Instruction: What field is the article from?

Prompt:
Title: Practical Estimation of Ensemble Accuracy
Abstract: Ensemble learning combines several individual models to obtain better
generalization performance. In this work we present a practical method for
estimating the joint power of several classifiers which differs from existing
approaches by {\em not relying on labels}, hence enabling the work in
unsupervised setting of huge datasets. It differs from existing methods which
define a "diversity measure".
  The heart of the method is a combinatorial bound on the number of mistakes
the ensemble is likely to make. The bound can be efficiently approximated in
time linear in the number of samples. Thus allowing an efficient search for a
combination of classifiers that are likely to produce higher joint accuracy.
Moreover, having the bound applicable to unlabeled data makes it both accurate
and practical in modern setting of unsupervised learning. We demonstrate the
method on popular large-scale face recognition datasets which provide a useful
playground for fine-grain classification tasks using noisy data over many
classes.
  The proposed framework fits neatly in trending practices of unsupervised
learning. It is a measure of the inherent independence of a set of classifiers
not relying on extra information such as another classifier or labeled data.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: The Hyperdimensional Transform for Distributional Modelling, Regression and Classification
Abstract: Hyperdimensional computing (HDC) is an increasingly popular computing
paradigm with immense potential for future intelligent applications. Although
the main ideas already took form in the 1990s, HDC recently gained significant
attention, especially in the field of machine learning and data science. Next
to efficiency, interoperability and explainability, HDC offers attractive
properties for generalization as it can be seen as an attempt to combine
connectionist ideas from neural networks with symbolic aspects. In recent work,
we introduced the hyperdimensional transform, revealing deep theoretical
foundations for representing functions and distributions as high-dimensional
holographic vectors. Here, we present the power of the hyperdimensional
transform to a broad data science audience. We use the hyperdimensional
transform as a theoretical basis and provide insight into state-of-the-art HDC
approaches for machine learning. We show how existing algorithms can be
modified and how this transform can lead to a novel, well-founded toolbox. Next
to the standard regression and classification tasks of machine learning, our
discussion includes various aspects of statistical modelling, such as
representation, learning and deconvolving distributions, sampling, Bayesian
inference, and uncertainty estimation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Are We Falling in a Middle-Intelligence Trap? An Analysis and Mitigation of the Reversal Curse
Abstract: Recent studies have highlighted a phenomenon in large language models (LLMs)
known as "the reversal curse," in which the order of knowledge entities in the
training data biases the models' comprehension. For example, if a model is
trained on sentences where entity A consistently appears before entity B, it
can respond to queries about A by providing B as the answer. However, it may
encounter confusion when presented with questions concerning B. We contend that
the reversal curse is partially a result of specific model training objectives,
particularly evident in the prevalent use of the next-token prediction within
most causal language models. For the next-token prediction, models solely focus
on a token's preceding context, resulting in a restricted comprehension of the
input. In contrast, we illustrate that the GLM, trained using the
autoregressive blank infilling objective where tokens to be predicted have
access to the entire context, exhibits better resilience against the reversal
curse. We propose a novel training method, BIdirectional Casual language
modeling Optimization (BICO), designed to mitigate the reversal curse when
fine-tuning pretrained causal language models on new data. BICO modifies the
causal attention mechanism to function bidirectionally and employs a mask
denoising optimization. In the task designed to assess the reversal curse, our
approach improves Llama's accuracy from the original 0% to around 70%. We hope
that more attention can be focused on exploring and addressing these inherent
weaknesses of the current LLMs, in order to achieve a higher level of
intelligence.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)
Abstract: Acuity assessments are vital in critical care settings to provide timely
interventions and fair resource allocation. Traditional acuity scores rely on
manual assessments and documentation of physiological states, which can be
time-consuming, intermittent, and difficult to use for healthcare providers.
Furthermore, such scores do not incorporate granular information such as
patients' mobility level, which can indicate recovery or deterioration in the
ICU. We hypothesized that existing acuity scores could be potentially improved
by employing Artificial Intelligence (AI) techniques in conjunction with
Electronic Health Records (EHR) and wearable sensor data. In this study, we
evaluated the impact of integrating mobility data collected from wrist-worn
accelerometers with clinical data obtained from EHR for developing an AI-driven
acuity assessment score. Accelerometry data were collected from 86 patients
wearing accelerometers on their wrists in an academic hospital setting. The
data was analyzed using five deep neural network models: VGG, ResNet,
MobileNet, SqueezeNet, and a custom Transformer network. These models
outperformed a rule-based clinical score (SOFA= Sequential Organ Failure
Assessment) used as a baseline, particularly regarding the precision,
sensitivity, and F1 score. The results showed that while a model relying solely
on accelerometer data achieved limited performance (AUC 0.50, Precision 0.61,
and F1-score 0.68), including demographic information with the accelerometer
data led to a notable enhancement in performance (AUC 0.69, Precision 0.75, and
F1-score 0.67). This work shows that the combination of mobility and patient
information can successfully differentiate between stable and unstable states
in critically ill patients.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Task-Distributionally Robust Data-Free Meta-Learning
Abstract: Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by
leveraging multiple pre-trained models without requiring their original
training data. Existing inversion-based DFML methods construct pseudo tasks
from a learnable dataset, which is inversely generated from the pre-trained
model pool. For the first time, we reveal two major challenges hindering their
practical deployments: Task-Distribution Shift (TDS) and Task-Distribution
Corruption (TDC). TDS leads to a biased meta-learner because of the skewed task
distribution towards newly generated tasks. TDC occurs when untrusted models
characterized by misleading labels or poor quality pollute the task
distribution. To tackle these issues, we introduce a robust DFML framework that
ensures task distributional robustness. We propose to meta-learn from a pseudo
task distribution, diversified through task interpolation within a compact
task-memory buffer. This approach reduces the meta-learner's overreliance on
newly generated tasks by maintaining consistent performance across a broader
range of interpolated memory tasks, thus ensuring its generalization for unseen
tasks. Additionally, our framework seamlessly incorporates an automated model
selection mechanism into the meta-training phase, parameterizing each model's
reliability as a learnable weight. This is optimized with a policy gradient
algorithm inspired by reinforcement learning, effectively addressing the
non-differentiable challenge posed by model selection. Comprehensive
experiments across various datasets demonstrate the framework's effectiveness
in mitigating TDS and TDC, underscoring its potential to improve DFML in
real-world scenarios.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LongBoX: Evaluating Transformers on Long-Sequence Clinical Tasks
Abstract: Many large language models (LLMs) for medicine have largely been evaluated on
short texts, and their ability to handle longer sequences such as a complete
electronic health record (EHR) has not been systematically explored. Assessing
these models on long sequences is crucial since prior work in the general
domain has demonstrated performance degradation of LLMs on longer texts.
Motivated by this, we introduce LongBoX, a collection of seven medical datasets
in text-to-text format, designed to investigate model performance on long
sequences. Preliminary experiments reveal that both medical LLMs (e.g., BioGPT)
and strong general domain LLMs (e.g., FLAN-T5) struggle on this benchmark. We
further evaluate two techniques designed for long-sequence handling: (i)
local-global attention, and (ii) Fusion-in-Decoder (FiD). Our results
demonstrate mixed results with long-sequence handling - while scores on some
datasets increase, there is substantial room for improvement. We hope that
LongBoX facilitates the development of more effective long-sequence techniques
for the medical domain. Data and source code are available at
https://github.com/Mihir3009/LongBoX.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: The Impact of Preference Agreement in Reinforcement Learning from Human Feedback: A Case Study in Summarization
Abstract: Reinforcement Learning from Human Feedback (RLHF) can be used to capture
complex and nuanced properties of text generation quality. As a result, the
task of text summarization has been identified as a good candidate for this
process. In this paper, we explore how preference agreement impacts the
efficacy of RLHF for summarization. We show that sampling human preferences to
include a range of annotator agreement results in (1) higher accuracy reward
models and (2) alters the characteristics of quality captured. We additionally
show improvements in downstream generation when using a reward model trained
with a range of preference agreements. Our contributions have implications for
the design of synthetic datasets as well as the importance of considering
quality differentials in comparison-based data.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: From Indeterminacy to Determinacy: Augmenting Logical Reasoning Capabilities with Large Language Models
Abstract: Recent advances in LLMs have revolutionized the landscape of reasoning tasks.
