OpenRaiser/Intern-Atlas huggingface dataset:
Cube2Pipes : Investigating Hybrid Gameplay Using AR and a Tangible 3D Puzzle
Venue: arXiv cs.HC 2024
Year: 2024

We present our game, Cube2Pipes, as an attempt to investigate a unique gameplay design where we use a tangible 3D spatial puzzle, in the form of a 2X2 Rubik's Cube, as an interface to a tabletop mobile augmented reality (AR) game. The game interface adapts to user movement and interaction with both virtual and tangible elements via computer vision based tracking. This game can be seen as an instance of generic interactive hybrid systems as it involves interaction with both virtual and real, tangible elements. We present a thorough user evaluation about various aspects of the gameplay in order to answer the question as to whether hybrid gameplay involving both real and virtual interfaces and elements is more captivating and preferred by users, than standard (baseline) gameplay with only virtual elements. We use multiple industry standard user study questionnaires to try and answer this question. We also try to determine whether the game facilitates understanding of the spatial moves required to solve a Rubik's Cube, and the efficacy of a tangible puzzle interface to a tabletop AR game.
Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning
Venue: arXiv cs.LG 2024
Year: 2024

We propose C-LAIfO, a computationally efficient algorithm designed for imitation learning from videos in the presence of visual mismatch between agent and expert domains. We analyze the problem of imitation from expert videos with visual discrepancies, and introduce a solution for robust latent space estimation using contrastive learning and data augmentation. Provided a visually robust latent space, our algorithm performs imitation entirely within this space using off-policy adversarial imitation learning. We conduct a thorough ablation study to justify our design and test C-LAIfO on high-dimensional continuous robotic tasks. Additionally, we demonstrate how C-LAIfO can be combined with other reward signals to facilitate learning on a set of challenging hand manipulation tasks with sparse rewards. Our experiments show improved performance compared to baseline methods, highlighting the effectiveness of C-LAIfO. To ensure reproducibility, we open source our code.
The need of a self for self-driving cars a theoretical model applying homeostasis to self driving
Venue: arXiv cs.HC 2024
Year: 2024

This paper explores the concept of creating a "self" for self-driving cars through a homeostatic architecture designed to enhance their autonomy, safety, and efficiency. The proposed system integrates inward focused sensors to monitor the car's internal state, such as the condition of its metal bodywork, wheels, engine, and battery, establishing a baseline homeostatic state representing optimal functionality. Outward facing sensors, like cameras and LIDAR, are then interpreted via their impact on the car's homeostatic state by quantifying deviations from homeostasis. This contrasts with the approach of trying to make cars "see" reality in a similar way to humans and identify elements in their reality in the same way humans. Virtual environments would be leveraged to accelerate training. Additionally, cars are programmed to communicate and share experiences via blockchain technology, learning from each other's mistakes while maintaining individualized training models. A dedicated language for self-driving cars is proposed to enable nuanced interpretation and response to environmental data. This architecture allows self-driving cars to dynamically adjust their behavior based on internal and external feedback, promoting cooperation and continuous improvement. The study concludes by discussing the broader implications for AI development, potential real-world applications, and future research directions.
Evaluation of LLMs Biases Towards Elite Universities: A Persona-Based Exploration
Venue: arXiv cs.CY 2024
Year: 2024

This study investigates whether popular LLMs exhibit bias towards elite universities when generating personas for technology industry professionals. We employed a novel persona-based approach to compare the educational background predictions of GPT-3.5, Gemini, and Claude 3 Sonnet with actual data from LinkedIn. The study focused on various roles at Microsoft, Meta, and Google, including VP Product, Director of Engineering, and Software Engineer. We generated 432 personas across the three LLMs and analyzed the frequency of elite universities (Stanford, MIT, UC Berkeley, and Harvard) in these personas compared to LinkedIn data. Results showed that LLMs significantly overrepresented elite universities, featuring these universities 72.45% of the time, compared to only 8.56% in the actual LinkedIn data. ChatGPT 3.5 exhibited the highest bias, followed by Claude Sonnet 3, while Gemini performed best. This research highlights the need to address educational bias in LLMs and suggests strategies for mitigating such biases in AI-driven recruitment processes.
Advanced Mathematical Modelling for Energy-Efficient Data Transmission and Fusion in Wireless Sensor Networks
Venue: arXiv cs.NI 2024
Year: 2024

