arXiv:2305.04214v3 »Full PDF »PiML (read π-ML, /`pai`em`el/) is an integrated and open-access Python
toolbox for interpretable machine learning model development and model
diagnostics. It is designed with machine learning workflows in both low-code
and high-code modes, including data pipeline, model training and tuning, model
interpretation and explanation, and model diagnostics and comparison. The
toolbox supports a growing list of interpretable models (e.g. GAM, GAMI-Net,
XGB1/XGB2) with inherent local and/or global interpretability. It also supports
model-agnostic explainability tools (e.g. PFI, PDP, LIME, SHAP) and a powerful
suite of model-agnostic diagnostics (e.g. weakness, reliability, robustness,
resilience, fairness). Integration of PiML models and tests to existing MLOps
platforms for quality assurance are enabled by flexible high-code APIs.
Furthermore, PiML toolbox comes with a comprehensive user guide and hands-on
examples, including the applications for model development and validation in
banking. The project is available at
https://github.com/SelfExplainML/PiML-Toolbox.Abstract
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained
Language Model
Update model and results; add comparison with EVA2.0
In this paper, we introduce PanGu-Bot, a Chinese pre-trained open-domain
dialogue generation model based on a large pre-trained language model (PLM)
PANGU-alpha (Zeng et al.,2021). Different from other pre-trained dialogue
models trained over a massive amount of dialogue data from scratch, we aim to
build a powerful dialogue model with relatively fewer data and computation
costs by inheriting valuable language capabilities and knowledge from PLMs. To
this end, we train PanGu-Bot from the large PLM PANGU-alpha, which has been
proven well-performed on a variety of Chinese natural language tasks. We
investigate different aspects of responses generated by PanGu-Bot, including
response quality, knowledge, and safety. We show that PanGu-Bot outperforms
state-of-the-art Chinese dialogue systems (CDIALGPT (Wang et al., 2020), EVA
(Zhou et al., 2021), EVA2.0 (Gu et al., 2022)) w.r.t. the above three aspects.
We also demonstrate that PanGu-Bot can be easily deployed to generate emotional
responses without further training. Throughout our empirical analysis, we also
point out that the PanGu-Bot response quality, knowledge correctness, and
safety are still far from perfect, and further explorations are indispensable
to building reliable and smart dialogue systems. Our model and code will be
available at
https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/PanGu-Bot
soon.Abstract
Advances in Photoacoustic Imaging Reconstruction and Quantitative
Analysis for Biomedical Applications
arXiv:2411.02843v1 »Full PDF »Photoacoustic imaging (PAI) represents an innovative biomedical imaging
modality that harnesses the advantages of optical resolution and acoustic
penetration depth while ensuring enhanced safety. Despite its promising
potential across a diverse array of preclinical and clinical applications, the
clinical implementation of PAI faces significant challenges, including the
trade-off between penetration depth and spatial resolution, as well as the
demand for faster imaging speeds. This paper explores the fundamental
principles underlying PAI, with a particular emphasis on three primary
implementations: photoacoustic computed tomography (PACT), photoacoustic
microscopy (PAM), and photoacoustic endoscopy (PAE). We undertake a critical
assessment of their respective strengths and practical limitations.