To enhance the capabilities of LLMs to emulate human reasoning, prior works
focus on modeling reasoning steps using specific thought structures like
chains, trees, or graphs. However, LLM-based reasoning continues to encounter
three challenges: 1) Selecting appropriate reasoning structures for various
tasks; 2) Exploiting known conditions sufficiently and efficiently to deduce
new insights; 3) Considering the impact of historical reasoning experience. To
address these challenges, we propose DetermLR, a novel reasoning framework that
formulates the reasoning process as a transformational journey from
indeterminate premises to determinate ones. This process is marked by the
incremental accumulation of determinate premises, making the conclusion
progressively closer to clarity. DetermLR includes three essential components:
1) Premise identification: We categorize premises into two distinct types:
determinate and indeterminate. This empowers LLMs to customize reasoning
structures to match the specific task complexities. 2) Premise prioritization
and exploration: We leverage quantitative measurements to assess the relevance
of each premise to the target, prioritizing more relevant premises for
exploring new insights. 3) Iterative process with reasoning memory: We
introduce a reasoning memory module to automate storage and extraction of
available premises and reasoning paths, preserving historical reasoning details
for more accurate premise prioritization. Comprehensive experimental results
show that DetermLR outperforms all baselines on four challenging logical
reasoning tasks: LogiQA, ProofWriter, FOLIO, and LogicalDeduction. DetermLR can
achieve better reasoning performance while requiring fewer visited states,
highlighting its superior efficiency and effectiveness in tackling logical
reasoning tasks.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition
Abstract: Implicit Discourse Relation Recognition (IDRR), which infers discourse
relations without the help of explicit connectives, is still a crucial and
challenging task for discourse parsing. Recent works tend to exploit the
hierarchical structure information from the annotated senses, which demonstrate
enhanced discourse relation representations can be obtained by integrating
sense hierarchy. Nevertheless, the performance and robustness for IDRR are
significantly constrained by the availability of annotated data. Fortunately,
there is a wealth of unannotated utterances with explicit connectives, that can
be utilized to acquire enriched discourse relation features. In light of such
motivation, we propose a Prompt-based Logical Semantics Enhancement (PLSE)
method for IDRR. Essentially, our method seamlessly injects knowledge relevant
to discourse relation into pre-trained language models through prompt-based
connective prediction. Furthermore, considering the prompt-based connective
prediction exhibits local dependencies due to the deficiency of masked language
model (MLM) in capturing global semantics, we design a novel self-supervised
learning objective based on mutual information maximization to derive enhanced
representations of logical semantics for IDRR. Experimental results on PDTB 2.0
and CoNLL16 datasets demonstrate that our method achieves outstanding and
consistent performance against the current state-of-the-art models.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Exploring the Robustness of Model-Graded Evaluations and Automated Interpretability
Abstract: There has been increasing interest in evaluations of language models for a
variety of risks and characteristics. Evaluations relying on natural language
understanding for grading can often be performed at scale by using other
language models. We test the robustness of these model-graded evaluations to
injections on different datasets including a new Deception Eval. These
injections resemble direct communication between the testee and the evaluator
to change their grading. We extrapolate that future, more intelligent models
might manipulate or cooperate with their evaluation model. We find significant
susceptibility to these injections in state-of-the-art commercial models on all
examined evaluations. Furthermore, similar injections can be used on automated
interpretability frameworks to produce misleading model-written explanations.
The results inspire future work and should caution against unqualified trust in
evaluations and automated interpretability.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Addressing Membership Inference Attack in Federated Learning with Model Compression
Abstract: Federated Learning (FL) has been proposed as a privacy-preserving solution
for machine learning. However, recent works have shown that Federated Learning
can leak private client data through membership attacks. In this paper, we show
that the effectiveness of these attacks on the clients negatively correlates
with the size of the client datasets and model complexity. Based on this
finding, we propose model-agnostic Federated Learning as a privacy-enhancing
solution because it enables the use of models of varying complexity in the
clients. To this end, we present $\texttt{MaPP-FL}$, a novel privacy-aware FL
approach that leverages model compression on the clients while keeping a full
model on the server. We compare the performance of $\texttt{MaPP-FL}$ against
state-of-the-art model-agnostic FL methods on the CIFAR-10, CIFAR-100, and
FEMNIST vision datasets. Our experiments show the effectiveness of
$\texttt{MaPP-FL}$ in preserving the clients' and the server's privacy while
achieving competitive classification accuracies.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction
Abstract: Knowledge graphs are often used to represent structured information in a
flexible and efficient manner, but their use in situated dialogue remains
under-explored. This paper presents a novel conversational model for
human--robot interaction that rests upon a graph-based representation of the
dialogue state. The knowledge graph representing the dialogue state is
continuously updated with new observations from the robot sensors, including
linguistic, situated and multimodal inputs, and is further enriched by other
modules, in particular for spatial understanding. The neural conversational
model employed to respond to user utterances relies on a simple but effective
graph-to-text mechanism that traverses the dialogue state graph and converts
the traversals into a natural language form. This conversion of the state graph
into text is performed using a set of parameterized functions, and the values
for those parameters are optimized based on a small set of Wizard-of-Oz
interactions. After this conversion, the text representation of the dialogue
state graph is included as part of the prompt of a large language model used to
decode the agent response. The proposed approach is empirically evaluated
through a user study with a humanoid robot that acts as conversation partner to
evaluate the impact of the graph-to-text mechanism on the response generation.
After moving a robot along a tour of an indoor environment, participants
interacted with the robot using spoken dialogue and evaluated how well the
robot was able to answer questions about what the robot observed during the
tour. User scores show a statistically significant improvement in the perceived
factuality of the robot responses when the graph-to-text approach is employed,
compared to a baseline using inputs structured as semantic triples.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms
Abstract: Image segmentation algorithms can be understood as a collection of pixel
classifiers, for which the outcomes of nearby pixels are correlated. Classifier
models can be calibrated using Inductive Conformal Prediction, but this
requires holding back a sufficiently large calibration dataset for computing
the distribution of non-conformity scores of the model's predictions. If one
only requires only marginal calibration on the image level, this calibration
set consists of all individual pixels in the images available for calibration.
However, if the goal is to attain proper calibration for each individual pixel
classifier, the calibration set consists of individual images. In a scenario
where data are scarce (such as the medical domain), it may not always be
possible to set aside sufficiently many images for this pixel-level
calibration. The method we propose, dubbed ``Kandinsky calibration'', makes use
of the spatial structure present in the distribution of natural images to
simultaneously calibrate the classifiers of ``similar'' pixels. This can be
seen as an intermediate approach between marginal (imagewise) and conditional
(pixelwise) calibration, where non-conformity scores are aggregated over
similar image regions, thereby making more efficient use of the images
available for calibration. We run experiments on segmentation algorithms
trained and calibrated on subsets of the public MS-COCO and Medical Decathlon
datasets, demonstrating that Kandinsky calibration method can significantly
improve the coverage. When compared to both pixelwise and imagewise calibration
on little data, the Kandinsky method achieves much lower coverage errors,
indicating the data efficiency of the Kandinsky calibration.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: CZL-CIAE: CLIP-driven Zero-shot Learning for Correcting Inverse Age Estimation
Abstract: Zero-shot age estimation aims to learn feature information about age from
input images and make inferences about a given person's image or video frame
without specific sample data. The development of zero-shot age estimation can
improve the efficiency and accuracy of various applications (e.g., age
verification and secure access control, etc.), while also promoting research on
multi-modal and zero-shot learning in the social media field. For example,
zero-sample age estimation can be used to create social networks focused on
specific age groups. However, existing methods mainly focus on supervised,
labeled age estimation learning, and the prediction effect of zero-shot
learning is very poor. To tackle the above issues, we propose a novel
CLIP-driven Zero-shot Learning for Correcting Inverse Age Estimation
(CZL-CIAE). Specifically, we first introduce the CLIP model to extract image
features and text semantic information respectively, and map them into a highly
semantically aligned high-dimensional feature space. Next, we designed a new
Transformer architecture (i.e., FourierFormer) to achieve channel evolution and
spatial interaction of images, and to fuse image and text semantic information.
Finally, we introduce reversible age estimation, which uses end-to-end error
feedback to reduce the error rate of age predictions. Through extensive
experiments on multiple data sets, CZL-CIAE has achieved better age prediction
results.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Labels Need Prompts Too: Mask Matching for Natural Language Understanding Tasks
Abstract: Textual label names (descriptions) are typically semantically rich in many
natural language understanding (NLU) tasks. In this paper, we incorporate the
prompting methodology, which is widely used to enrich model input, into the
label side for the first time. Specifically, we propose a Mask Matching method,
which equips an input with a prompt and its label with another, and then makes
predictions by matching their mask representations. We evaluate our method
extensively on 8 NLU tasks with 14 datasets. The experimental results show that
Mask Matching significantly outperforms its counterparts of fine-tuning and
conventional prompt-tuning, setting up state-of-the-art performances in several
datasets. Mask Matching is particularly good at handling NLU tasks with large
label counts and informative label names. As pioneering efforts that
investigate the label-side prompt, we also discuss open issues for future
study.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Boosting LLM Reasoning: Push the Limits of Few-shot Learning with Reinforced In-Context Pruning
Abstract: Large language models (LLMs) have shown impressive capabilities in various
tasks, yet they still struggle with math reasoning. Despite efforts to optimize
Chain-of-Thoughts (CoT) prompts and fine-tune LLMs, the potential of few-shot
learning remains unexplored. In this work, we propose CoT-Max, a novel approach
pushing the boundaries of few-shot CoT learning to improve LLM math reasoning
capabilities. CoT-Max addresses the challenges of the selection of useful
examples and limited number of examples due to restricted context window
length. Inspired by our observation that natural language inputs contain many
redundancy, we propose a coarse-to-fine pruner as a plug-and-play module for
LLMs, which first identifies crucial CoT examples from a large batch and then
further prunes unimportant tokens. To train the pruner, we collect a math
reasoning dataset with diverse difficulty and steps, introduce a reward to
measure both the input's effectiveness for math reasoning and token length
constraints, and propose a novel training approach with reinforcement learning.