Wireless Sensor Networks (WSNs) are indispensable for data-intensive applications, necessitating efficient energy management and robust data fusion techniques. This paper proposes an integrated framework leveraging fuzzy logic and backpropagation neural networks (BPNN) to enhance energy efficiency and data accuracy in WSNs. The model focuses on optimizing Cluster Head (CH) selection using fuzzy logic, considering parameters such as energy levels, proximity to the base station, and local density centrality. A Minimum Spanning Tree (MST) algorithm is employed for energy-efficient data transmission from sensor nodes to CHs, minimizing energy consumption during data routing. BPNN-based data fusion at CHs reduces redundant data transmissions to the base station, thereby optimizing energy utilization and enhancing overall network performance. Simulation results demonstrate substantial improvements over conventional methods, including a 30% increase in network longevity, 25% improvement in data accuracy, and a 40% reduction in energy consumption. These gains are attributed to the intelligent CH selection strategy enabled by fuzzy logic, which ensures efficient resource allocation and minimizes energy wastage. The effectiveness of the proposed approach is validated through comprehensive simulations, showcasing its ability to prolong network lifetime, maintain data integrity, and improve energy efficiency. The integration of fuzzy logic and BPNN not only addresses the challenges of energy management and data fusion in WSNs but also provides a scalable and adaptable framework for future applications requiring reliable and sustainable sensor network operations. The source code is available at https://github.com/hikomal/BPNN_WSN.git
Vision Controlled Sensorized Prosthetic Hand
Venue: arXiv cs.HC 2024
Year: 2024

This paper presents a sensorized vision-enabled prosthetic hand aimed at replicating a natural hand's performance, functionality, appearance, and comfort. The design goal was to create an accessible substitution with a user-friendly interface requiring little to no training. Our mechanical hand uses a camera and embedded processors to perform most of these tasks. The interfaced pressure sensor is used to get pressure feedback and ensure a safe grasp of the object; an accelerometer is used to detect gestures and release the object. Unlike current EMG-based designs, the prototyped hand does not require personalized training. The details of the design, trade-offs, results, and informing the next iteration are presented in this paper.
Simplify, Consolidate, Intervene: Facilitating Institutional Support with Mental Models of Learning Management System Use
Venue: arXiv cs.HC 2024
Year: 2024

Measuring instructors' adoption of learning management system (LMS) tools is a critical first step in evaluating the efficacy of online teaching and learning at scale. Existing models for LMS adoption are often qualitative, learner-centered, and difficult to leverage towards institutional support. We propose depth-of-use (DOU): an intuitive measurement model for faculty's utilization of a university-wide LMS and their needs for institutional support. We hypothesis-test the relationship between DOU and course attributes like modality, participation, logistics, and outcomes. In a large-scale analysis of metadata from 30000+ courses offered at Virginia Tech over two years, we find that a pervasive need for scale, interoperability and ubiquitous access drives LMS adoption by university instructors. We then demonstrate how DOU can help faculty members identify the opportunity-cost of transition from legacy apps to LMS tools. We also describe how DOU can help instructional designers and IT organizational leadership evaluate the impact of their support allocation, faculty development and LMS evangelism initiatives.
I've Got 99 Problems But FLOPS Ain't One
Venue: arXiv cs.DC 2024
Year: 2024

Hyperscalers dominate the landscape of large network deployments, yet they rarely share data or insights about the challenges they face. In light of this supremacy, what problems can we find to solve in this space? We take an unconventional approach to find relevant research directions, starting from public plans to build a $100 billion datacenter for machine learning applications. Leveraging the language models scaling laws, we discover what workloads such a datacenter might carry and explore the challenges one may encounter in doing so, with a focus on networking research. We conclude that building the datacenter and training such models is technically possible, but this requires novel wide-area transports for inter-DC communication, a multipath transport and novel datacenter topologies for intra-datacenter communication, high speed scale-up networks and transports, outlining a rich research agenda for the networking community.
WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Although Large Language Models (LLMs) excel in NLP tasks, they still need external tools to extend their ability. Current research on tool learning with LLMs often assumes mandatory tool use, which does not always align with real-world situations, where the necessity for tools is uncertain, and incorrect or unnecessary use of tools can damage the general abilities of LLMs. Therefore, we propose to explore whether LLMs can discern their ability boundaries and use tools flexibly. We then introduce the Whether-or-not tool usage Evaluation benchmark (WTU-Eval) to assess LLMs with eleven datasets, where six of them are tool-usage datasets, and five are general datasets. LLMs are prompted to use tools according to their needs. The results of eight LLMs on WTU-Eval reveal that LLMs frequently struggle to determine tool use in general datasets, and LLMs' performance in tool-usage datasets improves when their ability is similar to ChatGPT. In both datasets, incorrect tool usage significantly impairs LLMs' performance. To mitigate this, we also develop the finetuning dataset to enhance tool decision-making. Fine-tuning Llama2-7B results in a 14\% average performance improvement and a 16.8\% decrease in incorrect tool usage. We will release the WTU-Eval benchmark.
Black-box Model Ensembling for Textual and Visual Question Answering via Information Fusion
Venue: arXiv cs.CL 2024
Year: 2024

