Word embeddings represent a transformative technology for analyzing text data
in social work research, offering sophisticated tools for understanding case
notes, policy documents, research literature, and other text-based materials.
This methodological paper introduces word embeddings to social work
researchers, explaining how these mathematical representations capture meaning
and relationships in text data more effectively than traditional keyword-based
approaches. We discuss fundamental concepts, technical foundations, and
practical applications, including semantic search, clustering, and retrieval
augmented generation. The paper demonstrates how embeddings can enhance
research workflows through concrete examples from social work practice, such as
analyzing case notes for housing instability patterns and comparing social work
licensing examinations across languages. While highlighting the potential of
embeddings for advancing social work research, we acknowledge limitations
including information loss, training data constraints, and potential biases. We
conclude that successfully implementing embedding technologies in social work
requires developing domain-specific models, creating accessible tools, and
establishing best practices aligned with social work's ethical principles. This
integration can enhance our ability to analyze complex patterns in text data
while supporting more effective services and interventions.Abstract
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends
arXiv:2411.04383v1 »Full PDF »Explainability is an essential reason limiting the application of neural
networks in many vital fields. Although neuro-symbolic AI hopes to enhance the
overall explainability by leveraging the transparency of symbolic learning, the
results are less evident than imagined. This article proposes a classification
for explainability by considering both model design and behavior of 191 studies
from 2013, focusing on neuro-symbolic AI, hoping to inspire scholars who want
to understand the explainability of neuro-symbolic AI. Precisely, we classify
them into five categories by considering whether the form of bridging the
representation differences is readable as their design factor, if there are
representation differences between neural networks and symbolic logic learning,
and whether a model decision or prediction process is understandable as their
behavior factor: implicit intermediate representations and implicit prediction,
partially explicit intermediate representations and partially explicit
prediction, explicit intermediate representations or explicit prediction,
explicit intermediate representation and explicit prediction, unified
representation and explicit prediction. We also analyzed the research trends
and three significant challenges: unified representations, explainability and
transparency, and sufficient cooperation from neural networks and symbolic
learning. Finally, we put forward suggestions for future research in three
aspects: unified representations, enhancing model explainability, ethical
considerations, and social impact.Abstract
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
arXiv:2407.07457v4 »Full PDF »The emergence of large language models (LLMs) has revolutionized the way we
interact with graphs, leading to a new paradigm called GraphLLM. Despite the
rapid development of GraphLLM methods in recent years, the progress and
understanding of this field remain unclear due to the lack of a benchmark with
consistent experimental protocols. To bridge this gap, we introduce GLBench,
the first comprehensive benchmark for evaluating GraphLLM methods in both
supervised and zero-shot scenarios. GLBench provides a fair and thorough
evaluation of different categories of GraphLLM methods, along with traditional
baselines such as graph neural networks. Through extensive experiments on a
collection of real-world datasets with consistent data processing and splitting
strategies, we have uncovered several key findings. Firstly, GraphLLM methods
outperform traditional baselines in supervised settings, with LLM-as-enhancers
showing the most robust performance. However, using LLMs as predictors is less
effective and often leads to uncontrollable output issues. We also notice that
no clear scaling laws exist for current GraphLLM methods. In addition, both
structures and semantics are crucial for effective zero-shot transfer, and our
proposed simple baseline can even outperform several models tailored for
zero-shot scenarios. The data and code of the benchmark can be found at
https://github.com/NineAbyss/GLBench.Abstract
Verifying Non-friendly Formal Verification Designs: Can We Start
Earlier?