As a result, CoT-Max significantly outperforms CoT and few-shot prompting
baselines across various LLMs (LLaMA2-7B, 13B, 70B) and 5 mathematical
datasets, achieving up to 4.55% absolute improvements. Remarkably, without any
fine-tuning, LLaMA2-70B with CoT-Max surpasses GPT-3.5 and a wide range of
larger LLMs (PaLM, Minerva, etc.) on the GSM8K.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression
Abstract: We consider the problem of predicting how the likelihood of an outcome of
interest for a patient changes over time as we observe more of the patient
data. To solve this problem, we propose a supervised contrastive learning
framework that learns an embedding representation for each time step of a
patient time series. Our framework learns the embedding space to have the
following properties: (1) nearby points in the embedding space have similar
predicted class probabilities, (2) adjacent time steps of the same time series
map to nearby points in the embedding space, and (3) time steps with very
different raw feature vectors map to far apart regions of the embedding space.
To achieve property (3), we employ a nearest neighbor pairing mechanism in the
raw feature space. This mechanism also serves as an alternative to data
augmentation, a key ingredient of contrastive learning, which lacks a standard
procedure that is adequately realistic for clinical tabular data, to our
knowledge. We demonstrate that our approach outperforms state-of-the-art
baselines in predicting mortality of septic patients (MIMIC-III dataset) and
tracking progression of cognitive impairment (ADNI dataset). Our method also
consistently recovers the correct synthetic dataset embedding structure across
experiments, a feat not achieved by baselines. Our ablation experiments show
the pivotal role of our nearest neighbor pairing.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Introducing NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies
Abstract: Single cell analysis of skeletal muscle (SM) tissue is a fundamental tool for
understanding many neuromuscular disorders. For this analysis to be reliable
and reproducible, identification of individual fibres within microscopy images
(segmentation) of SM tissue should be precise. There is currently no tool or
pipeline that makes automatic and precise segmentation and curation of images
of SM tissue cross-sections possible. Biomedical scientists in this field rely
on custom tools and general machine learning (ML) models, both followed by
labour intensive and subjective manual interventions to get the segmentation
right. We believe that automated, precise, reproducible segmentation is
possible by training ML models. However, there are currently no good quality,
publicly available annotated imaging datasets available for ML model training.
In this paper we release NCL-SM: a high quality bioimaging dataset of 46 human
tissue sections from healthy control subjects and from patients with
genetically diagnosed muscle pathology. These images include $>$ 50k manually
segmented muscle fibres (myofibres). In addition we also curated high quality
myofibres and annotated reasons for rejecting low quality myofibres and regions
in SM tissue images, making this data completely ready for downstream analysis.
This, we believe, will pave the way for development of a fully automatic
pipeline that identifies individual myofibres within images of tissue sections
and, in particular, also classifies individual myofibres that are fit for
further analysis.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: No Prior Mask: Eliminate Redundant Action for Deep Reinforcement Learning
Abstract: The large action space is one fundamental obstacle to deploying Reinforcement
Learning methods in the real world. The numerous redundant actions will cause
the agents to make repeated or invalid attempts, even leading to task failure.
Although current algorithms conduct some initial explorations for this issue,
they either suffer from rule-based systems or depend on expert demonstrations,
which significantly limits their applicability in many real-world settings. In
this work, we examine the theoretical analysis of what action can be eliminated
in policy optimization and propose a novel redundant action filtering
mechanism. Unlike other works, our method constructs the similarity factor by
estimating the distance between the state distributions, which requires no
prior knowledge. In addition, we combine the modified inverse model to avoid
extensive computation in high-dimensional state space. We reveal the underlying
structure of action spaces and propose a simple yet efficient redundant action
filtering mechanism named No Prior Mask (NPM) based on the above techniques. We
show the superior performance of our method by conducting extensive experiments
on high-dimensional, pixel-input, and stochastic problems with various action
redundancy. Our code is public online at https://github.com/zhongdy15/npm.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
Abstract: The recent end-to-end neural solvers have shown promise for small-scale
routing problems but suffered from limited real-time scaling-up performance.
This paper proposes GLOP (Global and Local Optimization Policies), a unified
hierarchical framework that efficiently scales toward large-scale routing
problems. GLOP partitions large routing problems into Travelling Salesman
Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first
time, we hybridize non-autoregressive neural heuristics for coarse-grained
problem partitions and autoregressive neural heuristics for fine-grained route
constructions, leveraging the scalability of the former and the meticulousness
of the latter. Experimental results show that GLOP achieves competitive and
state-of-the-art real-time performance on large-scale routing problems,
including TSP, ATSP, CVRP, and PCTSP.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Classification of Human- and AI-Generated Texts for English, French, German, and Spanish
Abstract: In this paper we analyze features to classify human- and AI-generated text
for English, French, German and Spanish and compare them across languages. We
investigate two scenarios: (1) The detection of text generated by AI from
scratch, and (2) the detection of text rephrased by AI. For training and
testing the classifiers in this multilingual setting, we created a new text
corpus covering 10 topics for each language. For the detection of AI-generated
text, the combination of all proposed features performs best, indicating that
our features are portable to other related languages: The F1-scores are close
with 99% for Spanish, 98% for English, 97% for German and 95% for French. For
the detection of AI-rephrased text, the systems with all features outperform
systems with other features in many cases, but using only document features
performs best for German (72%) and Spanish (86%) and only text vector features
leads to best results for English (78%).

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Amodal Optical Flow
Abstract: Optical flow estimation is very challenging in situations with transparent or
occluded objects. In this work, we address these challenges at the task level
by introducing Amodal Optical Flow, which integrates optical flow with amodal
perception. Instead of only representing the visible regions, we define amodal
optical flow as a multi-layered pixel-level motion field that encompasses both
visible and occluded regions of the scene. To facilitate research on this new
task, we extend the AmodalSynthDrive dataset to include pixel-level labels for
amodal optical flow estimation. We present several strong baselines, along with
the Amodal Flow Quality metric to quantify the performance in an interpretable
manner. Furthermore, we propose the novel AmodalFlowNet as an initial step
toward addressing this task. AmodalFlowNet consists of a transformer-based
cost-volume encoder paired with a recurrent transformer decoder which
facilitates recurrent hierarchical feature propagation and amodal semantic
grounding. We demonstrate the tractability of amodal optical flow in extensive
experiments and show its utility for downstream tasks such as panoptic
tracking. We make the dataset, code, and trained models publicly available at
http://amodal-flow.cs.uni-freiburg.de.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: ViR: Vision Retention Networks
Abstract: Vision Transformers (ViTs) have attracted a lot of popularity in recent
years, due to their exceptional capabilities in modeling long-range spatial
dependencies and scalability for large scale training. Although the training
parallelism of self-attention mechanism plays an important role in retaining
great performance, its quadratic complexity baffles the application of ViTs in
many scenarios which demand fast inference. This effect is even more pronounced
in applications in which autoregressive modeling of input features is required.
In Natural Language Processing (NLP), a new stream of efforts have proposed
parallelizable models with recurrent formulation that allows for efficient
inference in generative applications. Inspired by this trend, we propose a new
class of computer vision models, dubbed Vision Retention Networks (ViR), with
dual parallel and recurrent formulations, which strike an optimal balance
between fast inference and parallel training with competitive performance. In
particular, ViR scales favorably for image throughput and memory consumption in
tasks that require higher-resolution images due to its flexible formulation in
processing large sequence lengths. The ViR is the first attempt to realize dual
parallel and recurrent equivalency in a general vision backbone for recognition
tasks. We have validated the effectiveness of ViR through extensive experiments
with different dataset sizes and various image resolutions and achieved
competitive performance. Our code and pretrained models will be made publicly
available.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism
Abstract: Transformer-based Large Language Models (LLMs) are the state-of-the-art for
natural language tasks. Recent work has attempted to decode, by reverse
engineering the role of linear layers, the internal mechanisms by which LLMs
arrive at their final predictions for text completion tasks. Yet little is
known about the specific role of attention heads in producing the final token
prediction. We propose Attention Lens, a tool that enables researchers to
translate the outputs of attention heads into vocabulary tokens via learned
attention-head-specific transformations called lenses. Preliminary findings
from our trained lenses indicate that attention heads play highly specialized
roles in language models. The code for Attention Lens is available at
github.com/msakarvadia/AttentionLens.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Uncertainty in Additive Feature Attribution methods
Abstract: In this work, we explore various topics that fall under the umbrella of
Uncertainty in post-hoc Explainable AI (XAI) methods. We in particular focus on
the class of additive feature attribution explanation methods. We first
describe our specifications of uncertainty and compare various statistical and
recent methods to quantify the same. Next, for a particular instance, we study
the relationship between a feature's attribution and its uncertainty and
observe little correlation. As a result, we propose a modification in the
distribution from which perturbations are sampled in LIME-based algorithms such
that the important features have minimal uncertainty without an increase in
computational cost. Next, while studying how the uncertainty in explanations
varies across the feature space of a classifier, we observe that a fraction of
instances show near-zero uncertainty. We coin the term "stable instances" for
such instances and diagnose factors that make an instance stable. Next, we
study how an XAI algorithm's uncertainty varies with the size and complexity of
the underlying model. We observe that the more complex the model, the more
inherent uncertainty is exhibited by it. As a result, we propose a measure to
quantify the relative complexity of a blackbox classifier. This could be
incorporated, for example, in LIME-based algorithms' sampling densities, to
help different explanation algorithms achieve tighter confidence levels.
