Instruction: What field is the article from?

Prompt:
Title: EMDM: Efficient Motion Diffusion Model for Fast, High-Quality Motion Generation
Abstract: We introduce Efficient Motion Diffusion Model (EMDM) for fast and
high-quality human motion generation. Although previous motion diffusion models
have shown impressive results, they struggle to achieve fast generation while
maintaining high-quality human motions. Motion latent diffusion has been
proposed for efficient motion generation. However, effectively learning a
latent space can be non-trivial in such a two-stage manner. Meanwhile,
accelerating motion sampling by increasing the step size, e.g., DDIM, typically
leads to a decline in motion quality due to the inapproximation of complex data
distributions when naively increasing the step size. In this paper, we propose
EMDM that allows for much fewer sample steps for fast motion generation by
modeling the complex denoising distribution during multiple sampling steps.
Specifically, we develop a Conditional Denoising Diffusion GAN to capture
multimodal data distributions conditioned on both control signals, i.e.,
textual description and denoising time step. By modeling the complex data
distribution, a larger sampling step size and fewer steps are achieved during
motion synthesis, significantly accelerating the generation process. To
effectively capture the human dynamics and reduce undesired artifacts, we
employ motion geometric loss during network training, which improves the motion
quality and training efficiency. As a result, EMDM achieves a remarkable
speed-up at the generation stage while maintaining high-quality motion
generation in terms of fidelity and diversity.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Dance of Channel and Sequence: An Efficient Attention-Based Approach for Multivariate Time Series Forecasting
Abstract: In recent developments, predictive models for multivariate time series
analysis have exhibited commendable performance through the adoption of the
prevalent principle of channel independence. Nevertheless, it is imperative to
acknowledge the intricate interplay among channels, which fundamentally
influences the outcomes of multivariate predictions. Consequently, the notion
of channel independence, while offering utility to a certain extent, becomes
increasingly impractical, leading to information degradation. In response to
this pressing concern, we present CSformer, an innovative framework
characterized by a meticulously engineered two-stage self-attention mechanism.
This mechanism is purposefully designed to enable the segregated extraction of
sequence-specific and channel-specific information, while sharing parameters to
promote synergy and mutual reinforcement between sequences and channels.
Simultaneously, we introduce sequence adapters and channel adapters, ensuring
the model's ability to discern salient features across various dimensions.
Rigorous experimentation, spanning multiple real-world datasets, underscores
the robustness of our approach, consistently establishing its position at the
forefront of predictive performance across all datasets. This augmentation
substantially enhances the capacity for feature extraction inherent to
multivariate time series data, facilitating a more comprehensive exploitation
of the available information.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft
Abstract: Traditional reinforcement-learning-based agents rely on sparse rewards that
often only use binary values to indicate task completion or failure. The
challenge in exploration efficiency makes it difficult to effectively learn
complex tasks in Minecraft. To address this, this paper introduces an advanced
learning system, named Auto MC-Reward, that leverages Large Language Models
(LLMs) to automatically design dense reward functions, thereby enhancing the
learning efficiency. Auto MC-Reward consists of three important components:
Reward Designer, Reward Critic, and Trajectory Analyzer. Given the environment
information and task descriptions, the Reward Designer first design the reward
function by coding an executable Python function with predefined observation
inputs. Then, our Reward Critic will be responsible for verifying the code,
checking whether the code is self-consistent and free of syntax and semantic
errors. Further, the Trajectory Analyzer summarizes possible failure causes and
provides refinement suggestions according to collected trajectories. In the
next round, Reward Designer will take further refine and iterate the dense
reward function based on feedback. Experiments demonstrate a significant
improvement in the success rate and learning efficiency of our agents in
complex tasks in Minecraft, such as obtaining diamond with the efficient
ability to avoid lava, and efficiently explore trees and animals that are
sparse on the plains biome.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Data Factors for Better Compositional Generalization
Abstract: Recent diagnostic datasets on compositional generalization, such as SCAN
(Lake and Baroni, 2018) and COGS (Kim and Linzen, 2020), expose severe problems
in models trained from scratch on these datasets. However, in contrast to this
poor performance, state-of-the-art models trained on larger and more general
datasets show better generalization ability. In this work, to reconcile this
inconsistency, we conduct an empirical analysis by training Transformer models
on a variety of training sets with different data factors, including dataset
scale, pattern complexity, example difficulty, etc. First, we show that
increased dataset complexity can lead to better generalization behavior on
multiple different generalization challenges. To further understand this
improvement, we show two axes of the benefit from more complex datasets: they
provide more diverse examples so compositional understanding becomes more
effective, and they also prevent ungeneralizable memorization of the examples
due to reduced example repetition frequency. Finally, we explore how training
examples of different difficulty levels influence generalization differently.
On synthetic datasets, simple examples invoke stronger compositionality than
hard examples do. On larger-scale real language datasets, while hard examples
become more important potentially to ensure decent data coverage, a balanced
mixture of simple and hard examples manages to induce the strongest
generalizability. The code and data for this work are available at
https://github.com/owenzx/data4comp

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Efficient Representation of the Activation Space in Deep Neural Networks
Abstract: The representations of the activation space of deep neural networks (DNNs)
are widely utilized for tasks like natural language processing, anomaly
detection and speech recognition. Due to the diverse nature of these tasks and
the large size of DNNs, an efficient and task-independent representation of
activations becomes crucial. Empirical p-values have been used to quantify the
relative strength of an observed node activation compared to activations
created by already-known inputs. Nonetheless, keeping raw data for these
calculations increases memory resource consumption and raises privacy concerns.
To this end, we propose a model-agnostic framework for creating representations
of activations in DNNs using node-specific histograms to compute p-values of
observed activations without retaining already-known inputs. Our proposed
approach demonstrates promising potential when validated with multiple network
architectures across various downstream tasks and compared with the kernel
density estimates and brute-force empirical baselines. In addition, the
framework reduces memory usage by 30% with up to 4 times faster p-value
computing time while maintaining state of-the-art detection power in downstream
tasks such as the detection of adversarial attacks and synthesized content.
Moreover, as we do not persist raw data at inference time, we could potentially
reduce susceptibility to attacks and privacy issues.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Few-shot Hybrid Domain Adaptation of Image Generators
Abstract: Can a pre-trained generator be adapted to the hybrid of multiple target
domains and generate images with integrated attributes of them? In this work,
we introduce a new task -- Few-shot Hybrid Domain Adaptation (HDA). Given a
source generator and several target domains, HDA aims to acquire an adapted
generator that preserves the integrated attributes of all target domains,
without overriding the source domain's characteristics. Compared with Domain
Adaptation (DA), HDA offers greater flexibility and versatility to adapt
generators to more composite and expansive domains. Simultaneously, HDA also
presents more challenges than DA as we have access only to images from
individual target domains and lack authentic images from the hybrid domain. To
address this issue, we introduce a discriminator-free framework that directly
encodes different domains' images into well-separable subspaces. To achieve
HDA, we propose a novel directional subspace loss comprised of a distance loss
and a direction loss. Concretely, the distance loss blends the attributes of
all target domains by reducing the distances from generated images to all
target subspaces. The direction loss preserves the characteristics from the
source domain by guiding the adaptation along the perpendicular to subspaces.
Experiments show that our method can obtain numerous domain-specific attributes
in a single adapted generator, which surpasses the baseline methods in semantic
similarity, image fidelity, and cross-domain consistency.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery
Abstract: Large, self-supervised vision models have led to substantial advancements for
automatically interpreting natural images. Recent works have begun tailoring
these methods to remote sensing data which has rich structure with
multi-sensor, multi-spectral, and temporal information providing massive
amounts of self-labeled data that can be used for self-supervised pre-training.
In this work, we develop a new encoder architecture called USat that can input
multi-spectral data from multiple sensors for self-supervised pre-training.
USat is a vision transformer with modified patch projection layers and
positional encodings to model spectral bands with varying spatial scales from
multiple sensors. We integrate USat into a Masked Autoencoder (MAE)
self-supervised pre-training procedure and find that a pre-trained USat
outperforms state-of-the-art self-supervised MAE models trained on remote
sensing data on multiple remote sensing benchmark datasets (up to 8%) and leads
to improvements in low data regimes (up to 7%). Code and pre-trained weights
are available at https://github.com/stanfordmlgroup/USat .

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology
Abstract: Computational pathology models rarely utilise data that will not be available
for inference. This means most models cannot learn from highly informative data
such as additional immunohistochemical (IHC) stains and spatial
transcriptomics. We present TriDeNT, a novel self-supervised method for
utilising privileged data that is not available during inference to improve
performance. We demonstrate the efficacy of this method for a range of
different paired data including immunohistochemistry, spatial transcriptomics
and expert nuclei annotations. In all settings, TriDeNT outperforms other
state-of-the-art methods in downstream tasks, with observed improvements of up
to 101%. Furthermore, we provide qualitative and quantitative measurements of
the features learned by these models and how they differ from baselines.
