Gregory Dudek

Hallucination Detection and Hallucination Mitigation: An Investigation

M. Jenkin

Steve Liu

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of d… (voir plus)ifferent applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.

2024-01-16

ArXiv (prépublication)

Hallucination Detection and Hallucination Mitigation: An Investigation

M. Jenkin

Steve Liu

2024-01-16

ArXiv (prépublication)

Hallucination Detection and Hallucination Mitigation: An Investigation

M. Jenkin

Steve Liu

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of d… (voir plus)ifferent applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.

2024-01-16

ArXiv (prépublication)

Hallucination Detection and Hallucination Mitigation: An Investigation

M. Jenkin

Steve Liu

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of d… (voir plus)ifferent applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.

2024-01-16

ArXiv (prépublication)

AIoT Smart Home via Autonomous LLM Agents

Dmitriy Rivkin

Francois Hogan

Amal Feriani

Abhisek Konar

Adam Sigal

Xue (Steve) Liu

2024-01-01

IEEE Internet of Things Journal (publié)

Interacting with a Visuotactile Countertop

M. Jenkin

Francois Hogan

Kaleem Siddiqi

Jean-François Tremblay

Bobak H. Baghi

2024-01-01

International Conference on Robotics, Computer Vision and Intelligent Systems (publié)

Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model

Faraz Lotfi

Khalil Virji

Farnoosh Faraji

Lucas Berry

Andrew Holliday

David Meger

In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning … (voir plus)(RL) for navigation planning of an autonomous model car across offroad, unstructured terrains without relying on predefined maps. Our innovative approach takes inspiration from BADGR, an LSTM-based network that primarily concentrates on environment modeling, but distinguishes itself by substituting LSTM modules with transformers to greatly elevate the performance our model. Addressing uncertainty within the system, we train an ensemble of predictive models and estimate the mutual information between model weights and outputs, facilitating dynamic horizon planning through the introduction of variable speeds. Further enhancing our methodology, we incorporate a nonlinear MPC controller that accounts for the intricacies of the vehicle's model and states. The model-based RL facet produces steering angles and quantifies inherent uncertainty. At the same time, the nonlinear MPC suggests optimal throttle settings, striking a balance between goal attainment speed and managing model uncertainty influenced by velocity. In the conducted studies, our approach excels over the existing baseline by consistently achieving higher metric values in predicting future events and seamlessly integrating the vehicle's kinematic model for enhanced decision-making. The code and the evaluation data are available at https://github.com/FARAZLOTFI/offroad_autonomous_navigation/).

2024-01-01

ICRA (publié)

Visual-Tactile Inference of 2.5D Object Shape From Marker Texture

Affan Jilani

Francois Hogan

Charlotte Morissette

M. Jenkin

Kaleem Siddiqi

2024-01-01

IEEE Robotics and Automation Letters (publié)

Device-Free Human State Estimation using UWB Multi-Static Radios

Saria Al Lahham

Bobak H. Baghi

Pierre-Yves Lajoie

Amal Feriani

Sachini Herath

Steve Liu

We present a human state estimation framework that allows us to estimate the location, and even the activities, of people in an indoor envir… (voir plus)onment without the requirement that they carry a specific devices with them. To achieve this"device free"localization we use a small number of low-cost Ultra-Wide Band (UWB) sensors distributed across the environment of interest. To achieve high quality estimation from the UWB signals merely reflected of people in the environment, we exploit a deep network that can learn to make inferences. The hardware setup consists of commercial off-the-shelf (COTS) single antenna UWB modules for sensing, paired with Raspberry PI units for computational processing and data transfer. We make use of the channel impulse response (CIR) measurements from the UWB sensors to estimate the human state - comprised of location and activity - in a given area. Additionally, we can also estimate the number of humans that occupy this region of interest. In our approach, first, we pre-process the CIR data which involves meticulous aggregation of measurements and extraction of key statistics. Afterwards, we leverage a convolutional deep neural network to map the CIRs into precise location estimates with sub-30 cm accuracy. Similarly, we achieve accurate human activity recognition and occupancy counting results. We show that we can quickly fine-tune our model for new out-of-distribution users, a process that requires only a few minutes of data and a few epochs of training. Our results show that UWB is a promising solution for adaptable smart-home localization and activity recognition problems.

2023-12-26

ArXiv (prépublication)

AdaTeacher: Adaptive Multi-Teacher Weighting for Communication Load Forecasting

Chengming Hu

Ju Wang

Yan Xin

Charlie Zhang

Xue (Steve) Liu

To deal with notorious delays in communication systems, it is crucial to forecast key system characteristics, such as the communication load… (voir plus). Most existing studies aggregate data from multiple edge nodes for improving the forecasting accuracy. However, the bandwidth cost of such data aggregation could be unacceptably high from the perspective of system operators. To achieve both the high forecasting accuracy and bandwidth efficiency, this paper proposes an Adaptive Multi-Teacher Weighting in Teacher-Student Learning approach, namely AdaTeacher, for communication load forecasting of multiple edge nodes. Each edge node trains a local model on its own data. A target node collects multiple models from its neighbor nodes and treats these models as teachers. Then, the target node trains a student model from teachers via Teacher-Student (T-S) learning. Unlike most existing T-S learning approaches that treat teachers evenly, resulting in a limited performance, AdaTeacher introduces a bilevel optimization algorithm to dynamically learn an importance weight for each teacher toward a more effective and accurate T-S learning process. Compared to the state-of-the-art methods, Ada Teacher not only reduces the bandwidth cost by 53.85%, but also improves the load forecasting accuracy by 21.56% and 24.24% on two real-world datasets.

2023-12-04

Global Communications Conference (publié)

Energy Saving in Cellular Wireless Networks via Transfer Deep Reinforcement Learning

Yi Tian Xu

M. Jenkin

Seowoo Jang

Ekram Hossain

Xue (Steve) Liu

With the increasing use of data-intensive mobile applications and the number of mobile users, the demand for wireless data services has been… (voir plus) increasing exponentially in recent years. In order to address this demand, a large number of new cellular base stations are being deployed around the world, leading to a significant increase in energy consumption and greenhouse gas emission. Consequently, energy consumption has emerged as a key concern in the fifth-generation (5G) network era and beyond. Reinforcement learning (RL), which aims to learn a control policy via interacting with the environment, has been shown to be effective in addressing network optimization problems. However, for reinforcement learning, especially deep reinforcement learning, a large number of interactions with the environment are required. This often limits its applicability in the real world. In this work, to better deal with dynamic traffic scenarios and improve real-world applicability, we propose a transfer deep reinforcement learning framework for energy optimization in cellular communication networks. Specifically, we first pre-train a set of RL-based energy-saving policies on source base stations and then transfer the most suitable policy to the given target base station in an unsupervised learning manner. Experimental results demonstrate that base station energy consumption can be reduced significantly using this approach.

2023-12-04

Global Communications Conference (publié)