Together, the above measures would have a strong impact on making XAI models
relatively trustworthy for the end-user as well as aiding scientific discovery.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Towards Autonomous Hypothesis Verification via Language Models with Minimal Guidance
Abstract: Research automation efforts usually employ AI as a tool to automate specific
tasks within the research process. To create an AI that truly conduct research
themselves, it must independently generate hypotheses, design verification
plans, and execute verification. Therefore, we investigated if an AI itself
could autonomously generate and verify hypothesis for a toy machine learning
research problem. We prompted GPT-4 to generate hypotheses and Python code for
hypothesis verification with limited methodological guidance. Our findings
suggest that, in some instances, GPT-4 can autonomously generate and validate
hypotheses without detailed guidance. While this is a promising result, we also
found that none of the verifications were flawless, and there remain
significant challenges in achieving autonomous, human-level research using only
generic instructions. These findings underscore the need for continued
exploration to develop a general and autonomous AI researcher.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Score Normalization for a Faster Diffusion Exponential Integrator Sampler
Abstract: Recently, Zhang et al. have proposed the Diffusion Exponential Integrator
Sampler (DEIS) for fast generation of samples from Diffusion Models. It
leverages the semi-linear nature of the probability flow ordinary differential
equation (ODE) in order to greatly reduce integration error and improve
generation quality at low numbers of function evaluations (NFEs). Key to this
approach is the score function reparameterisation, which reduces the
integration error incurred from using a fixed score function estimate over each
integration step. The original authors use the default parameterisation used by
models trained for noise prediction -- multiply the score by the standard
deviation of the conditional forward noising distribution. We find that
although the mean absolute value of this score parameterisation is close to
constant for a large portion of the reverse sampling process, it changes
rapidly at the end of sampling. As a simple fix, we propose to instead
reparameterise the score (at inference) by dividing it by the average absolute
value of previous score estimates at that time step collected from offline high
NFE generations. We find that our score normalisation (DEIS-SN) consistently
improves FID compared to vanilla DEIS, showing an improvement at 10 NFEs from
6.44 to 5.57 on CIFAR-10 and from 5.9 to 4.95 on LSUN-Church 64x64. Our code is
available at https://github.com/mtkresearch/Diffusion-DEIS-SN

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Multitask Multimodal Prompted Training for Interactive Embodied Task Completion
Abstract: Interactive and embodied tasks pose at least two fundamental challenges to
existing Vision & Language (VL) models, including 1) grounding language in
trajectories of actions and observations, and 2) referential disambiguation. To
tackle these challenges, we propose an Embodied MultiModal Agent (EMMA): a
unified encoder-decoder model that reasons over images and trajectories, and
casts action prediction as multimodal text generation. By unifying all tasks as
text generation, EMMA learns a language of actions which facilitates transfer
across tasks. Different to previous modular approaches with independently
trained components, we use a single multitask model where each task contributes
to goal completion. EMMA performs on par with similar models on several VL
benchmarks and sets a new state-of-the-art performance (36.81% success rate) on
the Dialog-guided Task Completion (DTC), a benchmark to evaluate dialog-guided
agents in the Alexa Arena

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Fine-tuning Language Models for Factuality
Abstract: The fluency and creativity of large pre-trained language models (LLMs) have
led to their widespread use, sometimes even as a replacement for traditional
search engines. Yet language models are prone to making convincing but
factually inaccurate claims, often referred to as 'hallucinations.' These
errors can inadvertently spread misinformation or harmfully perpetuate
misconceptions. Further, manual fact-checking of model responses is a
time-consuming process, making human factuality labels expensive to acquire. In
this work, we fine-tune language models to be more factual, without human
labeling and targeting more open-ended generation settings than past work. We
leverage two key recent innovations in NLP to do so. First, several recent
works have proposed methods for judging the factuality of open-ended text by
measuring consistency with an external knowledge base or simply a large model's
confidence scores. Second, the direct preference optimization algorithm enables
straightforward fine-tuning of language models on objectives other than
supervised imitation, using a preference ranking over possible model responses.
We show that learning from automatically generated factuality preference
rankings, generated either through existing retrieval systems or our novel
retrieval-free approach, significantly improves the factuality (percent of
generated claims that are correct) of Llama-2 on held-out topics compared with
RLHF or decoding strategies targeted at factuality. At 7B scale, compared to
Llama-2-chat, we observe 58% and 40% reduction in factual error rate when
generating biographies and answering medical questions, respectively.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks
Abstract: In this thesis, we develop theoretical, algorithmic and experimental
contributions for Machine Learning with limited labels, and more specifically
for the tasks of Image Classification and Object Detection in Computer Vision.
In a first contribution, we are interested in bridging the gap between theory
and practice for popular Meta-Learning algorithms used in Few-Shot
Classification. We make connections to Multi-Task Representation Learning,
which benefits from solid theoretical foundations, to verify the best
conditions for a more efficient meta-learning. Then, to leverage unlabeled data
when training object detectors based on the Transformer architecture, we
propose both an unsupervised pretraining and a semi-supervised learning method
in two other separate contributions. For pretraining, we improve Contrastive
Learning for object detectors by introducing the localization information.
Finally, our semi-supervised method is the first tailored to transformer-based
detectors.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text
Abstract: While Large Language Models (LLMs) have achieved remarkable performance in
many tasks, much about their inner workings remains unclear. In this study, we
present novel experimental insights into the resilience of LLMs, particularly
GPT-4, when subjected to extensive character-level permutations. To investigate
this, we first propose the Scrambled Bench, a suite designed to measure the
capacity of LLMs to handle scrambled input, in terms of both recovering
scrambled sentences and answering questions given scrambled context. The
experimental results indicate that most powerful LLMs demonstrate the
capability akin to typoglycemia, a phenomenon where humans can understand the
meaning of words even when the letters within those words are scrambled, as
long as the first and last letters remain in place. More surprisingly, we found
that only GPT-4 nearly flawlessly processes inputs with unnatural errors, even
under the extreme condition, a task that poses significant challenges for other
LLMs and often even for humans. Specifically, GPT-4 can almost perfectly
reconstruct the original sentences from scrambled ones, decreasing the edit
distance by 95%, even when all letters within each word are entirely scrambled.
It is counter-intuitive that LLMs can exhibit such resilience despite severe
disruption to input tokenization caused by scrambled text.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation
Abstract: Achieving empathy is a crucial step toward humanized dialogue systems.
Current approaches for empathetic dialogue generation mainly perceive an
emotional label to generate an empathetic response conditioned on it, which
simply treat emotions independently, but ignore the intrinsic emotion
correlation in dialogues, resulting in inaccurate emotion perception and
unsuitable response generation. In this paper, we propose a novel emotion
correlation enhanced empathetic dialogue generation framework, which
comprehensively realizes emotion correlation learning, utilization, and
supervising. Specifically, a multi-resolution emotion graph is devised to
capture context-based emotion interactions from different resolutions, further
modeling emotion correlation. Then we propose an emotion correlation enhanced
decoder, with a novel correlation-aware aggregation and soft/hard strategy,
respectively improving the emotion perception and response generation.