TriDeNT offers a novel method to distil knowledge from scarce or costly data
during training, to create significantly better models for routine inputs.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Cross-domain feature disentanglement for interpretable modeling of tumor microenvironment impact on drug response
Abstract: High-throughput screening technology has facilitated the generation of
large-scale drug responses across hundreds of cancer cell lines. However, there
exists significant discrepancy between in vitro cell lines and actual tumors in
vivo in terms of their response to drug treatments, because of tumors comprise
of complex cellular compositions and histopathology structure, known as tumor
microenvironment (TME), which greatly influences the drug cytotoxicity against
tumor cells. To date, no study has focused on modeling the impact of the TME on
clinical drug response. This paper proposed a domain adaptation network for
feature disentanglement to separate representations of cancer cells and TME of
a tumor in patients. Two denoising autoencoders were separately used to extract
features from cell lines (source domain) and tumors (target domain) for partial
domain alignment and feature decoupling. The specific encoder was enforced to
extract information only about TME. Moreover, to ensure generalizability to
novel drugs, we applied a graph attention network to learn the latent
representation of drugs, allowing us to linearly model the drug perturbation on
cellular state in latent space. We calibrated our model on a benchmark dataset
and demonstrated its superior performance in predicting clinical drug response
and dissecting the influence of the TME on drug efficacy.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Assessing Prompt Injection Risks in 200+ Custom GPTs
Abstract: In the rapidly evolving landscape of artificial intelligence, ChatGPT has
been widely used in various applications. The new feature: customization of
ChatGPT models by users to cater to specific needs has opened new frontiers in
AI utility. However, this study reveals a significant security vulnerability
inherent in these user-customized GPTs: prompt injection attacks. Through
comprehensive testing of over 200 user-designed GPT models via adversarial
prompts, we demonstrate that these systems are susceptible to prompt
injections. Through prompt injection, an adversary can not only extract the
customized system prompts but also access the uploaded files. This paper
provides a first-hand analysis of the prompt injection, alongside the
evaluation of the possible mitigation of such attacks. Our findings underscore
the urgent need for robust security frameworks in the design and deployment of
customizable GPT models. The intent of this paper is to raise awareness and
prompt action in the AI community, ensuring that the benefits of GPT
customization do not come at the cost of compromised security and privacy.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding
Abstract: Current Large Language Models (LLMs) are unparalleled in their ability to
generate grammatically correct, fluent text. LLMs are appearing rapidly, and
debates on LLM capacities have taken off, but reflection is lagging behind.
Thus, in this position paper, we first zoom in on the debate and critically
assess three points recurring in critiques of LLM capacities: i) that LLMs only
parrot statistical patterns in the training data; ii) that LLMs master formal
but not functional language competence; and iii) that language learning in LLMs
cannot inform human language learning. Drawing on empirical and theoretical
arguments, we show that these points need more nuance. Second, we outline a
pragmatic perspective on the issue of `real' understanding and intentionality
in LLMs. Understanding and intentionality pertain to unobservable mental states
we attribute to other humans because they have pragmatic value: they allow us
to abstract away from complex underlying mechanics and predict behaviour
effectively. We reflect on the circumstances under which it would make sense
for humans to similarly attribute mental states to LLMs, thereby outlining a
pragmatic philosophical context for LLMs as an increasingly prominent
technology in society.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: ReCoRe: Regularized Contrastive Representation Learning of World Model
Abstract: While recent model-free Reinforcement Learning (RL) methods have demonstrated
human-level effectiveness in gaming environments, their success in everyday
tasks like visual navigation has been limited, particularly under significant
appearance variations. This limitation arises from (i) poor sample efficiency
and (ii) over-fitting to training scenarios. To address these challenges, we
present a world model that learns invariant features using (i) contrastive
unsupervised learning and (ii) an intervention-invariant regularizer. Learning
an explicit representation of the world dynamics i.e. a world model, improves
sample efficiency while contrastive learning implicitly enforces learning of
invariant features, which improves generalization. However, the naive
integration of contrastive loss to world models fails due to a lack of
supervisory signals to the visual encoder, as world-model-based RL methods
independently optimize representation learning and agent policy. To overcome
this issue, we propose an intervention-invariant regularizer in the form of an
auxiliary task such as depth prediction, image denoising, etc., that explicitly
enforces invariance to style-interventions. Our method outperforms current
state-of-the-art model-based and model-free RL methods and significantly on
out-of-distribution point navigation task evaluated on the iGibson benchmark.
We further demonstrate that our approach, with only visual observations,
outperforms recent language-guided foundation models for point navigation,
which is essential for deployment on robots with limited computation
capabilities. Finally, we demonstrate that our proposed model excels at the
sim-to-real transfer of its perception module on Gibson benchmark.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Preserving the knowledge of long clinical texts using aggregated ensembles of large language models
Abstract: Clinical texts, such as admission notes, discharge summaries, and progress
notes, contain rich and valuable information that can be used for various
clinical outcome prediction tasks. However, applying large language models,
such as BERT-based models, to clinical texts poses two major challenges: the
limitation of input length and the diversity of data sources. This paper
proposes a novel method to preserve the knowledge of long clinical texts using
aggregated ensembles of large language models. Unlike previous studies which
use model ensembling or text aggregation methods separately, we combine
ensemble learning with text aggregation and train multiple large language
models on two clinical outcome tasks: mortality prediction and length of stay
prediction. We show that our method can achieve better results than baselines,
ensembling, and aggregation individually, and can improve the performance of
large language models while handling long inputs and diverse datasets. We
conduct extensive experiments on the admission notes from the MIMIC-III
clinical database by combining multiple unstructured and high-dimensional
datasets, demonstrating our method's effectiveness and superiority over
existing approaches. We also provide a comprehensive analysis and discussion of
our results, highlighting our method's applications and limitations for future
research in the domain of clinical healthcare. The results and analysis of this
study is supportive of our method assisting in clinical healthcare systems by
enabling clinical decision-making with robust performance overcoming the
challenges of long text inputs and varied datasets.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems
Abstract: As Artificial Intelligence (AI) permeates many aspects of society, it brings
numerous advantages while at the same time raising ethical concerns and
potential risks, such as perpetuating inequalities through biased or
discriminatory decision-making. To develop AI systems that cater for the needs
of diverse users and uphold ethical values, it is essential to consider and
integrate diversity and inclusion (D&I) principles throughout AI development
and deployment. Requirements engineering (RE) is a fundamental process in
developing software systems by eliciting and specifying relevant needs from
diverse stakeholders. This research aims to address the lack of research and
practice on how to elicit and capture D&I requirements for AI systems. We have
conducted comprehensive data collection and synthesis from the literature
review to extract requirements themes related to D&I in AI. We have proposed a
tailored user story template to capture D&I requirements and conducted focus
group exercises to use the themes and user story template in writing D&I
requirements for two example AI systems. Additionally, we have investigated the
capability of our solution by generating synthetic D&I requirements captured in
user stories with the help of a Large Language Model.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery
Abstract: The trajectory on the road traffic is commonly collected at a low sampling
rate, and trajectory recovery aims to recover a complete and continuous
trajectory from the sparse and discrete inputs. Recently, sequential language
models have been innovatively adopted for trajectory recovery in a pre-trained
manner: it learns road segment representation vectors, which will be used in
the downstream tasks. However, existing methods are incapable of handling
complex trajectories: when the trajectory crosses remote road segments or makes
several turns, which we call critical nodes, the quality of learned
representations deteriorates, and the recovered trajectories skip the critical
nodes. This work is dedicated to offering a more robust trajectory recovery for
complex trajectories. Firstly, we define the trajectory complexity based on the
detour score and entropy score and construct the complexity-aware semantic
graphs correspondingly. Then, we propose a Multi-view Graph and Complexity
Aware Transformer (MGCAT) model to encode these semantics in trajectory
pre-training from two aspects: 1) adaptively aggregate the multi-view graph
features considering trajectory pattern, and 2) higher attention to critical
nodes in a complex trajectory. Such that, our MGCAT is perceptual when handling
the critical scenario of complex trajectories. Extensive experiments are
conducted on large-scale datasets. The results prove that our method learns
better representations for trajectory recovery, with 5.22% higher F1-score
overall and 8.16% higher F1-score for complex trajectories particularly. The
code is available at https://github.com/bonaldli/ComplexTraj.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Impact of Tokenization on LLaMa Russian Adaptation
Abstract: Latest instruction-tuned large language models (LLM) show great results on
various tasks, however, they often face performance degradation for non-English
input. There is evidence that the reason lies in inefficient tokenization
caused by low language representation in pre-training data which hinders the
comprehension of non-English instructions, limiting the potential of target
language instruction-tuning. In this work we investigate the possibility of
addressing the issue with vocabulary substitution in the context of LLaMa
Russian language adaptation. We explore three variants of vocabulary adaptation
and test their performance on Saiga instruction-tuning and fine-tuning on
Russian Super Glue benchmark. The results of automatic evaluation show that
vocabulary substitution not only improves the model's quality in Russian but
also accelerates fine-tuning (35%) and inference (up to 60%) while reducing