Furthermore, recent developments in utilizing conventional or deep learning
(DL) methodologies for image reconstruction and artefact mitigation across
PACT, PAM, and PAE are outlined, demonstrating considerable potential to
enhance image quality and accelerate imaging processes. Furthermore, this paper
examines the recent developments in quantitative analysis within PAI, including
the quantification of haemoglobin concentration, oxygen saturation, and other
physiological parameters within tissues. Finally, our discussion encompasses
current trends and future directions in PAI research while emphasizing the
transformative impact of deep learning on advancing PAI.Abstract
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models
arXiv:2410.21471v2 »Full PDF »Recent advances in diffusion models have significantly enhanced the quality
of image synthesis, yet they have also introduced serious safety concerns,
particularly the generation of Not Safe for Work (NSFW) content. Previous
research has demonstrated that adversarial prompts can be used to generate NSFW
content. However, such adversarial text prompts are often easily detectable by
text-based filters, limiting their efficacy. In this paper, we expose a
previously overlooked vulnerability: adversarial image attacks targeting
Image-to-Image (I2I) diffusion models. We propose AdvI2I, a novel framework
that manipulates input images to induce diffusion models to generate NSFW
content. By optimizing a generator to craft adversarial images, AdvI2I
circumvents existing defense mechanisms, such as Safe Latent Diffusion (SLD),
without altering the text prompts. Furthermore, we introduce AdvI2I-Adaptive,
an enhanced version that adapts to potential countermeasures and minimizes the
resemblance between adversarial images and NSFW concept embeddings, making the
attack more resilient against defenses. Through extensive experiments, we
demonstrate that both AdvI2I and AdvI2I-Adaptive can effectively bypass current
safeguards, highlighting the urgent need for stronger security measures to
address the misuse of I2I diffusion models.Abstract
Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
Generative models such as Large Language Models (LLM) and Multimodal Large
Language models (MLLMs) trained on massive web corpora can memorize and
disclose individuals' confidential and private data, raising legal and ethical
concerns. While many previous works have addressed this issue in LLM via
machine unlearning, it remains largely unexplored for MLLMs. To tackle this
challenge, we introduce Multimodal Large Language Model Unlearning Benchmark
(MLLMU-Bench), a novel benchmark aimed at advancing the understanding of
multimodal machine unlearning. MLLMU-Bench consists of 500 fictitious profiles
and 153 profiles for public celebrities, each profile feature over 14
customized question-answer pairs, evaluated from both multimodal (image+text)
and unimodal (text) perspectives. The benchmark is divided into four sets to
assess unlearning algorithms in terms of efficacy, generalizability, and model
utility. Finally, we provide baseline results using existing generative model
unlearning algorithms. Surprisingly, our experiments show that unimodal
unlearning algorithms excel in generation and cloze tasks, while multimodal
unlearning approaches perform better in classification tasks with multimodal
inputs.Abstract
Building Altruistic and Moral AI Agent with Brain-inspired Affective
Empathy Mechanisms
arXiv:2410.21882v1 »Full PDF »As AI closely interacts with human society, it is crucial to ensure that its
decision-making is safe, altruistic, and aligned with human ethical and moral
values. However, existing research on embedding ethical and moral
considerations into AI remains insufficient, and previous external constraints
based on principles and rules are inadequate to provide AI with long-term
stability and generalization capabilities. In contrast, the intrinsic
altruistic motivation based on empathy is more willing, spontaneous, and
robust. Therefore, this paper is dedicated to autonomously driving intelligent
agents to acquire morally behaviors through human-like affective empathy
mechanisms. We draw inspiration from the neural mechanism of human brain's
moral intuitive decision-making, and simulate the mirror neuron system to
construct a brain-inspired affective empathy-driven altruistic decision-making
model. Here, empathy directly impacts dopamine release to form intrinsic
altruistic motivation. Based on the principle of moral utilitarianism, we
design the moral reward function that integrates intrinsic empathy and
extrinsic self-task goals. A comprehensive experimental scenario incorporating
empathetic processes, personal objectives, and altruistic goals is developed.
The proposed model enables the agent to make consistent moral decisions
(prioritizing altruism) by balancing self-interest with the well-being of
others. We further introduce inhibitory neurons to regulate different levels of
empathy and verify the positive correlation between empathy levels and
altruistic preferences, yielding conclusions consistent with findings from
psychological behavioral experiments. This work provides a feasible solution
for the development of ethical AI by leveraging the intrinsic human-like
empathy mechanisms, and contributes to the harmonious coexistence between
humans and AI.Abstract
arXiv:2410.21276v1 »Full PDF »GPT-4o is an autoregressive omni model that accepts as input any combination
of text, audio, image, and video, and generates any combination of text, audio,
and image outputs. It's trained end-to-end across text, vision, and audio,
meaning all inputs and outputs are processed by the same neural network. GPT-4o
can respond to audio inputs in as little as 232 milliseconds, with an average
of 320 milliseconds, which is similar to human response time in conversation.