Experimental results on the benchmark dataset demonstrate the superiority of
our model in both empathetic perception and expression.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Towards More Likely Models for AI Planning
Abstract: This is the first work to look at the application of large language models
(LLMs) for the purpose of model space edits in automated planning tasks. To set
the stage for this sangam, we explore two different flavors of model space
problems that have been studied in the AI planning literature and explore the
effect of an LLM on those tasks. We empirically demonstrate how the performance
of an LLM contrasts with combinatorial search (CS) - an approach that has been
traditionally used to solve model space tasks in planning, both with the LLM in
the role of a standalone model space reasoner as well as in the role of a
statistical signal in concert with the CS approach as part of a two-stage
process. Our experiments show promising results suggesting further forays of
LLMs into the exciting world of model space reasoning for planning tasks in the
future.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation
Abstract: Understanding causality is a core aspect of intelligence. The Event Causality
Identification with Causal News Corpus Shared Task addresses two aspects of
this challenge: Subtask 1 aims at detecting causal relationships in texts, and
Subtask 2 requires identifying signal words and the spans that refer to the
cause or effect, respectively. Our system, which is based on pre-trained
transformers, stacked sequence tagging, and synthetic data augmentation, ranks
third in Subtask 1 and wins Subtask 2 with an F1 score of 72.8, corresponding
to a margin of 13 pp. to the second-best system.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Hybrid Minimax-MCTS and Difficulty Adjustment for General Game Playing
Abstract: Board games are a great source of entertainment for all ages, as they create
a competitive and engaging environment, as well as stimulating learning and
strategic thinking. It is common for digital versions of board games, as any
other type of digital games, to offer the option to select the difficulty of
the game. This is usually done by customizing the search parameters of the AI
algorithm. However, this approach cannot be extended to General Game Playing
agents, as different games might require different parametrization for each
difficulty level. In this paper, we present a general approach to implement an
artificial intelligence opponent with difficulty levels for zero-sum games,
together with a propose of a Minimax-MCTS hybrid algorithm, which combines the
minimax search process with GGP aspects of MCTS. This approach was tested in
our mobile application LoBoGames, an extensible board games platform, that is
intended to have an broad catalog of games, with an emphasis on accessibility:
the platform is friendly to visually-impaired users, and is compatible with
more than 92\% of Android devices. The tests in this work indicate that both
the hybrid Minimax-MCTS and the new difficulty adjustment system are promising
GGP approaches that could be expanded in future work.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Fine-Tuning Language Models Using Formal Methods Feedback
Abstract: Although pre-trained language models encode generic knowledge beneficial for
planning and control, they may fail to generate appropriate control policies
for domain-specific tasks. Existing fine-tuning methods use human feedback to
address this limitation, however, sourcing human feedback is labor intensive
and costly. We present a fully automated approach to fine-tune pre-trained
language models for applications in autonomous systems, bridging the gap
between generic knowledge and domain-specific requirements while reducing cost.
The method synthesizes automaton-based controllers from pre-trained models
guided by natural language task descriptions. These controllers are verifiable
against independently provided specifications within a world model, which can
be abstract or obtained from a high-fidelity simulator. Controllers with high
compliance with the desired specifications receive higher ranks, guiding the
iterative fine-tuning process. We provide quantitative evidences, primarily in
autonomous driving, to demonstrate the method's effectiveness across multiple
tasks. The results indicate an improvement in percentage of specifications
satisfied by the controller from 60% to 90%.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Adversarial Preference Optimization
Abstract: Human preference alignment is a crucial training step to improve the
interaction quality of large language models (LLMs). Existing aligning methods
depend on manually annotated preference data to guide the LLM optimization
directions. However, in practice, continuously updating LLMs raises a
distribution gap between model-generated samples and human-preferred responses,
which hinders model fine-tuning efficiency. To mitigate this issue, previous
methods require additional preference annotation on generated samples to adapt
the shifted distribution, which consumes a large amount of annotation
resources. Targeting more efficient human preference optimization, we propose
an adversarial preference optimization (APO) framework, where the LLM agent and
the preference model update alternatively via a min-max game. Without
additional annotation, our APO method can make a self-adaption to the
generation distribution gap through the adversarial learning process. In
experiments, we empirically verify the effectiveness of APO in improving LLM's
helpfulness and harmlessness compared with rejection sampling baselines.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections
Abstract: In today's technologically driven world, the spread of fake news,
particularly during crucial events such as elections, presents an increasing
challenge to the integrity of information. To address this challenge, we
introduce FakeWatch ElectionShield, an innovative framework carefully designed
to detect fake news. We have created a novel dataset of North American
election-related news articles through a blend of advanced language models
(LMs) and thorough human verification, for precision and relevance. We propose
a model hub of LMs for identifying fake news. Our goal is to provide the
research community with adaptable and accurate classification models in
recognizing the dynamic nature of misinformation. Extensive evaluation of fake
news classifiers on our dataset and a benchmark dataset shows our that while
state-of-the-art LMs slightly outperform the traditional ML models, classical
models are still competitive with their balance of accuracy, explainability,
and computational efficiency. This research sets the foundation for future
studies to address misinformation related to elections.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Abstract: Entropy and mutual information in neural networks provide rich information on
the learning process, but they have proven difficult to compute reliably in
high dimensions. Indeed, in noisy and high-dimensional data, traditional
estimates in ambient dimensions approach a fixed entropy and are prohibitively
hard to compute. To address these issues, we leverage data geometry to access
the underlying manifold and reliably compute these information-theoretic
measures. Specifically, we define diffusion spectral entropy (DSE) in neural
representations of a dataset as well as diffusion spectral mutual information
(DSMI) between different variables representing data. First, we show that they
form noise-resistant measures of intrinsic dimensionality and relationship
strength in high-dimensional simulated data that outperform classic Shannon
entropy, nonparametric estimation, and mutual information neural estimation
(MINE). We then study the evolution of representations in classification
networks with supervised learning, self-supervision, or overfitting. We observe
that (1) DSE of neural representations increases during training; (2) DSMI with
the class label increases during generalizable learning but stays stagnant
during overfitting; (3) DSMI with the input signal shows differing trends: on
MNIST it increases, while on CIFAR-10 and STL-10 it decreases. Finally, we show
that DSE can be used to guide better network initialization and that DSMI can
be used to predict downstream classification accuracy across 962 models on
ImageNet. The official implementation is available at
https://github.com/ChenLiu-1996/DiffusionSpectralEntropy.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Unveiling Safety Vulnerabilities of Large Language Models
Abstract: As large language models become more prevalent, their possible harmful or
inappropriate responses are a cause for concern. This paper introduces a unique
dataset containing adversarial examples in the form of questions, which we call
AttaQ, designed to provoke such harmful or inappropriate responses. We assess
the efficacy of our dataset by analyzing the vulnerabilities of various models
when subjected to it. Additionally, we introduce a novel automatic approach for
identifying and naming vulnerable semantic regions - input semantic areas for
which the model is likely to produce harmful outputs. This is achieved through
the application of specialized clustering techniques that consider both the
semantic similarity of the input attacks and the harmfulness of the model's
responses. Automatically identifying vulnerable semantic regions enhances the
evaluation of model weaknesses, facilitating targeted improvements to its
safety mechanisms and overall reliability.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs
Abstract: Large language models (LLMs) have shown impressive capabilities across
various natural language tasks. However, evaluating their alignment with human
preferences remains a challenge. To this end, we propose a comprehensive human
evaluation framework to assess LLMs' proficiency in following instructions on
diverse real-world tasks. We construct a hierarchical task tree encompassing 7
major areas covering over 200 categories and over 800 tasks, which covers
diverse capabilities such as question answering, reasoning, multiturn dialogue,
and text generation, to evaluate LLMs in a comprehensive and in-depth manner.
We also design detailed evaluation standards and processes to facilitate
consistent, unbiased judgments from human evaluators. A test set of over 3,000
instances is released, spanning different difficulty levels and knowledge
domains. Our work provides a standardized methodology to evaluate human
alignment in LLMs for both English and Chinese. We also analyze the feasibility
of automating parts of evaluation with a strong LLM (GPT-4). Our framework
supports a thorough assessment of LLMs as they are integrated into real-world
applications. We have made publicly available the task tree, TencentLLMEval
dataset, and evaluation methodology which have been demonstrated as effective
in assessing the performance of Tencent Hunyuan LLMs. By doing so, we aim to
facilitate the benchmarking of advances in the development of safe and
human-aligned LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: FRAD: Front-Running Attacks Detection on Ethereum using Ternary Classification Model
Abstract: With the evolution of blockchain technology, the issue of transaction
security, particularly on platforms like Ethereum, has become increasingly
critical. Front-running attacks, a unique form of security threat, pose
significant challenges to the integrity of blockchain transactions. In these
attack scenarios, malicious actors monitor other users' transaction activities,
then strategically submit their own transactions with higher fees. This ensures
their transactions are executed before the monitored transactions are included
in the block. The primary objective of this paper is to delve into a
comprehensive classification of transactions associated with front-running
attacks, which aims to equip developers with specific strategies to counter
each type of attack. To achieve this, we introduce a novel detection method
named FRAD (Front-Running Attacks Detection on Ethereum using Ternary
Classification Model). This method is specifically tailored for transactions
within decentralized applications (DApps) on Ethereum, enabling accurate
classification of front-running attacks involving transaction displacement,
insertion, and suppression. Our experimental validation reveals that the
Multilayer Perceptron (MLP) classifier offers the best performance in detecting
front-running attacks, achieving an impressive accuracy rate of 84.59% and
F1-score of 84.60%.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Analyzing and Improving the Training Dynamics of Diffusion Models
Abstract: Diffusion models currently dominate the field of data-driven image synthesis
with their unparalleled scaling to large datasets. In this paper, we identify
and rectify several causes for uneven and ineffective training in the popular
ADM diffusion model architecture, without altering its high-level structure.
Observing uncontrolled magnitude changes and imbalances in both the network
activations and weights over the course of training, we redesign the network
layers to preserve activation, weight, and update magnitudes on expectation. We
find that systematic application of this philosophy eliminates the observed
drifts and imbalances, resulting in considerably better networks at equal
computational complexity. Our modifications improve the previous record FID of
2.41 in ImageNet-512 synthesis to 1.81, achieved using fast deterministic
sampling.