memory consumption. Additional human evaluation of the instruction-tuned models
demonstrates that models with Russian-adapted vocabulary generate answers with
higher user preference than the original Saiga-LLaMa model.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning
Abstract: We present a data and cost efficient way of incorporating the speech modality
into a large language model (LLM). The resulting multi-modal LLM is a
COntextual Speech Model with Instruction-following/in-context-learning
Capabilities - COSMIC. Speech comprehension test question-answer (SQA) pairs
are generated using GPT-3.5 based on the speech transcriptions as a part of the
supervision for the instruction tuning. With fewer than 20M trainable
parameters and as little as 450 hours of English speech data for SQA
generation, COSMIC exhibits emergent instruction-following and in-context
learning capabilities in speech-to-text tasks. The model is able to follow the
given text instructions to generate text response even on the unseen EN$\to$X
speech-to-text translation (S2TT) task with zero-shot setting. We evaluate the
model's in-context learning via various tasks such as EN$\to$X S2TT and
few-shot domain adaptation. And instruction-following capabilities are
evaluated through a contextual biasing benchmark. Our results demonstrate the
efficacy of the proposed low cost recipe for building a speech LLM and that
with the new instruction-tuning data.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
Abstract: Recent advances in text-to-image generation have made remarkable progress in
synthesizing realistic human photos conditioned on given text prompts. However,
existing personalized generation methods cannot simultaneously satisfy the
requirements of high efficiency, promising identity (ID) fidelity, and flexible
text controllability. In this work, we introduce PhotoMaker, an efficient
personalized text-to-image generation method, which mainly encodes an arbitrary
number of input ID images into a stack ID embedding for preserving ID
information. Such an embedding, serving as a unified ID representation, can not
only encapsulate the characteristics of the same input ID comprehensively, but
also accommodate the characteristics of different IDs for subsequent
integration. This paves the way for more intriguing and practically valuable
applications. Besides, to drive the training of our PhotoMaker, we propose an
ID-oriented data construction pipeline to assemble the training data. Under the
nourishment of the dataset constructed through the proposed pipeline, our
PhotoMaker demonstrates better ID preservation ability than test-time
fine-tuning based methods, yet provides significant speed improvements,
high-quality generation results, strong generalization capabilities, and a wide
range of applications. Our project page is available at
https://photo-maker.github.io/

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: ToolTalk: Evaluating Tool-Usage in a Conversational Setting
Abstract: Large language models (LLMs) have displayed massive improvements in reasoning
and decision-making skills and can hold natural conversations with users. Many
recent works seek to augment LLM-based assistants with external tools so they
can access private or up-to-date information and carry out actions on behalf of
users. To better measure the performance of these assistants, this paper
introduces ToolTalk, a benchmark consisting of complex user intents requiring
multi-step tool usage specified through dialogue. ToolTalk contains 28 tools
grouped into 7 plugins, and includes a complete simulated implementation of
each tool, allowing for fully automated evaluation of assistants that rely on
execution feedback. ToolTalk also emphasizes tools that externally affect the
world rather than only tools for referencing or searching information. We
evaluate GPT-3.5 and GPT-4 on ToolTalk resulting in success rates of 26% and
50% respectively. Our analysis of the errors reveals three major categories and
suggests some future directions for improvement. We release ToolTalk at
https://github.com/microsoft/ToolTalk.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment
Abstract: Language models trained on large-scale corpus often generate content that is
harmful, toxic, or contrary to human preferences, making their alignment with
human values a critical concern. Reinforcement learning from human feedback
(RLHF) with algorithms like PPO is a prevalent approach for alignment but is
often complex, unstable, and resource-intensive. Recently, ranking-based
alignment methods have emerged, offering stability and effectiveness by
replacing the RL framework with supervised fine-tuning, but they are costly due
to the need for annotated data. Considering that existing large language models
(LLMs) like ChatGPT are already relatively well-aligned and cost-friendly,
researchers have begun to align the language model with human preference from
AI feedback. The common practices, which unidirectionally distill the
instruction-following responses from LLMs, are constrained by their bottleneck.
Thus we introduce CycleAlign to distill alignment capabilities from
parameter-invisible LLMs (black-box) to a parameter-visible model (white-box)
in an iterative manner. With in-context learning (ICL) as the core of the
cycle, the black-box models are able to rank the model-generated responses
guided by human-craft instruction and demonstrations about their preferences.
During iterative interaction, the white-box models also have a judgment about
responses generated by them. Consequently, the agreement ranking could be
viewed as a pseudo label to dynamically update the in-context demonstrations
and improve the preference ranking ability of black-box models. Through
multiple interactions, the CycleAlign framework could align the white-box model
with the black-box model effectively in a low-resource way. Empirical results
illustrate that the model fine-tuned by CycleAlign remarkably exceeds existing
methods, and achieves the state-of-the-art performance in alignment with human
value.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Prediction of Locally Stationary Data Using Expert Advice
Abstract: The problem of continuous machine learning is studied. Within the framework
of the game-theoretic approach, when for calculating the next forecast, no
assumptions about the stochastic nature of the source that generates the data
flow are used -- the source can be analog, algorithmic or probabilistic, its
parameters can change at random times, when building a prognostic model, only
structural assumptions are used about the nature of data generation. An online
forecasting algorithm for a locally stationary time series is presented. An
estimate of the efficiency of the proposed algorithm is obtained.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion
Abstract: Large Language Models (LLMs) excel at tackling various natural language
tasks. However, due to the significant costs involved in re-training or
fine-tuning them, they remain largely static and difficult to personalize.
Nevertheless, a variety of applications could benefit from generations that are
tailored to users' preferences, goals, and knowledge. Among them is web search,
where knowing what a user is trying to accomplish, what they care about, and
what they know can lead to improved search experiences. In this work, we
propose a novel and general approach that augments an LLM with relevant context
from users' interaction histories with a search engine in order to personalize
its outputs. Specifically, we construct an entity-centric knowledge store for
each user based on their search and browsing activities on the web, which is
then leveraged to provide contextually relevant LLM prompt augmentations. This
knowledge store is light-weight, since it only produces user-specific aggregate
projections of interests and knowledge onto public knowledge graphs, and
leverages existing search log infrastructure, thereby mitigating the privacy,
compliance, and scalability concerns associated with building deep user
profiles for personalization. We then validate our approach on the task of
contextual query suggestion, which requires understanding not only the user's
current search context but also what they historically know and care about.
Through a number of experiments based on human evaluation, we show that our
approach is significantly better than several other LLM-powered baselines,
generating query suggestions that are contextually more relevant, personalized,
and useful.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations
Abstract: Emotion recognition in conversations (ERC), the task of recognizing the
emotion of each utterance in a conversation, is crucial for building empathetic
machines. Existing studies focus mainly on capturing context- and
speaker-sensitive dependencies on the textual modality but ignore the
significance of multimodal information. Different from emotion recognition in
textual conversations, capturing intra- and inter-modal interactions between
utterances, learning weights between different modalities, and enhancing modal
representations play important roles in multimodal ERC. In this paper, we
propose a transformer-based model with self-distillation (SDT) for the task.
The transformer-based model captures intra- and inter-modal interactions by
utilizing intra- and inter-modal transformers, and learns weights between
modalities dynamically by designing a hierarchical gated fusion strategy.
Furthermore, to learn more expressive modal representations, we treat soft
labels of the proposed model as extra training supervision. Specifically, we
introduce self-distillation to transfer knowledge of hard and soft labels from
the proposed model to each modality. Experiments on IEMOCAP and MELD datasets
demonstrate that SDT outperforms previous state-of-the-art baselines.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Distributional Latent Variable Models with an Application in Active Cognitive Testing
Abstract: Cognitive modeling commonly relies on asking participants to complete a
battery of varied tests in order to estimate attention, working memory, and
other latent variables. In many cases, these tests result in highly variable
observation models. A near-ubiquitous approach is to repeat many observations
for each test, resulting in a distribution over the outcomes from each test
given to each subject. In this paper, we explore the usage of latent variable
modeling to enable learning across many correlated variables simultaneously. We
extend latent variable models (LVMs) to the setting where observed data for
each subject are a series of observations from many different distributions,
rather than simple vectors to be reconstructed. By embedding test battery
results for individuals in a latent space that is trained jointly across a
population, we are able to leverage correlations both between tests for a
single participant and between multiple participants. We then propose an active
learning framework that leverages this model to conduct more efficient
cognitive test batteries. We validate our approach by demonstrating with
real-time data acquisition that it performs comparably to conventional methods
in making item-level predictions with fewer test items.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: GPT-4 Surpassing Human Performance in Linguistic Pragmatics
Abstract: As Large Language Models (LLMs) become increasingly integrated into everyday
life, their capabilities to understand and emulate human cognition are under
steady examination. This study investigates the ability of LLMs to comprehend
and interpret linguistic pragmatics, an aspect of communication that considers
context and implied meanings. Using Grice's communication principles, LLMs and
human subjects (N=76) were evaluated based on their responses to various
dialogue-based tasks. The findings revealed the superior performance and speed
of LLMs, particularly GPT4, over human subjects in interpreting pragmatics.