The design of Systems on Chips (SoCs) is becoming more and more complex due
to technological advancements. Missed bugs can cause drastic failures in
safety-critical environments leading to the endangerment of lives. To overcome
these drastic failures, formal property verification (FPV) has been applied in
the industry. However, there exist multiple hardware designs where the results
of FPV are not conclusive even for long runtimes of model-checking tools. For
this reason, the use of High-level Equivalence Checking (HLEC) tools has been
proposed in the last few years. However, the procedure for how to use it inside
an industrial toolchain has not been defined. For this reason, we proposed an
automated methodology based on metamodeling techniques which consist of two
main steps. First, an untimed algorithmic description written in C++ is
verified in an early stage using generated assertions; the advantage of this
step is that the assertions at the software level run in seconds and we can
start our analysis with conclusive results about our algorithm before starting
to write the RTL (Register Transfer Level) design. Second, this algorithmic
description is verified against its sequential design using HLEC and the
respective metamodel parameters. The results show that the presented
methodology can find bugs early related to the algorithmic description and
prepare the setup for the HLEC verification. This helps to reduce the
verification efforts to set up the tool and write the properties manually which
is always error-prone. The proposed framework can help teams working on
datapaths to verify and make decisions in an early stage of the verification
flow.Abstract
Acoustic Model Optimization over Multiple Data Sources: Merging and
Valuation
arXiv:2410.15620v1 »Full PDF »Due to the rising awareness of privacy protection and the voluminous scale of
speech data, it is becoming infeasible for Automatic Speech Recognition (ASR)
system developers to train the acoustic model with complete data as before. For
example, the data may be owned by different curators, and it is not allowed to
share with others. In this paper, we propose a novel paradigm to solve salient
problems plaguing the ASR field. In the first stage, multiple acoustic models
are trained based upon different subsets of the complete speech data, while in
the second phase, two novel algorithms are utilized to generate a high-quality
acoustic model based upon those trained on data subsets. We first propose the
Genetic Merge Algorithm (GMA), which is a highly specialized algorithm for
optimizing acoustic models but suffers from low efficiency. We further propose
the SGD-Based Optimizational Merge Algorithm (SOMA), which effectively
alleviates the efficiency bottleneck of GMA and maintains superior model
accuracy. Extensive experiments on public data show that the proposed methods
can significantly outperform the state-of-the-art. Furthermore, we introduce
Shapley Value to estimate the contribution score of the trained models, which
is useful for evaluating the effectiveness of the data and providing fair
incentives to their curators.Abstract
The Politics of Fear and the Experience of Bangladeshi Religious
Minority Communities Using Social Media Platforms
Despite significant research on online harm, polarization, public
deliberation, and justice, CSCW still lacks a comprehensive understanding of
the experiences of religious minorities, particularly in relation to fear, as
prominently evident in our study. Gaining faith-sensitive insights into the
expression, participation, and inter-religious interactions on social media can
contribute to CSCW's literature on online safety and interfaith communication.
In pursuit of this goal, we conducted a six-month-long, interview-based study
with the Hindu, Buddhist, and Indigenous communities in Bangladesh. Our study
draws on an extensive body of research encompassing the spiral of silence, the
cultural politics of fear, and communication accommodation to examine how
social media use by religious minorities is influenced by fear, which is
associated with social conformity, misinformation, stigma, stereotypes, and
South Asian postcolonial memory. Moreover, we engage with scholarly
perspectives from religious studies, justice, and South Asian violence and
offer important critical insights and design lessons for the CSCW literature on
public deliberation, justice, and interfaith communication.Abstract
DreamSat: Towards a General 3D Model for Novel View Synthesis of Space
Objects
Presented at the 75th International Astronautical Congress, October
2024, Milan, Italy
Novel view synthesis (NVS) enables to generate new images of a scene or
convert a set of 2D images into a comprehensive 3D model. In the context of
Space Domain Awareness, since space is becoming increasingly congested, NVS can
accurately map space objects and debris, improving the safety and efficiency of
space operations. Similarly, in Rendezvous and Proximity Operations missions,
3D models can provide details about a target object's shape, size, and
orientation, allowing for better planning and prediction of the target's
behavior. In this work, we explore the generalization abilities of these
reconstruction techniques, aiming to avoid the necessity of retraining for each
new scene, by presenting a novel approach to 3D spacecraft reconstruction from
single-view images, DreamSat, by fine-tuning the Zero123 XL, a state-of-the-art
single-view reconstruction model, on a high-quality dataset of 190 high-quality
spacecraft models and integrating it into the DreamGaussian framework. We
demonstrate consistent improvements in reconstruction quality across multiple
metrics, including Contrastive Language-Image Pretraining (CLIP) score
(+0.33%), Peak Signal-to-Noise Ratio (PSNR) (+2.53%), Structural Similarity
Index (SSIM) (+2.38%), and Learned Perceptual Image Patch Similarity (LPIPS)
(+0.16%) on a test set of 30 previously unseen spacecraft images. Our method
addresses the lack of domain-specific 3D reconstruction tools in the space
industry by leveraging state-of-the-art diffusion models and 3D Gaussian
splatting techniques. This approach maintains the efficiency of the
DreamGaussian framework while enhancing the accuracy and detail of spacecraft
reconstructions. The code for this work can be accessed on GitHub
(https://github.com/ARCLab-MIT/space-nvs).Abstract
Precision Knowledge Editing: Enhancing Safety in Large Language Models
arXiv:2410.03772v1 »Full PDF »Large language models (LLMs) have demonstrated remarkable capabilities, but
they also pose risks related to the generation of toxic or harmful content.