  As an independent contribution, we present a method for setting the
exponential moving average (EMA) parameters post-hoc, i.e., after completing
the training run. This allows precise tuning of EMA length without the cost of
performing several training runs, and reveals its surprising interactions with
network architecture, training time, and guidance.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Converting and Smoothing False Negatives for Vision-Language Pre-training
Abstract: We consider the critical issue of false negatives in Vision-Language
Pre-training (VLP), a challenge that arises from the inherent many-to-many
correspondence of image-text pairs in large-scale web-crawled datasets. The
presence of false negatives can impede achieving optimal performance and even
lead to learning failures. To address this challenge, we propose a method
called COSMO (COnverting and SMOoothing false negatives) that manages the false
negative issues, especially powerful in hard negative sampling. Building upon
the recently developed GRouped mIni-baTch sampling (GRIT) strategy, our
approach consists of two pivotal components: 1) an efficient connection mining
process that identifies and converts false negatives into positives, and 2)
label smoothing for the image-text contrastive loss (ITC). Our comprehensive
experiments verify the effectiveness of COSMO across multiple downstream tasks,
emphasizing the crucial role of addressing false negatives in VLP, potentially
even surpassing the importance of addressing false positives. In addition, the
compatibility of COSMO with the recent BLIP-family model is also demonstrated.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Prompt Engineering a Prompt Engineer
Abstract: Prompt engineering is a challenging yet crucial task for optimizing the
performance of large language models (LLMs). It requires complex reasoning to
examine the model's errors, hypothesize what is missing or misleading in the
current prompt, and communicate the task with clarity. While recent works
indicate that LLMs can be meta-prompted to perform automatic prompt
engineering, their potentials may not be fully untapped due to the lack of
sufficient guidance to elicit complex reasoning capabilities in LLMs in the
meta-prompt. In this work, we investigate the problem of "prompt engineering a
prompt engineer" -- constructing a meta-prompt that more effectively guides
LLMs to perform automatic prompt engineering. We introduce and analyze key
components, such as a step-by-step reasoning template and context
specification, which lead to improved performance. In addition, inspired by
common optimization concepts such as batch size, step size and momentum, we
introduce their verbalized counterparts to the meta-prompt and investigate
their effects. Our final method, named PE2, finds a prompt that outperforms
"let's think step by step" by 6.3% on the MultiArith dataset and 3.1% on the
GSM8K dataset. To demonstrate its versatility, we apply PE2 to the Instruction
Induction benchmark, a suite of counterfactual tasks, and a lengthy, real-world
industrial prompt. In these settings, PE2 achieves strong performance and
outperforms prior automatic prompt engineering baselines. Further, we show that
PE2 makes meaningful and targeted prompt edits, amends erroneous or incomplete
prompts, and presents non-trivial counterfactual reasoning abilities.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Characterizing Large Language Model Geometry Solves Toxicity Detection and Generation
Abstract: Large Language Models~(LLMs) drive current AI breakthroughs despite very
little being known about their internal representations, e.g., how to extract a
few informative features to solve various downstream tasks. To provide a
practical and principled answer, we propose to characterize LLMs from a
geometric perspective. We obtain in closed form (i) the intrinsic dimension in
which the Multi-Head Attention embeddings are constrained to exist and (ii) the
partition and per-region affine mappings of the per-layer feedforward networks.
Our results are informative, do not rely on approximations, and are actionable.
First, we show that, motivated by our geometric interpretation, we can bypass
Llama$2$'s RLHF by controlling its embedding's intrinsic dimension through
informed prompt manipulation. Second, we derive $7$ interpretable spline
features that can be extracted from any (pre-trained) LLM layer, providing a
rich abstract representation of their inputs. Those features alone ($224$ for
Mistral-7B/Llama$2$-7B and $560$ for Llama$2$-70B) are sufficient to help solve
toxicity detection, infer the domain of the prompt, and even tackle the Jigsaw
challenge, which aims at characterizing the type of toxicity of various
prompts. Our results demonstrate how, even in large-scale regimes, exact
theoretical results can answer practical questions in language models. Code:
\url{https://github.com/RandallBalestriero/SplineLLM}.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: BaRDa: A Belief and Reasoning Dataset that Separates Factual Accuracy and Reasoning Ability
Abstract: While there are numerous benchmarks comparing the performance of modern
language models (LMs), end-task evaluations often conflate notions of *factual
accuracy* ("truth") and *reasoning ability* ("rationality", or "honesty" in the
sense of correctly reporting implications of beliefs). Our goal is a dataset
that clearly distinguishes these two notions. Our approach is to leverage and
extend a collection of human-annotated *entailment trees*, engineered to
express both good and bad chains of reasoning, and using a mixture of true and
false facts, in particular including counterfactual examples, to avoid belief
bias (also known as the "content effect"). The resulting dataset, called BaRDa,
contains 3000 entailments (1787 valid, 1213 invalid), using 6681 true and 2319
false statements. Testing on four GPT-series models,
GPT3(curie)/GPT3(davinici)/3.5/4, we find factual accuracy (truth) scores of
74.1/80.6/82.6/87.1 and reasoning accuracy scores of 63.1/78.0/71.8/79.2. This
shows the clear progression of models towards improved factual accuracy and
entailment reasoning, and the dataset provides a new benchmark that more
cleanly separates and quantifies these two notions.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Generalization in medical AI: a perspective on developing scalable models
Abstract: Over the past few years, research has witnessed the advancement of deep
learning models trained on large datasets, some even encompassing millions of
examples. While these impressive performance on their hidden test sets, they
often underperform when assessed on external datasets. Recognizing the critical
role of generalization in medical AI development, many prestigious journals now
require reporting results both on the local hidden test set as well as on
external datasets before considering a study for publication. Effectively, the
field of medical AI has transitioned from the traditional usage of a single
dataset that is split into train and test to a more comprehensive framework
using multiple datasets, some of which are used for model development (source
domain) and others for testing (target domains). However, this new experimental
setting does not necessarily resolve the challenge of generalization. This is
because of the variability encountered in intended use and specificities across
hospital cultures making the idea of universally generalizable systems a myth.
On the other hand, the systematic, and a fortiori recurrent re-calibration, of
models at the individual hospital level, although ideal, may be overoptimistic
given the legal, regulatory and technical challenges that are involved.
Re-calibration using transfer learning may not even be possible in some
instances where reference labels of target domains are not available. In this
perspective we establish a hierarchical three-level scale system reflecting the
generalization level of a medical AI algorithm. This scale better reflects the
diversity of real-world medical scenarios per which target domain data for
re-calibration of models may or not be available and if it is, may or not have
reference labels systematically available.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction
Abstract: Due to an alarming trend related to obesity affecting 93.3 million adults in
the United States alone, body mass index (BMI) and body weight have drawn
significant interest in various health monitoring applications. Consequently,
several studies have proposed self-diagnostic facial image-based BMI prediction
methods for healthy weight monitoring. These methods have mostly used
convolutional neural network (CNN) based regression baselines, such as VGG19,
ResNet50, and Efficient-NetB0, for BMI prediction from facial images. However,
the high computational requirement of these heavy-weight CNN models limits
their deployment to resource-constrained mobile devices, thus deterring weight
monitoring using smartphones. This paper aims to develop a lightweight facial
patch-based ensemble (PatchBMI-Net) for BMI prediction to facilitate the
deployment and weight monitoring using smartphones. Extensive experiments on
BMI-annotated facial image datasets suggest that our proposed PatchBMI-Net
model can obtain Mean Absolute Error (MAE) in the range [3.58, 6.51] with a
size of about 3.3 million parameters. On cross-comparison with heavyweight
models, such as ResNet-50 and Xception, trained for BMI prediction from facial
images, our proposed PatchBMI-Net obtains equivalent MAE along with the model
size reduction of about 5.4x and the average inference time reduction of about
3x when deployed on Apple-14 smartphone. Thus, demonstrating performance
efficiency as well as low latency for on-device deployment and weight
monitoring using smartphone applications.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies
Abstract: OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued
considerable interest for its potential in medical applications. Despite its
promise, recent studies and internal reviews highlight its underperformance in
specialized medical tasks. This paper explores the boundary of GPT-4V's
capabilities in medicine, particularly in processing complex imaging data from
endoscopies, CT scans, and MRIs etc. Leveraging open-source datasets, we
assessed its foundational competencies, identifying substantial areas for
enhancement. Our research emphasizes prompt engineering, an often-underutilized
strategy for improving AI responsiveness. Through iterative testing, we refined
the model's prompts, significantly improving its interpretative accuracy and
relevance in medical imaging. From our comprehensive evaluations, we distilled
10 effective prompt engineering techniques, each fortifying GPT-4V's medical
acumen. These methodical enhancements facilitate more reliable, precise, and
clinically valuable insights from GPT-4V, advancing its operability in critical
healthcare environments. Our findings are pivotal for those employing AI in
medicine, providing clear, actionable guidance on harnessing GPT-4V's full
diagnostic potential.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
Abstract: In this work, we study the low-rank MDPs with adversarially changed losses in
the full-information feedback setting. In particular, the unknown transition
probability kernel admits a low-rank matrix decomposition \citep{REPUCB22}, and
the loss functions may change adversarially but are revealed to the learner at
the end of each episode. We propose a policy optimization-based algorithm POLO,
and we prove that it attains the
$\widetilde{O}(K^{\frac{5}{6}}A^{\frac{1}{2}}d\ln(1+M)/(1-\gamma)^2)$ regret
guarantee, where $d$ is rank of the transition kernel (and hence the dimension
of the unknown representations), $A$ is the cardinality of the action space,
$M$ is the cardinality of the model class, and $\gamma$ is the discounted
factor. Notably, our algorithm is oracle-efficient and has a regret guarantee
with no dependence on the size of potentially arbitrarily large state space.