GPT4 also demonstrated accuracy in the pre-testing of human-written samples,
indicating its potential in text analysis. In a comparative analysis of LLMs
using human individual and average scores, the models exhibited significant
chronological improvement. The models were ranked from lowest to highest score,
with GPT2 positioned at 78th place, GPT3 ranking at 23rd, Bard at 10th, GPT3.5
placing 5th, Best Human scoring 2nd, and GPT4 achieving the top spot. The
findings highlight the remarkable progress made in the development and
performance of these LLMs. Future studies should consider diverse subjects,
multiple languages, and other cognitive aspects to fully comprehend the
capabilities of LLMs. This research holds significant implications for the
development and application of AI-based models in communication-centered
sectors.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Prompt Optimisation with Random Sampling
Abstract: Using the generative nature of a language model to generate task-relevant
separators has shown competitive results compared to human-curated prompts like
"TL;DR". We demonstrate that even randomly chosen tokens from the vocabulary as
separators can achieve near-state-of-the-art performance. We analyse this
phenomenon in detail using three different random generation strategies,
establishing that the language space is rich with potential good separators,
regardless of the underlying language model size. These observations challenge
the common assumption that an effective prompt should be human-readable or
task-relevant. Experimental results show that using random separators leads to
an average 16% relative improvement across nine text classification tasks on
seven language models, compared to human-curated separators, and is on par with
automatic prompt searching methods.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy Labels
Abstract: This paper delves into applying reinforcement learning (RL) in strategy
games, particularly those characterized by parity challenges, as seen in
specific positions of Go and Chess and a broader range of impartial games. We
propose a simulated learning process, structured within a curriculum learning
framework and augmented with noisy labels, to mirror the intricacies of
self-play learning scenarios. This approach thoroughly analyses how neural
networks (NNs) adapt and evolve from elementary to increasingly complex game
positions. Our empirical research indicates that even minimal label noise can
significantly impede NNs' ability to discern effective strategies, a difficulty
that intensifies with the growing complexity of the game positions. These
findings underscore the urgent need for advanced methodologies in RL training,
specifically tailored to counter the obstacles imposed by noisy evaluations.
The development of such methodologies is crucial not only for enhancing NN
proficiency in strategy games with significant parity elements but also for
broadening the resilience and efficiency of RL systems across diverse and
complex environments.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: minimax: Efficient Baselines for Autocurricula in JAX
Abstract: Unsupervised environment design (UED) is a form of automatic curriculum
learning for training robust decision-making agents to zero-shot transfer into
unseen environments. Such autocurricula have received much interest from the RL
community. However, UED experiments, based on CPU rollouts and GPU model
updates, have often required several weeks of training. This compute
requirement is a major obstacle to rapid innovation for the field. This work
introduces the minimax library for UED training on accelerated hardware. Using
JAX to implement fully-tensorized environments and autocurriculum algorithms,
minimax allows the entire training loop to be compiled for hardware
acceleration. To provide a petri dish for rapid experimentation, minimax
includes a tensorized grid-world based on MiniGrid, in addition to reusable
abstractions for conducting autocurricula in procedurally-generated
environments. With these components, minimax provides strong UED baselines,
including new parallelized variants, which achieve over 120$\times$ speedups in
wall time compared to previous implementations when training with equal batch
sizes. The minimax library is available under the Apache 2.0 license at
https://github.com/facebookresearch/minimax.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Responsible AI Research Needs Impact Statements Too
Abstract: All types of research, development, and policy work can have unintended,
adverse consequences - work in responsible artificial intelligence (RAI),
ethical AI, or ethics in AI is no exception.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Infinite forecast combinations based on Dirichlet process
Abstract: Forecast combination integrates information from various sources by
consolidating multiple forecast results from the target time series. Instead of
the need to select a single optimal forecasting model, this paper introduces a
deep learning ensemble forecasting model based on the Dirichlet process.
Initially, the learning rate is sampled with three basis distributions as
hyperparameters to convert the infinite mixture into a finite one. All
checkpoints are collected to establish a deep learning sub-model pool, and
weight adjustment and diversity strategies are developed during the combination
process. The main advantage of this method is its ability to generate the
required base learners through a single training process, utilizing the
decaying strategy to tackle the challenge posed by the stochastic nature of
gradient descent in determining the optimal learning rate. To ensure the
method's generalizability and competitiveness, this paper conducts an empirical
analysis using the weekly dataset from the M4 competition and explores
sensitivity to the number of models to be combined. The results demonstrate
that the ensemble model proposed offers substantial improvements in prediction
accuracy and stability compared to a single benchmark model.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: I Was Blind but Now I See: Implementing Vision-Enabled Dialogue in Social Robots
Abstract: In the rapidly evolving landscape of human-computer interaction, the
integration of vision capabilities into conversational agents stands as a
crucial advancement. This paper presents an initial implementation of a
dialogue manager that leverages the latest progress in Large Language Models
(e.g., GPT-4, IDEFICS) to enhance the traditional text-based prompts with
real-time visual input. LLMs are used to interpret both textual prompts and
visual stimuli, creating a more contextually aware conversational agent. The
system's prompt engineering, incorporating dialogue with summarisation of the
images, ensures a balance between context preservation and computational
efficiency. Six interactions with a Furhat robot powered by this system are
reported, illustrating and discussing the results obtained. By implementing
this vision-enabled dialogue system, the paper envisions a future where
conversational agents seamlessly blend textual and visual modalities, enabling
richer, more context-aware dialogues.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Using Curiosity for an Even Representation of Tasks in Continual Offline Reinforcement Learning
Abstract: In this work, we investigate the means of using curiosity on replay buffers
to improve offline multi-task continual reinforcement learning when tasks,
which are defined by the non-stationarity in the environment, are non labeled
and not evenly exposed to the learner in time. In particular, we investigate
the use of curiosity both as a tool for task boundary detection and as a
priority metric when it comes to retaining old transition tuples, which we
respectively use to propose two different buffers. Firstly, we propose a Hybrid
Reservoir Buffer with Task Separation (HRBTS), where curiosity is used to
detect task boundaries that are not known due to the task agnostic nature of
the problem. Secondly, by using curiosity as a priority metric when it comes to
retaining old transition tuples, a Hybrid Curious Buffer (HCB) is proposed. We
ultimately show that these buffers, in conjunction with regular reinforcement
learning algorithms, can be used to alleviate the catastrophic forgetting issue
suffered by the state of the art on replay buffers when the agent's exposure to
tasks is not equal along time. We evaluate catastrophic forgetting and the
efficiency of our proposed buffers against the latest works such as the Hybrid
Reservoir Buffer (HRB) and the Multi-Time Scale Replay Buffer (MTR) in three
different continual reinforcement learning settings. Experiments were done on
classical control tasks and Metaworld environment. Experiments show that our
proposed replay buffers display better immunity to catastrophic forgetting
compared to existing works in most of the settings.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: FFINet: Future Feedback Interaction Network for Motion Forecasting
Abstract: Motion forecasting plays a crucial role in autonomous driving, with the aim
of predicting the future reasonable motions of traffic agents. Most existing
methods mainly model the historical interactions between agents and the
environment, and predict multi-modal trajectories in a feedforward process,
ignoring potential trajectory changes caused by future interactions between
agents. In this paper, we propose a novel Future Feedback Interaction Network
(FFINet) to aggregate features the current observations and potential future
interactions for trajectory prediction. Firstly, we employ different
spatial-temporal encoders to embed the decomposed position vectors and the
current position of each scene, providing rich features for the subsequent
cross-temporal aggregation. Secondly, the relative interaction and
cross-temporal aggregation strategies are sequentially adopted to integrate
features in the current fusion module, observation interaction module, future
feedback module and global fusion module, in which the future feedback module
can enable the understanding of pre-action by feeding the influence of preview
information to feedforward prediction. Thirdly, the comprehensive interaction
features are further fed into final predictor to generate the joint predicted
trajectories of multiple agents. Extensive experimental results show that our
FFINet achieves the state-of-the-art performance on Argoverse 1 and Argoverse 2
motion forecasting benchmarks.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Do large language models and humans have similar behaviors in causal inference with script knowledge?