A diverse range of large language models (LLMs), e.g., ChatGPT, and visual question answering (VQA) models, e.g., BLIP, have been developed for solving textual and visual question answering tasks. However, fine-tuning these models is either difficult, as it requires access via APIs, rendering them as black-boxes, or costly due to the need of tuning a large number of parameters. To address this, we introduce InfoSel, a data-efficient ensemble method that learns to dynamically pick the winner from existing black-box models for predictions on both textual and multimodal visual question answering tasks. Unlike traditional ensemble models, InfoSel does not rely on prediction probabilities or confidences, which typically are not available in black-box models. Experimental results on four datasets demonstrate that our approach achieves an absolute increase of up to +5.19\% in the F1-score compared to standalone LLMs using only 1K training instances.
MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production
Venue: arXiv cs.CL 2024
Year: 2024

Sign language understanding has made significant strides; however, there is still no viable solution for generating sign sequences directly from entire spoken content, e.g., text or speech. In this paper, we propose a unified framework for continuous sign language production, easing communication between sign and non-sign language users. In particular, a sequence diffusion model, utilizing embeddings extracted from text or speech, is crafted to generate sign predictions step by step. Moreover, by creating a joint embedding space for text, audio, and sign, we bind these modalities and leverage the semantic consistency among them to provide informative feedback for the model training. This embedding-consistency learning strategy minimizes the reliance on sign triplets and ensures continuous model refinement, even with a missing audio modality. Experiments on How2Sign and PHOENIX14T datasets demonstrate that our model achieves competitive performance in sign language production.
Aligning Model Evaluations with Human Preferences: Mitigating Token Count Bias in Language Model Assessments
Venue: arXiv cs.CL 2024
Year: 2024

The SLAM paper demonstrated that on-device Small Language Models (SLMs) are a viable and cost-effective alternative to API-based Large Language Models (LLMs), such as OpenAI's GPT-4, offering comparable performance and stability. However, SLAM also identified discrepancies between human preferences and traditional auto-evaluators. This follow-up paper explores methods to align LLM evaluator preferences with human evaluations by addressing biases, particularly toward higher token counts. We employed Bayesian statistics and a t-test to quantify this bias and developed a recalibration procedure to adjust the GPTScorer. Our findings significantly improve aligning the recalibrated LLM evaluator with human evaluations across multiple use cases. For instance, spearman's ranking correlation score in the Recommendation use case improved from -27.27 to 44.55. These results highlight the importance of accounting for biases in automated evaluations to ensure fair and accurate model assessments. The recalibration process enhances the reliability of automated evaluators, leading to better AI models that align with human values and expectations. This study provides a robust methodology for future research into bias correction and emphasizes the feasibility and benefits of developing human-aligned AI evaluation systems.
Large language models are good medical coders, if provided with tools
Venue: arXiv cs.IR 2024
Year: 2024

This study presents a novel two-stage Retrieve-Rank system for automated ICD-10-CM medical coding, comparing its performance against a Vanilla Large Language Model (LLM) approach. Evaluating both systems on a dataset of 100 single-term medical conditions, the Retrieve-Rank system achieved 100% accuracy in predicting correct ICD-10-CM codes, significantly outperforming the Vanilla LLM (GPT-3.5-turbo), which achieved only 6% accuracy. Our analysis demonstrates the Retrieve-Rank system's superior precision in handling various medical terms across different specialties. While these results are promising, we acknowledge the limitations of using simplified inputs and the need for further testing on more complex, realistic medical cases. This research contributes to the ongoing effort to improve the efficiency and accuracy of medical coding, highlighting the importance of retrieval-based approaches.
Large Language Models can impersonate politicians and other public figures
Venue: arXiv cs.CL 2024
Year: 2024

Modern AI technology like Large language models (LLMs) has the potential to pollute the public information sphere with made-up content, which poses a significant threat to the cohesion of societies at large. A wide range of research has shown that LLMs are capable of generating text of impressive quality, including persuasive political speech, text with a pre-defined style, and role-specific content. But there is a crucial gap in the literature: We lack large-scale and systematic studies of how capable LLMs are in impersonating political and societal representatives and how the general public judges these impersonations in terms of authenticity, relevance and coherence. We present the results of a study based on a cross-section of British society that shows that LLMs are able to generate responses to debate questions that were part of a broadcast political debate programme in the UK. The impersonated responses are judged to be more authentic and relevant than the original responses given by people who were impersonated. This shows two things: (1) LLMs can be made to contribute meaningfully to the public political debate and (2) there is a dire need to inform the general public of the potential harm this can have on society.
Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis
Venue: arXiv cs.CL 2024
Year: 2024