Furthermore, we also prove an $\Omega(\frac{\gamma^2}{1-\gamma} \sqrt{d A K})$
regret lower bound for this problem, showing that low-rank MDPs are
statistically more difficult to learn than linear MDPs in the regret
minimization setting. To the best of our knowledge, we present the first
algorithm that interleaves representation learning, exploration, and
exploitation to achieve the sublinear regret guarantee for RL with nonlinear
function approximation and adversarial losses.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
Abstract: In this paper, we focus on multimedia recommender systems using graph
convolutional networks (GCNs) where the multimodal features as well as
user-item interactions are employed together. Our study aims to exploit
multimodal features more effectively in order to accurately capture users'
preferences for items. To this end, we point out following two limitations of
existing GCN-based multimedia recommender systems: (L1) although multimodal
features of interacted items by a user can reveal her preferences on items,
existing methods utilize GCN designed to focus only on capturing collaborative
signals, resulting in insufficient reflection of the multimodal features in the
final user/item embeddings; (L2) although a user decides whether to prefer the
target item by considering its multimodal features, existing methods represent
her as only a single embedding regardless of the target item's multimodal
features and then utilize her embedding to predict her preference for the
target item. To address the above issues, we propose a novel multimedia
recommender system, named MONET, composed of following two core ideas:
modality-embracing GCN (MeGCN) and target-aware attention. Through extensive
experiments using four real-world datasets, we demonstrate i) the significant
superiority of MONET over seven state-of-the-art competitors (up to 30.32%
higher accuracy in terms of recall@20, compared to the best competitor) and ii)
the effectiveness of the two core ideas in MONET. All MONET codes are available
at https://github.com/Kimyungi/MONET.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs
Abstract: While physics-informed neural networks (PINNs) have been proven effective for
low-dimensional partial differential equations (PDEs), the computational cost
remains a hurdle in high-dimensional scenarios. This is particularly pronounced
when computing high-order and high-dimensional derivatives in the
physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian
noise for stochastic smoothing of the original neural net model, enabling Monte
Carlo methods for derivative approximation, eliminating the need for costly
auto-differentiation. Despite its computational efficiency in high dimensions,
RS-PINN introduces biases in both loss and gradients, negatively impacting
convergence, especially when coupled with stochastic gradient descent (SGD). We
present a comprehensive analysis of biases in RS-PINN, attributing them to the
nonlinearity of the Mean Squared Error (MSE) loss and the PDE nonlinearity. We
propose tailored bias correction techniques based on the order of PDE
nonlinearity. The unbiased RS-PINN allows for a detailed examination of its
pros and cons compared to the biased version. Specifically, the biased version
has a lower variance and runs faster than the unbiased version, but it is less
accurate due to the bias. To optimize the bias-variance trade-off, we combine
the two approaches in a hybrid method that balances the rapid convergence of
the biased version with the high accuracy of the unbiased version. In addition,
we present an enhanced implementation of RS-PINN. Extensive experiments on
diverse high-dimensional PDEs, including Fokker-Planck, HJB, viscous Burgers',
Allen-Cahn, and Sine-Gordon equations, illustrate the bias-variance trade-off
and highlight the effectiveness of the hybrid RS-PINN. Empirical guidelines are
provided for selecting biased, unbiased, or hybrid versions, depending on the
dimensionality and nonlinearity of the specific PDE problem.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Kindness in Multi-Agent Reinforcement Learning
Abstract: In human societies, people often incorporate fairness in their decisions and
treat reciprocally by being kind to those who act kindly. They evaluate the
kindness of others' actions not only by monitoring the outcomes but also by
considering the intentions. This behavioral concept can be adapted to train
cooperative agents in Multi-Agent Reinforcement Learning (MARL). We propose the
KindMARL method, where agents' intentions are measured by counterfactual
reasoning over the environmental impact of the actions that were available to
the agents. More specifically, the current environment state is compared with
the estimation of the current environment state provided that the agent had
chosen another action. The difference between each agent's reward, as the
outcome of its action, with that of its fellow, multiplied by the intention of
the fellow is then taken as the fellow's "kindness". If the result of each
reward-comparison confirms the agent's superiority, it perceives the fellow's
kindness and reduces its own reward. Experimental results in the Cleanup and
Harvest environments show that training based on the KindMARL method enabled
the agents to earn 89\% (resp. 37\%) and 44% (resp. 43\%) more total rewards
than training based on the Inequity Aversion and Social Influence methods. The
effectiveness of KindMARL is further supported by experiments in a traffic
light control problem.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints
Abstract: Consider the setting of constrained optimization, with some parameters
unknown at solving time and requiring prediction from relevant features.
Predict+Optimize is a recent framework for end-to-end training supervised
learning models for such predictions, incorporating information about the
optimization problem in the training process in order to yield better
predictions in terms of the quality of the predicted solution under the true
parameters. Almost all prior works have focused on the special case where the
unknowns appear only in the optimization objective and not the constraints. Hu
et al.~proposed the first adaptation of Predict+Optimize to handle unknowns
appearing in constraints, but the framework has somewhat ad-hoc elements, and
they provided a training algorithm only for covering and packing linear
programs. In this work, we give a new \emph{simpler} and \emph{more powerful}
framework called \emph{Two-Stage Predict+Optimize}, which we believe should be
the canonical framework for the Predict+Optimize setting. We also give a
training algorithm usable for all mixed integer linear programs, vastly
generalizing the applicability of the framework. Experimental results
demonstrate the superior prediction performance of our training framework over
all classical and state-of-the-art methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Improved Anonymous Multi-Agent Path Finding Algorithm
Abstract: We consider an Anonymous Multi-Agent Path-Finding (AMAPF) problem where the
set of agents is confined to a graph, a set of goal vertices is given and each
of these vertices has to be reached by some agent. The problem is to find an
assignment of the goals to the agents as well as the collision-free paths, and
we are interested in finding the solution with the optimal makespan. A
well-established approach to solve this problem is to reduce it to a special
type of a graph search problem, i.e. to the problem of finding a maximum flow
on an auxiliary graph induced by the input one. The size of the former graph
may be very large and the search on it may become a bottleneck. To this end, we
suggest a specific search algorithm that leverages the idea of exploring the
search space not through considering separate search states but rather bulks of
them simultaneously. That is, we implicitly compress, store and expand bulks of
the search states as single states, which results in high reduction in runtime
and memory. Empirically, the resultant AMAPF solver demonstrates superior
performance compared to the state-of-the-art competitor and is able to solve
all publicly available MAPF instances from the well-known MovingAI benchmark in
less than 30 seconds.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: On the stability, correctness and plausibility of visual explanation methods based on feature importance
Abstract: In the field of Explainable AI, multiples evaluation metrics have been
proposed in order to assess the quality of explanation methods w.r.t. a set of
desired properties. In this work, we study the articulation between the
stability, correctness and plausibility of explanations based on feature
importance for image classifiers. We show that the existing metrics for
evaluating these properties do not always agree, raising the issue of what
constitutes a good evaluation metric for explanations. Finally, in the
particular case of stability and correctness, we show the possible limitations
of some evaluation metrics and propose new ones that take into account the
local behaviour of the model under test.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: JudgeLM: Fine-tuned Large Language Models are Scalable Judges
Abstract: Evaluating Large Language Models (LLMs) in open-ended scenarios is
challenging because existing benchmarks and metrics can not measure them
comprehensively. To address this problem, we propose to fine-tune LLMs as
scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in
open-ended benchmarks. We first propose a comprehensive, large-scale,
high-quality dataset containing task seeds, LLMs-generated answers, and
GPT-4-generated judgments for fine-tuning high-performance judges, as well as a
new benchmark for evaluating the judges. We train JudgeLM at different scales
from 7B, 13B, to 33B parameters, and conduct a systematic analysis of its
capabilities and behaviors. We then analyze the key biases in fine-tuning LLM
as a judge and consider them as position bias, knowledge bias, and format bias.