Abstract: Recently, large pre-trained language models (LLMs) have demonstrated superior
language understanding abilities, including zero-shot causal reasoning.
However, it is unclear to what extent their capabilities are similar to human
ones. We here study the processing of an event $B$ in a script-based story,
which causally depends on a previous event $A$. In our manipulation, event $A$
is stated, negated, or omitted in an earlier section of the text. We first
conducted a self-paced reading experiment, which showed that humans exhibit
significantly longer reading times when causal conflicts exist ($\neg A
\rightarrow B$) than under logical conditions ($A \rightarrow B$). However,
reading times remain similar when cause A is not explicitly mentioned,
indicating that humans can easily infer event B from their script knowledge. We
then tested a variety of LLMs on the same data to check to what extent the
models replicate human behavior. Our experiments show that 1) only recent LLMs,
like GPT-3 or Vicuna, correlate with human behavior in the $\neg A \rightarrow
B$ condition. 2) Despite this correlation, all models still fail to predict
that $nil \rightarrow B$ is less surprising than $\neg A \rightarrow B$,
indicating that LLMs still have difficulties integrating script knowledge. Our
code and collected data set are available at
https://github.com/tony-hong/causal-script.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Language-Guided Transformer for Federated Multi-Label Classification
Abstract: Federated Learning (FL) is an emerging paradigm that enables multiple users
to collaboratively train a robust model in a privacy-preserving manner without
sharing their private data. Most existing approaches of FL only consider
traditional single-label image classification, ignoring the impact when
transferring the task to multi-label image classification. Nevertheless, it is
still challenging for FL to deal with user heterogeneity in their local data
distribution in the real-world FL scenario, and this issue becomes even more
severe in multi-label image classification. Inspired by the recent success of
Transformers in centralized settings, we propose a novel FL framework for
multi-label classification. Since partial label correlation may be observed by
local clients during training, direct aggregation of locally updated models
would not produce satisfactory performances. Thus, we propose a novel FL
framework of Language-Guided Transformer (FedLGT) to tackle this challenging
task, which aims to exploit and transfer knowledge across different clients for
learning a robust global model. Through extensive experiments on various
multi-label datasets (e.g., FLAIR, MS-COCO, etc.), we show that our FedLGT is
able to achieve satisfactory performance and outperforms standard FL techniques
under multi-label FL scenarios. Code is available at
https://github.com/Jack24658735/FedLGT.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Optimizing Fault-Tolerant Quality-Guaranteed Sensor Deployments for UAV Localization in Critical Areas via Computational Geometry
Abstract: The increasing spreading of small commercial Unmanned Aerial Vehicles (UAVs,
aka drones) presents serious threats for critical areas such as airports, power
plants, governmental and military facilities. In fact, such UAVs can easily
disturb or jam radio communications, collide with other flying objects, perform
espionage activity, and carry offensive payloads, e.g., weapons or explosives.
A central problem when designing surveillance solutions for the localization of
unauthorized UAVs in critical areas is to decide how many triangulating sensors
to use, and where to deploy them to optimise both coverage and cost
effectiveness.
  In this article, we compute deployments of triangulating sensors for UAV
localization, optimizing a given blend of metrics, namely: coverage under
multiple sensing quality levels, cost-effectiveness, fault-tolerance. We focus
on large, complex 3D regions, which exhibit obstacles (e.g., buildings),
varying terrain elevation, different coverage priorities, constraints on
possible sensors placement. Our novel approach relies on computational geometry
and statistical model checking, and enables the effective use of off-the-shelf
AI-based black-box optimizers. Moreover, our method allows us to compute a
closed-form, analytical representation of the region uncovered by a sensor
deployment, which provides the means for rigorous, formal certification of the
quality of the latter.
  We show the practical feasibility of our approach by computing optimal sensor
deployments for UAV localization in two large, complex 3D critical regions, the
Rome Leonardo Da Vinci International Airport (FCO) and the Vienna International
Center (VIC), using NOMAD as our state-of-the-art underlying optimization
engine. Results show that we can compute optimal sensor deployments within a
few hours on a standard workstation and within minutes on a small parallel
infrastructure.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Learning county from pixels: Corn yield prediction with attention-weighted multiple instance learning
Abstract: Remote sensing technology has become a promising tool in yield prediction.
Most prior work employs satellite imagery for county-level corn yield
prediction by spatially aggregating all pixels within a county into a single
value, potentially overlooking the detailed information and valuable insights
offered by more granular data. To this end, this research examines each county
at the pixel level and applies multiple instance learning to leverage detailed
information within a county. In addition, our method addresses the "mixed
pixel" issue caused by the inconsistent resolution between feature datasets and
crop mask, which may introduce noise into the model and therefore hinder
accurate yield prediction. Specifically, the attention mechanism is employed to
automatically assign weights to different pixels, which can mitigate the
influence of mixed pixels. The experimental results show that the developed
model outperforms four other machine learning models over the past five years
in the U.S. corn belt and demonstrates its best performance in 2022, achieving
a coefficient of determination (R2) value of 0.84 and a root mean square error
(RMSE) of 0.83. This paper demonstrates the advantages of our approach from
both spatial and temporal perspectives. Furthermore, through an in-depth study
of the relationship between mixed pixels and attention, it is verified that our
approach can capture critical feature information while filtering out noise
from mixed pixels.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data
Abstract: Recent advances in supervised deep learning techniques have demonstrated the
possibility to remotely measure human physiological vital signs (e.g.,
photoplethysmograph, heart rate) just from facial videos. However, the
performance of these methods heavily relies on the availability and diversity
of real labeled data. Yet, collecting large-scale real-world data with
high-quality labels is typically challenging and resource intensive, which also
raises privacy concerns when storing personal bio-metric data. Synthetic
video-based datasets (e.g., SCAMPS \cite{mcduff2022scamps}) with
photo-realistic synthesized avatars are introduced to alleviate the issues
while providing high-quality synthetic data. However, there exists a
significant gap between synthetic and real-world data, which hinders the
generalization of neural models trained on these synthetic datasets. In this
paper, we proposed several measures to add real-world noise to synthetic
physiological signals and corresponding facial videos. We experimented with
individual and combined augmentation methods and evaluated our framework on
three public real-world datasets. Our results show that we were able to reduce
the average MAE from 6.9 to 2.0.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Uncovering communities of pipelines in the task-fMRI analytical space
Abstract: Functional magnetic resonance imaging analytical workflows are highly
flexible with no definite consensus on how to choose a pipeline. While methods
have been developed to explore this analytical space, there is still a lack of
understanding of the relationships between the different pipelines. We use
community detection algorithms to explore the pipeline space and assess its
stability across different contexts. We show that there are subsets of
pipelines that give similar results, especially those sharing specific
parameters (e.g. number of motion regressors, software packages, etc.), with
relative stability across groups of participants. By visualizing the
differences between these subsets, we describe the effect of pipeline
parameters and derive general relationships in the analytical space.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Generating Interpretable Networks using Hypernetworks
Abstract: An essential goal in mechanistic interpretability to decode a network, i.e.,
to convert a neural network's raw weights to an interpretable algorithm. Given
the difficulty of the decoding problem, progress has been made to understand
the easier encoding problem, i.e., to convert an interpretable algorithm into
network weights. Previous works focus on encoding existing algorithms into
networks, which are interpretable by definition. However, focusing on encoding
limits the possibility of discovering new algorithms that humans have never
stumbled upon, but that are nevertheless interpretable. In this work, we
explore the possibility of using hypernetworks to generate interpretable
networks whose underlying algorithms are not yet known. The hypernetwork is
carefully designed such that it can control network complexity, leading to a
diverse family of interpretable algorithms ranked by their complexity. All of
them are interpretable in hindsight, although some of them are less intuitive
to humans, hence providing new insights regarding how to "think" like a neural
network. For the task of computing L1 norms, hypernetworks find three
algorithms: (a) the double-sided algorithm, (b) the convexity algorithm, (c)
the pudding algorithm, although only the first algorithm was expected by the
authors before experiments. We automatically classify these algorithms and
analyze how these algorithmic phases develop during training, as well as how
they are affected by complexity control. Furthermore, we show that a trained
hypernetwork can correctly construct models for input dimensions not seen in
training, demonstrating systematic generalization.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Abstract: Deep reinforcement learning (DRL) provides a promising way for intelligent
agents (e.g., autonomous vehicles) to learn to navigate complex scenarios.