This work introduces Precision Knowledge Editing (PKE), an advanced technique
that builds upon existing knowledge editing methods to more effectively
identify and modify toxic parameter regions within LLMs. By leveraging neuron
weight tracking and activation pathway tracing, PKE achieves finer granularity
in toxic content management compared to previous methods like Detoxifying
Instance Neuron Modification (DINM). Our experiments demonstrate that PKE
significantly reduces the attack success rate (ASR) across various models,
including Llama2-7b and Llama-3-8b-instruct, while maintaining overall model
performance. Additionally, we also compared the performance of some
closed-source models (gpt-4-0613 and Claude 3 Sonnet) in our experiments, and
found that models adjusted using our method far outperformed the closed-source
models in terms of safety. This research contributes to the ongoing efforts to
make LLMs safer and more reliable for real-world applications.Abstract
Privacy in Large Language Models: Attacks, Defenses and Future
Directions
We upload the survey to cover more recent papers and inlcude privacy
resaearch on multi-modality
The advancement of large language models (LLMs) has significantly enhanced
the ability to effectively tackle various downstream NLP tasks and unify these
tasks into generative pipelines. On the one hand, powerful language models,
trained on massive textual data, have brought unparalleled accessibility and
usability for both models and users. On the other hand, unrestricted access to
these models can also introduce potential malicious and unintentional privacy
risks. Despite ongoing efforts to address the safety and privacy concerns
associated with LLMs, the problem remains unresolved. In this paper, we provide
a comprehensive analysis of the current privacy attacks targeting LLMs and
categorize them according to the adversary's assumed capabilities to shed light
on the potential vulnerabilities present in LLMs. Then, we present a detailed
overview of prominent defense strategies that have been developed to counter
these privacy attacks. Beyond existing works, we identify upcoming privacy
concerns as LLMs evolve. Lastly, we point out several potential avenues for
future exploration.Abstract
The Responsible Foundation Model Development Cheatsheet: A Review of
Tools & Resources
arXiv:2406.16746v3 »Full PDF »Foundation model development attracts a rapidly expanding body of
contributors, scientists, and applications. To help shape responsible
development practices, we introduce the Foundation Model Development
Cheatsheet: a growing collection of 250+ tools and resources spanning text,
vision, and speech modalities. We draw on a large body of prior work to survey
resources (e.g. software, documentation, frameworks, guides, and practical
tools) that support informed data selection, processing, and understanding,
precise and limitation-aware artifact documentation, efficient model training,
advance awareness of the environmental impact from training, careful model
evaluation of capabilities, risks, and claims, as well as responsible model
release, licensing and deployment practices. We hope this curated collection of
resources helps guide more responsible development. The process of curating
this list, enabled us to review the AI development ecosystem, revealing what
tools are critically missing, misused, or over-used in existing practices. We
find that (i) tools for data sourcing, model evaluation, and monitoring are
critically under-serving ethical and real-world needs, (ii) evaluations for
model safety, capabilities, and environmental impact all lack reproducibility
and transparency, (iii) text and particularly English-centric analyses continue
to dominate over multilingual and multi-modal analyses, and (iv) evaluation of
systems, rather than just models, is needed so that capabilities and impact are
assessed in context.Abstract
16737 latest Fairness/Ethics + ML/AI papers