To address these issues, JudgeLM introduces a bag of techniques including swap
augmentation, reference support, and reference drop, which clearly enhance the
judge's performance. JudgeLM obtains the state-of-the-art judge performance on
both the existing PandaLM benchmark and our proposed new benchmark. Our JudgeLM
is efficient and the JudgeLM-7B only needs 3 minutes to judge 5K samples with 8
A100 GPUs. JudgeLM obtains high agreement with the teacher judge, achieving an
agreement exceeding 90% that even surpasses human-to-human agreement. JudgeLM
also demonstrates extended capabilities in being judges of the single answer,
multimodal models, multiple answers, and multi-turn chat.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Positional Description Matters for Transformers Arithmetic
Abstract: Transformers, central to the successes in modern Natural Language Processing,
often falter on arithmetic tasks despite their vast capabilities --which
paradoxically include remarkable coding abilities. We observe that a crucial
challenge is their naive reliance on positional information to solve arithmetic
problems with a small number of digits, leading to poor performance on larger
numbers. Herein, we delve deeper into the role of positional encoding, and
propose several ways to fix the issue, either by modifying the positional
encoding directly, or by modifying the representation of the arithmetic task to
leverage standard positional encoding differently. We investigate the value of
these modifications for three tasks: (i) classical multiplication, (ii) length
extrapolation in addition, and (iii) addition in natural language context. For
(i) we train a small model on a small dataset (100M parameters and 300k
samples) with remarkable aptitude in (direct, no scratchpad) 15 digits
multiplication and essentially perfect up to 12 digits, while usual training in
this context would give a model failing at 4 digits multiplication. In the
experiments on addition, we use a mere 120k samples to demonstrate: for (ii)
extrapolation from 10 digits to testing on 12 digits numbers while usual
training would have no extrapolation, and for (iii) almost perfect accuracy up
to 5 digits while usual training would be correct only up to 3 digits (which is
essentially memorization with a training set of 120k samples).

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Rethinking and Improving Multi-task Learning for End-to-end Speech Translation
Abstract: Significant improvements in end-to-end speech translation (ST) have been
achieved through the application of multi-task learning. However, the extent to
which auxiliary tasks are highly consistent with the ST task, and how much this
approach truly helps, have not been thoroughly studied. In this paper, we
investigate the consistency between different tasks, considering different
times and modules. We find that the textual encoder primarily facilitates
cross-modal conversion, but the presence of noise in speech impedes the
consistency between text and speech representations. Furthermore, we propose an
improved multi-task learning (IMTL) approach for the ST task, which bridges the
modal gap by mitigating the difference in length and representation. We conduct
experiments on the MuST-C dataset. The results demonstrate that our method
attains state-of-the-art results. Moreover, when additional data is used, we
achieve the new SOTA result on MuST-C English to Spanish task with 20.8% of the
training time required by the current SOTA method.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Image Transformation for IoT Time-Series Data: A Review
Abstract: In the era of the Internet of Things (IoT), where smartphones, built-in
systems, wireless sensors, and nearly every smart device connect through local
networks or the internet, billions of smart things communicate with each other
and generate vast amounts of time-series data. As IoT time-series data is
high-dimensional and high-frequency, time-series classification or regression
has been a challenging issue in IoT. Recently, deep learning algorithms have
demonstrated superior performance results in time-series data classification in
many smart and intelligent IoT applications. However, it is hard to explore the
hidden dynamic patterns and trends in time-series. Recent studies show that
transforming IoT data into images improves the performance of the learning
model. In this paper, we present a review of these studies which use image
transformation/encoding techniques in IoT domain. We examine the studies
according to their encoding techniques, data types, and application areas.
Lastly, we emphasize the challenges and future dimensions of image
transformation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks
Abstract: In-context learning (ICL) has become the default method for using large
language models (LLMs), making the exploration of its limitations and
understanding the underlying causes crucial. In this paper, we find that ICL
falls short of handling specification-heavy tasks, which are tasks with
complicated and extensive task specifications, requiring several hours for
ordinary humans to master, such as traditional information extraction tasks.
The performance of ICL on these tasks mostly cannot reach half of the
state-of-the-art results. To explore the reasons behind this failure, we
conduct comprehensive experiments on 18 specification-heavy tasks with various
LLMs and identify three primary reasons: inability to specifically understand
context, misalignment in task schema comprehension with humans, and inadequate
long-text understanding ability. Furthermore, we demonstrate that through
fine-tuning, LLMs can achieve decent performance on these tasks, indicating
that the failure of ICL is not an inherent flaw of LLMs, but rather a drawback
of existing alignment methods that renders LLMs incapable of handling
complicated specification-heavy tasks via ICL. To substantiate this, we perform
dedicated instruction tuning on LLMs for these tasks and observe a notable
improvement. We hope the analyses in this paper could facilitate advancements
in alignment methods enabling LLMs to meet more sophisticated human demands.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: PBWR: Parametric Building Wireframe Reconstruction from Aerial LiDAR Point Clouds
Abstract: In this paper, we present an end-to-end 3D building wireframe reconstruction
method to regress edges directly from aerial LiDAR point clouds.Our method,
named Parametric Building Wireframe Reconstruction (PBWR), takes aerial LiDAR
point clouds and initial edge entities as input, and fully uses self-attention
mechanism of transformers to regress edge parameters without any intermediate
steps such as corner prediction. We propose an edge non-maximum suppression
(E-NMS) module based on edge similarityto remove redundant edges. Additionally,
a dedicated edge loss function is utilized to guide the PBWR in regressing
edges parameters, where simple use of edge distance loss isn't suitable. In our
experiments, we demonstrate state-of-the-art results on the Building3D dataset,
achieving an improvement of approximately 36% in entry-level dataset edge
accuracy and around 42% improvement in the Tallinn dataset.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Removing NSFW Concepts from Vision-and-Language Models for Text-to-Image Retrieval and Generation
Abstract: Vision-and-Language models such as CLIP have demonstrated remarkable
effectiveness across a wide range of tasks. However, these models are typically
trained on web-scale data, which can introduce inappropriate content and lead
to the development of unsafe and biased behavior. This, in turn, hampers their
applicability in sensitive and trustworthy contexts and could raise significant
concern in their adoption. To overcome these limitations, we introduce a
methodology to make Vision-and-Language models safer by removing their
sensitivity to not-safe-for-work concepts. We show how this can be done by
distilling from a large language model which converts between safe and unsafe
sentences and which is fine-tuned starting from just 100 manually-curated
pairs. We conduct extensive experiments on the resulting embedding space for
both retrieval and text-to-image generation, where we show that our model can
also be properly employed with pre-trained image generators. Our source code
and trained models are available at: https://github.com/aimagelab/safe-clip.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Abstract: Transformer-based large language models (LLMs) have demonstrated impressive
capabilities in a variety of natural language processing (NLP) tasks.
Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge
devices with limited computing, memory, and energy budgets. In this paper, we
propose Confidant, a multi-backend collaborative training framework for
customizing state-of-the-art LLMs on commodity mobile devices like smartphones.
Confidant partitions an LLM into several sub-models so that each fits into a
mobile device's memory. A pipeline parallel training mechanism is further
developed to ensure fast and efficient distributed training. In addition, we
propose a novel backend scheduler to allocate different attention heads to
heterogeneous compute hardware, including mobile CPU and GPUs, to maximize the
compute resource utilization on each edge device. Our preliminary experimental
results show that Confidant achieves at most 45.3% memory reduction and 8.03x
inference speedup in practical settings.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: VERVE: Template-based ReflectiVE Rewriting for MotiVational IntErviewing
Abstract: Reflective listening is a fundamental skill that counselors must acquire to
achieve proficiency in motivational interviewing (MI). It involves responding
in a manner that acknowledges and explores the meaning of what the client has
expressed in the conversation. In this work, we introduce the task of
counseling response rewriting, which transforms non-reflective statements into
reflective responses. We introduce VERVE, a template-based rewriting system
with paraphrase-augmented training and adaptive template updating. VERVE first
creates a template by identifying and filtering out tokens that are not
relevant to reflections and constructs a reflective response using the
template. Paraphrase-augmented training allows the model to learn less-strict
fillings of masked spans, and adaptive template updating helps discover
effective templates for rewriting without significantly removing the original
content. Using both automatic and human evaluations, we compare our method
against text rewriting baselines and show that our framework is effective in
turning non-reflective statements into more reflective responses while
achieving a good content preservation-reflection style trade-off.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering
Abstract: Graph clustering has been popularly studied in recent years. However, most
existing graph clustering methods focus on node-level clustering, i.e.,
grouping nodes in a single graph into clusters. In contrast, graph-level
clustering, i.e., grouping multiple graphs into clusters, remains largely
unexplored. Graph-level clustering is critical in a variety of real-world
applications, such as, properties prediction of molecules and community
analysis in social networks. However, graph-level clustering is challenging due
to the insufficient discriminability of graph-level representations, and the
insufficient discriminability makes deep clustering be more likely to obtain
degenerate solutions (cluster collapse). To address the issue, we propose a
novel deep graph-level clustering method called Uniform Deep Graph Clustering
(UDGC). UDGC assigns instances evenly to different clusters and then scatters
those clusters on unit hypersphere, leading to a more uniform cluster-level
distribution and a slighter cluster collapse. Specifically, we first propose
Augmentation-Consensus Optimal Transport (ACOT) for generating uniformly
distributed and reliable pseudo labels for partitioning clusters. Then we adopt
contrastive learning to scatter those clusters. Besides, we propose Center
Alignment Optimal Transport (CAOT) for guiding the model to learn better
parameters, which further promotes the cluster performance. Our empirical study
on eight well-known datasets demonstrates that UDGC significantly outperforms
the state-of-the-art models.

Output: Machine Learning