However, DRL with neural networks as function approximators is typically
considered a black box with little explainability and often suffers from
suboptimal performance, especially for autonomous navigation in highly
interactive multi-agent environments. To address these issues, we propose three
auxiliary tasks with spatio-temporal relational reasoning and integrate them
into the standard DRL framework, which improves the decision making performance
and provides explainable intermediate indicators. We propose to explicitly
infer the internal states (i.e., traits and intentions) of surrounding agents
(e.g., human drivers) as well as to predict their future trajectories in the
situations with and without the ego agent through counterfactual reasoning.
These auxiliary tasks provide additional supervision signals to infer the
behavior patterns of other interactive agents. Multiple variants of framework
integration strategies are compared. We also employ a spatio-temporal graph
neural network to encode relations between dynamic entities, which enhances
both internal state inference and decision making of the ego agent. Moreover,
we propose an interactivity estimation mechanism based on the difference
between predicted trajectories in these two situations, which indicates the
degree of influence of the ego agent on other agents. To validate the proposed
method, we design an intersection driving simulator based on the Intelligent
Intersection Driver Model (IIDM) that simulates vehicles and pedestrians. Our
approach achieves robust and state-of-the-art performance in terms of standard
evaluation metrics and provides explainable intermediate indicators (i.e.,
internal states, and interactivity scores) for decision making.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs
Abstract: Responding to the thousands of student questions on online QA platforms each
semester has a considerable human cost, particularly in computing courses with
rapidly growing enrollments. To address the challenges of scalable and
intelligent question-answering (QA), we introduce an innovative solution that
leverages open-source Large Language Models (LLMs) from the LLaMA-2 family to
ensure data privacy. Our approach combines augmentation techniques such as
retrieval augmented generation (RAG), supervised fine-tuning (SFT), and
learning from human preferences data using Direct Preference Optimization
(DPO). Through extensive experimentation on a Piazza dataset from an
introductory CS course, comprising 10,000 QA pairs and 1,500 pairs of
preference data, we demonstrate a significant 30% improvement in the quality of
answers, with RAG being a particularly impactful addition. Our contributions
include the development of a novel architecture for educational QA, extensive
evaluations of LLM performance utilizing both human assessments and LLM-based
metrics, and insights into the challenges and future directions of educational
data processing. This work paves the way for the development of AI-TA, an
intelligent QA assistant customizable for courses with an online QA platform

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework
Abstract: Modern Large language models (LLMs) can still generate responses that may not
be aligned with human expectations or values. While many weight-based alignment
methods have been proposed, many of them still leave models vulnerable to
attacks when used on their own. To help mitigate this issue, we introduce
Bergeron, a framework designed to improve the robustness of LLMs against
adversarial attacks. Bergeron employs a two-tiered architecture. Here, a
secondary LLM serves as a simulated conscience that safeguards a primary LLM.
We do this by monitoring for and correcting potentially harmful text within
both the prompt inputs and the generated outputs of the primary LLM. Empirical
evaluation shows that Bergeron can improve the alignment and robustness of
several popular LLMs without costly fine-tuning. It aids both open-source and
black-box LLMs by complementing and reinforcing their existing alignment
training.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Towards Generalized Multi-stage Clustering: Multi-view Self-distillation
Abstract: Existing multi-stage clustering methods independently learn the salient
features from multiple views and then perform the clustering task.
Particularly, multi-view clustering (MVC) has attracted a lot of attention in
multi-view or multi-modal scenarios. MVC aims at exploring common semantics and
pseudo-labels from multiple views and clustering in a self-supervised manner.
However, limited by noisy data and inadequate feature learning, such a
clustering paradigm generates overconfident pseudo-labels that mis-guide the
model to produce inaccurate predictions. Therefore, it is desirable to have a
method that can correct this pseudo-label mistraction in multi-stage clustering
to avoid the bias accumulation. To alleviate the effect of overconfident
pseudo-labels and improve the generalization ability of the model, this paper
proposes a novel multi-stage deep MVC framework where multi-view
self-distillation (DistilMVC) is introduced to distill dark knowledge of label
distribution. Specifically, in the feature subspace at different hierarchies,
we explore the common semantics of multiple views through contrastive learning
and obtain pseudo-labels by maximizing the mutual information between views.
Additionally, a teacher network is responsible for distilling pseudo-labels
into dark knowledge, supervising the student network and improving its
predictive capabilities to enhance the robustness. Extensive experiments on
real-world multi-view datasets show that our method has better clustering
performance than state-of-the-art methods.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Transformer as Linear Expansion of Learngene
Abstract: We propose expanding the shared Transformer module to produce and initialize
Transformers with diverse depths, enabling adaptation to dynamic resource
constraints. Drawing an analogy to genetic expansibility, we term such module
as learngene. To identify the expansion mechanism, we delve into the
relationship between the layer position and its corresponding weight value, and
find that linear function appropriately approximates this relationship.
Building on this insight, we present Transformer as Linear Expansion of
learnGene (TLEG), a novel approach for flexibly producing and initializing
Transformers of diverse depths. Specifically, to learn learngene, we firstly
construct an auxiliary Transformer linearly expanded from learngene, after
which we train it through employing soft distillation. Subsequently, we can
produce and initialize Transformers of varying depths via linearly expanding
the well-trained learngene, thereby supporting diverse downstream scenarios.
Extensive experiments on ImageNet-1K classification demonstrate that TLEG
achieves comparable or better performance compared to many individual models
trained from scratch, while reducing around 2$\times$ training cost. When
transferring one model to several downstream classification datasets, TLEG
surpasses existing initialization methods by a large margin (e.g., +6.87% on
iNat 2019 and +7.66% on CIFAR-100). Under the situation where we need to
produce models of different scales adapting for different resource constraints,
TLEG achieves comparable results while reducing around 19$\times$ parameters
stored to initialize these models and around 5$\times$ training costs, in
contrast to the pre-training and fine-tuning approach.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Class-Incremental Continual Learning for General Purpose Healthcare Models
Abstract: Healthcare clinics regularly encounter dynamic data that changes due to
variations in patient populations, treatment policies, medical devices, and
emerging disease patterns. Deep learning models can suffer from catastrophic
forgetting when fine-tuned in such scenarios, causing poor performance on
previously learned tasks. Continual learning allows learning on new tasks
without performance drop on previous tasks. In this work, we investigate the
performance of continual learning models on four different medical imaging
scenarios involving ten classification datasets from diverse modalities,
clinical specialties, and hospitals. We implement various continual learning
approaches and evaluate their performance in these scenarios. Our results
demonstrate that a single model can sequentially learn new tasks from different
specialties and achieve comparable performance to naive methods. These findings
indicate the feasibility of recycling or sharing models across the same or
different medical specialties, offering another step towards the development of
general-purpose medical imaging AI that can be shared across institutions.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning
Abstract: Dynamic Bayesian Networks (DBNs), renowned for their interpretability, have
become increasingly vital in representing complex stochastic processes in
various domains such as gene expression analysis, healthcare, and traffic
prediction. Structure learning of DBNs from data is challenging, particularly
for datasets with thousands of variables. Most current algorithms for DBN
structure learning are adaptations from those used in static Bayesian Networks
(BNs), and are typically focused on small-scale problems. In order to solve
large-scale problems while taking full advantage of existing algorithms, this
paper introduces a novel divide-and-conquer strategy, originally developed for
static BNs, and adapts it for large-scale DBN structure learning. In this work,
we specifically concentrate on 2 Time-sliced Bayesian Networks (2-TBNs), a
special class of DBNs. Furthermore, we leverage the prior knowledge of 2-TBNs
to enhance the performance of the strategy we introduce. Our approach
significantly improves the scalability and accuracy of 2-TBN structure
learning. Experimental results demonstrate the effectiveness of our method,
showing substantial improvements over existing algorithms in both computational
efficiency and structure learning accuracy. On problem instances with more than
1,000 variables, our approach improves two accuracy metrics by 74.45% and
110.94% on average , respectively, while reducing runtime by 93.65% on average.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Framework for Realistic Simulation of Daily Human Activity
Abstract: For social robots like Astro which interact with and adapt to the daily
movements of users within the home, realistic simulation of human activity is
needed for feature development and testing. This paper presents a framework for
simulating daily human activity patterns in home environments at scale,
supporting manual configurability of different personas or activity patterns,
variation of activity timings, and testing on multiple home layouts. We
introduce a method for specifying day-to-day variation in schedules and present
a bidirectional constraint propagation algorithm for generating schedules from
templates. We validate the expressive power of our framework through a use case
scenario analysis and demonstrate that our method can be used to generate data
closely resembling human behavior from three public datasets and a
self-collected dataset. Our contribution supports systematic testing of social
robot behaviors at scale, enables procedural generation of synthetic datasets
of human movement in different households, and can help minimize bias in