It matches GPT-4 Turbo performance on text in English and code, with
significant improvement on text in non-English languages, while also being much
faster and 50\% cheaper in the API. GPT-4o is especially better at vision and
audio understanding compared to existing models. In line with our commitment to
building AI safely and consistent with our voluntary commitments to the White
House, we are sharing the GPT-4o System Card, which includes our Preparedness
Framework evaluations. In this System Card, we provide a detailed look at
GPT-4o's capabilities, limitations, and safety evaluations across multiple
categories, focusing on speech-to-speech while also evaluating text and image
capabilities, and measures we've implemented to ensure the model is safe and
aligned. We also include third-party assessments on dangerous capabilities, as
well as discussion of potential societal impacts of GPT-4o's text and vision
capabilities.Abstract
Reinforced Imitative Trajectory Planning for Urban Automated Driving
Reinforcement learning (RL) faces challenges in trajectory planning for urban
automated driving due to the poor convergence of RL and the difficulty in
designing reward functions. The convergence problem is alleviated by combining
RL with supervised learning. However, most existing approaches only reason one
step ahead and lack the capability to plan for multiple future steps. Besides,
although inverse reinforcement learning holds promise for solving the reward
function design issue, existing methods for automated driving impose a linear
structure assumption on reward functions, making them difficult to apply to
urban automated driving. In light of these challenges, this paper proposes a
novel RL-based trajectory planning method that integrates RL with imitation
learning to enable multi-step planning. Furthermore, a transformer-based
Bayesian reward function is developed, providing effective reward signals for
RL in urban scenarios. Moreover, a hybrid-driven trajectory planning framework
is proposed to enhance safety and interpretability. The proposed methods were
validated on the large-scale real-world urban automated driving nuPlan dataset.
The results demonstrated the significant superiority of the proposed methods
over the baselines in terms of the closed-loop metrics. The code is available
at https://github.com/Zigned/nuplan_zigned.Abstract
MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation
and Rigorous Verification
arXiv:2410.15154v1 »Full PDF »Large Language Models (LLMs) have shown considerable promise in code
generation. However, the automation sector, especially in motion control,
continues to rely heavily on manual programming due to the complexity of tasks
and critical safety considerations. In this domain, incorrect code execution
can pose risks to both machinery and personnel, necessitating specialized
expertise. To address these challenges, we introduce MCCoder, an LLM-powered
system designed to generate code that addresses complex motion control tasks,
with integrated soft-motion data verification. MCCoder enhances code generation
through multitask decomposition, hybrid retrieval-augmented generation (RAG),
and self-correction with a private motion library. Moreover, it supports data
verification by logging detailed trajectory data and providing simulations and
plots, allowing users to assess the accuracy of the generated code and
bolstering confidence in LLM-based programming. To ensure robust validation, we
propose MCEVAL, an evaluation dataset with metrics tailored to motion control
tasks of varying difficulties. Experiments indicate that MCCoder improves
performance by 11.61% overall and by 66.12% on complex tasks in MCEVAL dataset
compared with base models with naive RAG. This system and dataset aim to
facilitate the application of code generation in automation settings with
strict safety requirements. MCCoder is publicly available at
https://github.com/MCCodeAI/MCCoder.Abstract
A Common Pitfall of Margin-based Language Model Alignment: Gradient
Entanglement
arXiv:2410.13828v1 »Full PDF »Reinforcement Learning from Human Feedback (RLHF) has become the predominant
approach for language model (LM) alignment. At its core, RLHF uses a
margin-based loss for preference optimization, specifying ideal LM behavior
only by the difference between preferred and dispreferred responses. In this
paper, we identify a common pitfall of margin-based methods -- the
under-specification of ideal LM behavior on preferred and dispreferred
responses individually, which leads to two unintended consequences as the
margin increases: (1) The probability of dispreferred (e.g., unsafe) responses
may increase, resulting in potential safety alignment failures. (2) The
probability of preferred responses may decrease, even when those responses are
ideal. We demystify the reasons behind these problematic behaviors:
margin-based losses couple the change in the preferred probability to the
gradient of the dispreferred one, and vice versa, often preventing the
preferred probability from increasing while the dispreferred one decreases, and
thus causing a synchronized increase or decrease in both probabilities. We term
this effect, inherent in margin-based objectives, gradient entanglement.
Formally, we derive conditions for general margin-based alignment objectives
under which gradient entanglement becomes concerning: the inner product of the
gradients of preferred and dispreferred log-probabilities is large relative to
the individual gradient norms. We theoretically investigate why such inner
products can be large when aligning language models and empirically validate
our findings. Empirical implications of our framework extend to explaining
important differences in the training dynamics of various preference
optimization algorithms, and suggesting potential algorithm designs to mitigate
the under-specification issue of margin-based methods and thereby improving
language model alignment.Abstract
16737 latest Fairness/Ethics + ML/AI papers
