settings in contrast to most prior research focusing on ethically trimmed models like ChatGPT for specific tasks. We aim to understand the originator of toxicity. Our findings show that although LLMs are rightfully accused of providing toxic content, it is mostly demanded or at least provoked by humans who actively seek such content. Our manual analysis of hundreds of conversations judged as toxic by APIs commercial vendors, also raises questions with respect to current practices of what user requests are refused to answer. Furthermore, we conjecture based on multiple empirical indicators that humans exhibit a change of their mental model, switching from the mindset of interacting with a machine more towards interacting with a human.
PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
Venue: arXiv cs.CL 2024
Year: 2024

In recent years, the rise of Large Language Models (LLMs) has spurred a growing demand for plug-and-play AI systems. Among the various AI techniques, prompt engineering stands out as particularly significant. However, users often face challenges in writing prompts due to the steep learning curve and significant time investment, and existing automatic prompt engineering (APE) models can be difficult to use. To address this issue, we propose PAS, an LLM-based plug-and-play APE system. PAS utilizes LLMs trained on high-quality, automatically generated prompt complementary datasets, resulting in exceptional performance. In comprehensive benchmarks, PAS achieves state-of-the-art (SoTA) results compared to previous APE models, with an average improvement of 6.09 points. Moreover, PAS is highly efficient, achieving SoTA performance with only 9000 data points. Additionally, PAS can autonomously generate prompt augmentation data without requiring additional human labor. Its flexibility also allows it to be compatible with all existing LLMs and applicable to a wide range of tasks. PAS excels in human evaluations, underscoring its suitability as a plug-in for users. This combination of high performance, efficiency, and flexibility makes PAS a valuable system for enhancing the usability and effectiveness of LLMs through improved prompt engineering.
Enabling Performant and Secure EDA as a Service in Public Clouds Using Confidential Containers
Venue: arXiv cs.CR 2024
Year: 2024

Increasingly, business opportunities available to fabless design teams in the semiconductor industry far exceed those addressable with on-prem compute resources. An attractive option to capture these electronic design automation (EDA) design opportunities is through public cloud bursting. However, security concerns with public cloud bursting arise from having to protect process design kits, third party intellectual property, and new design data for semiconductor devices and chips. One way to address security concerns for public cloud bursting is to leverage confidential containers for EDA workloads. Confidential containers add zero trust computing elements to significantly reduce the probability of intellectual property escapes. A key concern that often follows security discussions is whether EDA workload performance will suffer with confidential computing. In this work we demonstrate a full set of EDA confidential containers and their deployment and characterize performance impacts of confidential elements of the flow including storage and networking. A complete end-to-end confidential container-based EDA workload exhibits 7.13% and 2.05% performance overheads over bare-metal container and VM based solutions, respectively.
Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator
Venue: arXiv cs.LG 2024
Year: 2024

Modeling thermal states for complex space missions, such as the surface exploration of airless bodies, requires high computation, whether used in ground-based analysis for spacecraft design or during onboard reasoning for autonomous operations. For example, a finite-element thermal model with hundreds of elements can take significant time to simulate, which makes it unsuitable for onboard reasoning during time-sensitive scenarios such as descent and landing, proximity operations, or in-space assembly. Further, the lack of fast and accurate thermal modeling drives thermal designs to be more conservative and leads to spacecraft with larger mass and higher power budgets. The emerging paradigm of physics-informed machine learning (PIML) presents a class of hybrid modeling architectures that address this challenge by combining simplified physics models with machine learning (ML) models resulting in models which maintain both interpretability and robustness. Such techniques enable designs with reduced mass and power through onboard thermal-state estimation and control and may lead to improved onboard handling of off-nominal states, including unplanned down-time. The PIML model or hybrid model presented here consists of a neural network which predicts reduced nodalizations (distribution and size of coarse mesh) given on-orbit thermal load conditions, and subsequently a (relatively coarse) finite-difference model operates on this mesh to predict thermal states. We compare the computational performance and accuracy of the hybrid model to a data-driven neural net model, and a high-fidelity finite-difference model of a prototype Earth-orbiting small spacecraft. The PIML based active nodalization approach provides significantly better generalization than the neural net model and coarse mesh model, while reducing computing cost by up to 1.7x compared to the high-fidelity model.
Temporal Grounding of Activities using Multimodal Large Language Models
Venue: arXiv cs.CV 2024
Year: 2024

Temporal grounding of activities, the identification of specific time intervals of actions within a larger event context, is a critical task in video understanding. Recent advancements in multimodal large language models (LLMs) offer new opportunities for enhancing temporal reasoning capabilities. In this paper, we evaluate the effectiveness of combining image-based and text-based large language models (LLMs) in a two-stage approach for temporal activity localization. We demonstrate that our method outperforms existing video-based LLMs. Furthermore, we explore the impact of instruction-tuning on a smaller multimodal LLM, showing that refining its ability to process action queries leads to more expressive and informative outputs, thereby enhancing its performance in identifying specific time intervals of activities. Our experimental results on the Charades-STA dataset highlight the potential of this approach in advancing the field of temporal activity localization and video understanding.