training data, leading to more robust and effective robots for home
environments.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Stable Diffusion Reference Only: Image Prompt and Blueprint Jointly Guided Multi-Condition Diffusion Model for Secondary Painting
Abstract: Stable Diffusion and ControlNet have achieved excellent results in the field
of image generation and synthesis. However, due to the granularity and method
of its control, the efficiency improvement is limited for professional artistic
creations such as comics and animation production whose main work is secondary
painting. In the current workflow, fixing characters and image styles often
need lengthy text prompts, and even requires further training through
TextualInversion, DreamBooth or other methods, which is very complicated and
expensive for painters. Therefore, we present a new method in this paper,
Stable Diffusion Reference Only, a images-to-image self-supervised model that
uses only two types of conditional images for precise control generation to
accelerate secondary painting. The first type of conditional image serves as an
image prompt, supplying the necessary conceptual and color information for
generation. The second type is blueprint image, which controls the visual
structure of the generated image. It is natively embedded into the original
UNet, eliminating the need for ControlNet. We released all the code for the
module and pipeline, and trained a controllable character line art coloring
model at https://github.com/aihao2000/stable-diffusion-reference-only, that
achieved state-of-the-art results in this field. This verifies the
effectiveness of the structure and greatly improves the production efficiency
of animations, comics, and fanworks.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Optimizing Dense Feed-Forward Neural Networks
Abstract: Deep learning models have been widely used during the last decade due to
their outstanding learning and abstraction capacities. However, one of the main
challenges any scientist has to face using deep learning models is to establish
the network's architecture. Due to this difficulty, data scientists usually
build over complex models and, as a result, most of them result computationally
intensive and impose a large memory footprint, generating huge costs,
contributing to climate change and hindering their use in computational-limited
devices. In this paper, we propose a novel feed-forward neural network
constructing method based on pruning and transfer learning. Its performance has
been thoroughly assessed in classification and regression problems. Without any
accuracy loss, our approach can compress the number of parameters by more than
70%. Even further, choosing the pruning parameter carefully, most of the
refined models outperform original ones. We also evaluate the transfer learning
level comparing the refined model and the original one training from scratch a
neural network with the same hyper parameters as the optimized model. The
results obtained show that our constructing method not only helps in the design
of more efficient models but also more effective ones.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: "It's not like Jarvis, but it's pretty close!" -- Examining ChatGPT's Usage among Undergraduate Students in Computer Science
Abstract: Large language models (LLMs) such as ChatGPT and Google Bard have garnered
significant attention in the academic community. Previous research has
evaluated these LLMs for various applications such as generating programming
exercises and solutions. However, these evaluations have predominantly been
conducted by instructors and researchers, not considering the actual usage of
LLMs by students. This study adopts a student-first approach to comprehensively
understand how undergraduate computer science students utilize ChatGPT, a
popular LLM, released by OpenAI. We employ a combination of student surveys and
interviews to obtain valuable insights into the benefits, challenges, and
suggested improvements related to ChatGPT. Our findings suggest that a majority
of students (over 57%) have a convincingly positive outlook towards adopting
ChatGPT as an aid in coursework-related tasks. However, our research also
highlights various challenges that must be resolved for long-term acceptance of
ChatGPT amongst students. The findings from this investigation have broader
implications and may be applicable to other LLMs and their role in computing
education.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems
Abstract: Encoding legislative text in a formal representation is an important
prerequisite to different tasks in the field of AI & Law. For example,
rule-based expert systems focused on legislation can support laypeople in
understanding how legislation applies to them and provide them with helpful
context and information. However, the process of analyzing legislation and
other sources to encode it in the desired formal representation can be
time-consuming and represents a bottleneck in the development of such systems.
Here, we investigate to what degree large language models (LLMs), such as
GPT-4, are able to automatically extract structured representations from
legislation. We use LLMs to create pathways from legislation, according to the
JusticeBot methodology for legal decision support systems, evaluate the
pathways and compare them to manually created pathways. The results are
promising, with 60% of generated pathways being rated as equivalent or better
than manually created ones in a blind comparison. The approach suggests a
promising path to leverage the capabilities of LLMs to ease the costly
development of systems based on symbolic approaches that are transparent and
explainable.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A DRL solution to help reduce the cost in waiting time of securing a traffic light for cyclists
Abstract: Cyclists prefer to use infrastructure that separates them from motorized
traffic. Using a traffic light to segregate car and bike flows, with the
addition of bike-specific green phases, is a lightweight and cheap solution
that can be deployed dynamically to assess the opportunity of a heavier
infrastructure such as a separate bike lane. To compensate for the increased
waiting time induced by these new phases, we introduce in this paper a deep
reinforcement learning solution that adapts the green phase cycle of a traffic
light to the traffic. Vehicle counter data are used to compare the DRL approach
with the actuated traffic light control algorithm over whole days. Results show
that DRL achieves better minimization of vehicle waiting time at almost all
hours. Our DRL approach is also robust to moderate changes in bike traffic. The
code of this paper is available at
https://github.com/LucasMagnana/A-DRL-solution-to-help-reduce-the-cost-in-waiting-time-of-securing-a-traffic-light-for-cyclists.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Coupling Fairness and Pruning in a Single Run: a Bi-level Optimization Perspective
Abstract: Deep neural networks have demonstrated remarkable performance in various
tasks. With a growing need for sparse deep learning, model compression
techniques, especially pruning, have gained significant attention. However,
conventional pruning techniques can inadvertently exacerbate algorithmic bias,
resulting in unequal predictions. To address this, we define a fair pruning
task where a sparse model is derived subject to fairness requirements. In
particular, we propose a framework to jointly optimize the pruning mask and
weight update processes with fairness constraints. This framework is engineered
to compress models that maintain performance while ensuring fairness in a
single execution. To this end, we formulate the fair pruning problem as a novel
constrained bi-level optimization task and derive efficient and effective
solving strategies. We design experiments spanning various datasets and
settings to validate our proposed method. Our empirical analysis contrasts our
framework with several mainstream pruning strategies, emphasizing our method's
superiority in maintaining model fairness, performance, and efficiency.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise
Abstract: Large Language Models (LLMs) exhibit impressive reasoning and data
augmentation capabilities in various NLP tasks. However, what about small
models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant
fundamentals, chain of thought, and common mistakes for most NLP samples, which
makes annotation more than just an answer, thus allowing other models to learn
"why" instead of just "what". The TeacherLM-7.1B model achieved a zero-shot
score of 52.3 on MMLU, surpassing most models with over 100B parameters. Even
more remarkable is its data augmentation ability. Based on TeacherLM-7.1B, we
augmented 58 NLP datasets and taught various student models with different
parameters from OPT and BLOOM series in a multi-task setting. The experimental
results indicate that the data augmentation provided by TeacherLM has brought
significant benefits. We will release the TeacherLM series of models and
augmented datasets as open-source.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model
Abstract: Efficiently modeling spatio-temporal (ST) physical processes and observations
presents a challenging problem for the deep learning community. Many recent
studies have concentrated on meticulously reconciling various advantages,
leading to designed models that are neither simple nor practical. To address
this issue, this paper presents a systematic study on existing shortcomings
faced by off-the-shelf models, including lack of local fidelity, poor
prediction performance over long time-steps,low scalability, and inefficiency.
To systematically address the aforementioned problems, we propose an
EarthFarseer, a concise framework that combines parallel local convolutions and
global Fourier-based transformer architectures, enabling dynamically capture
the local-global spatial interactions and dependencies. EarthFarseer also
incorporates a multi-scale fully convolutional and Fourier architectures to
efficiently and effectively capture the temporal evolution. Our proposal
demonstrates strong adaptability across various tasks and datasets, with fast
convergence and better local fidelity in long time-steps predictions. Extensive
experiments and visualizations over eight human society physical and natural
physical datasets demonstrates the state-of-the-art performance of
EarthFarseer. We release our code at
https://github.com/easylearningscores/EarthFarseer.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment
Abstract: Deep learning techniques have greatly enhanced the performance of fire
detection in videos. However, video-based fire detection models heavily rely on
labeled data, and the process of data labeling is particularly costly and
time-consuming, especially when dealing with videos. Considering the limited
quantity of labeled video data, we propose a semi-supervised fire detection
model called FireMatch, which is based on consistency regularization and
adversarial distribution alignment. Specifically, we first combine consistency
regularization with pseudo-label. For unlabeled data, we design video data
augmentation to obtain corresponding weakly augmented and strongly augmented
samples. The proposed model predicts weakly augmented samples and retains
pseudo-label above a threshold, while training on strongly augmented samples to
predict these pseudo-labels for learning more robust feature representations.