In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have explored the capabilities of Large Language Models (LLMs) for automated scientific reviewing, their generated contents are often generic or partial. To address the issues above, we introduce an automated paper reviewing framework SEA. It comprises of three modules: Standardization, Evaluation, and Analysis, which are represented by models SEA-S, SEA-E, and SEA-A, respectively. Initially, SEA-S distills data standardization capabilities of GPT-4 for integrating multiple reviews for a paper. Then, SEA-E utilizes standardized data for fine-tuning, enabling it to generate constructive reviews. Finally, SEA-A introduces a new evaluation metric called mismatch score to assess the consistency between paper contents and reviews. Moreover, we design a self-correction strategy to enhance the consistency. Extensive experimental results on datasets collected from eight venues show that SEA can generate valuable insights for authors to improve their papers.
Automated Question Generation on Tabular Data for Conversational Data Exploration
Venue: arXiv cs.CL 2024
Year: 2024

Exploratory data analysis (EDA) is an essential step for analyzing a dataset to derive insights. Several EDA techniques have been explored in the literature. Many of them leverage visualizations through various plots. But it is not easy to interpret them for a non-technical user, and producing appropriate visualizations is also tough when there are a large number of columns. Few other works provide a view of some interesting slices of data but it is still difficult for the user to draw relevant insights from them. Of late, conversational data exploration is gaining a lot of traction among non-technical users. It helps the user to explore the dataset without having deep technical knowledge about the data. Towards this, we propose a system that recommends interesting questions in natural language based on relevant slices of a dataset in a conversational setting. Specifically, given a dataset, we pick a select set of interesting columns and identify interesting slices of such columns and column combinations based on few interestingness measures. We use our own fine-tuned variation of a pre-trained language model(T5) to generate natural language questions in a specific manner. We then slot-fill values in the generated questions and rank them for recommendations. We show the utility of our proposed system in a coversational setting with a collection of real datasets.
STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs
Venue: arXiv cs.CL 2024
Year: 2024

We present Simplified Text-Attributed Graph Embeddings (STAGE), a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our approach leverages Large-Language Models (LLMs) to generate embeddings for textual attributes. STAGE achieves competitive results on various node classification benchmarks while also maintaining a simplicity in implementation relative to current state-of-the-art (SoTA) techniques. We show that utilizing pre-trained LLMs as embedding generators provides robust features for ensemble GNN training, enabling pipelines that are simpler than current SoTA approaches which require multiple expensive training and prompting stages. We also implement diffusion-pattern GNNs in an effort to make this pipeline scalable to graphs beyond academic benchmarks.
Clustering Time-Evolving Networks Using the Spatio-Temporal Graph Laplacian
Venue: arXiv cs.SI 2024
Year: 2024

Time-evolving graphs arise frequently when modeling complex dynamical systems such as social networks, traffic flow, and biological processes. Developing techniques to identify and analyze communities in these time-varying graph structures is an important challenge. In this work, we generalize existing spectral clustering algorithms from static to dynamic graphs using canonical correlation analysis (CCA) to capture the temporal evolution of clusters. Based on this extended canonical correlation framework, we define the spatio-temporal graph Laplacian and investigate its spectral properties. We connect these concepts to dynamical systems theory via transfer operators, and illustrate the advantages of our method on benchmark graphs by comparison with existing methods. We show that the spatio-temporal graph Laplacian allows for a clear interpretation of cluster structure evolution over time for directed and undirected graphs.
Beyond KV Caching: Shared Attention for Efficient LLMs
Venue: arXiv cs.CL 2024
Year: 2024

The efficiency of large language models (LLMs) remains a critical challenge, particularly in contexts where computational resources are limited. Traditional attention mechanisms in these models, while powerful, require significant computational and memory resources due to the necessity of recalculating and storing attention weights across different layers. This paper introduces a novel Shared Attention (SA) mechanism, designed to enhance the efficiency of LLMs by directly sharing computed attention weights across multiple layers. Unlike previous methods that focus on sharing intermediate Key-Value (KV) caches, our approach utilizes the isotropic tendencies of attention distributions observed in advanced LLMs post-pretraining to reduce both the computational flops and the size of the KV cache required during inference. We empirically demonstrate that implementing SA across various LLMs results in minimal accuracy loss on standard benchmarks. Our findings suggest that SA not only conserves computational resources but also maintains robust model performance, thereby facilitating the deployment of more efficient LLMs in resource-constrained environments.