Secondly, we generate video cross-set augmented samples by adversarial
distribution alignment to expand the training data and alleviate the decline in
classification performance caused by insufficient labeled data. Finally, we
introduce a fairness loss to help the model produce diverse predictions for
input samples, thereby addressing the issue of high confidence with the
non-fire class in fire classification scenarios. The FireMatch achieved an
accuracy of 76.92% and 91.81% on two real-world fire datasets, respectively.
The experimental results demonstrate that the proposed method outperforms the
current state-of-the-art semi-supervised classification methods.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases
Abstract: Enterprise applications of Large Language Models (LLMs) hold promise for
question answering on enterprise SQL databases. However, the extent to which
LLMs can accurately respond to enterprise questions in such databases remains
unclear, given the absence of suitable Text-to-SQL benchmarks tailored to
enterprise settings. Additionally, the potential of Knowledge Graphs (KGs) to
enhance LLM-based question answering by providing business context is not well
understood. This study aims to evaluate the accuracy of LLM-powered question
answering systems in the context of enterprise questions and SQL databases,
while also exploring the role of knowledge graphs in improving accuracy. To
achieve this, we introduce a benchmark comprising an enterprise SQL schema in
the insurance domain, a range of enterprise queries encompassing reporting to
metrics, and a contextual layer incorporating an ontology and mappings that
define a knowledge graph. Our primary finding reveals that question answering
using GPT-4, with zero-shot prompts directly on SQL databases, achieves an
accuracy of 16%. Notably, this accuracy increases to 54% when questions are
posed over a Knowledge Graph representation of the enterprise SQL database.
Therefore, investing in Knowledge Graph provides higher accuracy for LLM
powered question answering systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A Path to Simpler Models Starts With Noise
Abstract: The Rashomon set is the set of models that perform approximately equally well
on a given dataset, and the Rashomon ratio is the fraction of all models in a
given hypothesis space that are in the Rashomon set. Rashomon ratios are often
large for tabular datasets in criminal justice, healthcare, lending, education,
and in other areas, which has practical implications about whether simpler
models can attain the same level of accuracy as more complex models. An open
question is why Rashomon ratios often tend to be large. In this work, we
propose and study a mechanism of the data generation process, coupled with
choices usually made by the analyst during the learning process, that
determines the size of the Rashomon ratio. Specifically, we demonstrate that
noisier datasets lead to larger Rashomon ratios through the way that
practitioners train models. Additionally, we introduce a measure called pattern
diversity, which captures the average difference in predictions between
distinct classification patterns in the Rashomon set, and motivate why it tends
to increase with label noise. Our results explain a key aspect of why simpler
models often tend to perform as well as black box models on complex, noisier
datasets.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows
Abstract: Designing predictive models for subjective problems in natural language
processing (NLP) remains challenging. This is mainly due to its
non-deterministic nature and different perceptions of the content by different
humans. It may be solved by Personalized Natural Language Processing (PNLP),
where the model exploits additional information about the reader to make more
accurate predictions. However, current approaches require complete information
about the recipients to be straight embedded. Besides, the recent methods focus
on deterministic inference or simple frequency-based estimations of the
probabilities. In this work, we overcome this limitation by proposing a novel
approach to capture the uncertainty of the forecast using conditional
Normalizing Flows. This allows us to model complex multimodal distributions and
to compare various models using negative log-likelihood (NLL). In addition, the
new solution allows for various interpretations of possible reader perception
thanks to the available sampling function. We validated our method on three
challenging, subjective NLP tasks, including emotion recognition and hate
speech. The comparative analysis of generalized and personalized approaches
revealed that our personalized solutions significantly outperform the baseline
and provide more precise uncertainty estimates. The impact on the text
interpretability and uncertainty studies are presented as well. The information
brought by the developed methods makes it possible to build hybrid models whose
effectiveness surpasses classic solutions. In addition, an analysis and
visualization of the probabilities of the given decisions for texts with high
entropy of annotations and annotators with mixed views were carried out.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Leveraging Reinforcement Learning and Large Language Models for Code Optimization
Abstract: Code optimization is a daunting task that requires a significant level of
expertise from experienced programmers. This level of expertise is not
sufficient when compared to the rapid development of new hardware
architectures. Towards advancing the whole code optimization process, recent
approaches rely on machine learning and artificial intelligence techniques.
This paper introduces a new framework to decrease the complexity of code
optimization. The proposed framework builds on large language models (LLMs) and
reinforcement learning (RL) and enables LLMs to receive feedback from their
environment (i.e., unit tests) during the fine-tuning process. We compare our
framework with existing state-of-the-art models and show that it is more
efficient with respect to speed and computational usage, as a result of the
decrement in training steps and its applicability to models with fewer
parameters. Additionally, our framework reduces the possibility of logical and
syntactical errors. Toward evaluating our approach, we run several experiments
on the PIE dataset using a CodeT5 language model and RRHF, a new reinforcement
learning algorithm. We adopt a variety of evaluation metrics with regards to
optimization quality, and speedup. The evaluation results demonstrate that the
proposed framework has similar results in comparison with existing models using
shorter training times and smaller pre-trained models. In particular, we
accomplish an increase of 5.6% and 2.2 over the baseline models concerning the
%OP T and SP metrics.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Unified Segment-to-Segment Framework for Simultaneous Sequence Generation
Abstract: Simultaneous sequence generation is a pivotal task for real-time scenarios,
such as streaming speech recognition, simultaneous machine translation and
simultaneous speech translation, where the target sequence is generated while
receiving the source sequence. The crux of achieving high-quality generation
with low latency lies in identifying the optimal moments for generating,
accomplished by learning a mapping between the source and target sequences.
However, existing methods often rely on task-specific heuristics for different
sequence types, limiting the model's capacity to adaptively learn the
source-target mapping and hindering the exploration of multi-task learning for
various simultaneous tasks. In this paper, we propose a unified
segment-to-segment framework (Seg2Seg) for simultaneous sequence generation,
which learns the mapping in an adaptive and unified manner. During the process
of simultaneous generation, the model alternates between waiting for a source
segment and generating a target segment, making the segment serve as the
natural bridge between the source and target. To accomplish this, Seg2Seg
introduces a latent segment as the pivot between source to target and explores
all potential source-target mappings via the proposed expectation training,
thereby learning the optimal moments for generating. Experiments on multiple
simultaneous generation tasks demonstrate that Seg2Seg achieves
state-of-the-art performance and exhibits better generality across various
tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Real Customization or Just Marketing: Are Customized Versions of Chat GPT Useful?
Abstract: Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are
revolutionizing several industries, including higher education. In this
context, LLMs can be personalized through a fine-tuning process to meet the
student demands on every particular subject, like statistics. Recently, OpenAI
has launched the possibility to fine-tune their model with a natural language
web interface, enabling the possibility to create customized GPT version
deliberately conditioned to meet the demands of a specific task. The objective
of this research is to assess the potential of the customized GPTs that have
recently been launched by OpenAI. After developing a Business Statistics
Virtual Professor (BSVP), tailored for students at the Universidad Pontificia
Comillas, its behavior was evaluated and compared with that of ChatGPT-4 Turbo.
The results lead to several conclusions. Firstly, a substantial modification in
the style of communication was observed. Following the instructions it was
trained with, BSVP provided responses in a more relatable and friendly tone,
even incorporating a few minor jokes. Secondly, and this is a matter of
relevance, when explicitly asked for something like, "I would like to practice
a programming exercise similar to those in R practice 4," BSVP was capable of
providing a far superior response: having access to contextual documentation,
it could fulfill the request, something beyond ChatGPT-4 Turbo's capabilities.
On the downside, the response times were generally higher. Lastly, regarding
overall performance, quality, depth, and alignment with the specific content of
the course, no statistically significant differences were observed in the
responses between BSVP and ChatGPT-4 Turbo. It appears that customized
assistants trained with prompts present advantages as virtual aids for
students, yet they do not constitute a substantial improvement over ChatGPT-4
Turbo.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: The Role of Chain-of-Thought in Complex Vision-Language Reasoning Task
Abstract: The study explores the effectiveness of the Chain-of-Thought approach, known
for its proficiency in language tasks by breaking them down into sub-tasks and
intermediate steps, in improving vision-language tasks that demand
sophisticated perception and reasoning. We present the "Description then
Decision" strategy, which is inspired by how humans process signals. This
strategy significantly improves probing task performance by 50%, establishing
the groundwork for future research on reasoning paradigms in complex
vision-language tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: LuminanceL1Loss: A loss function which measures percieved brightness and colour differences
Abstract: We introduce LuminanceL1Loss, a novel loss function designed to enhance the
performance of image restoration tasks. We demonstrate its superiority over MSE
when applied to the Retinexformer, BUIFD and DnCNN architectures. Our proposed
LuminanceL1Loss leverages a unique approach by transforming images into
grayscale and subsequently computing the MSE loss for both grayscale and color
channels. Experimental results demonstrate that this innovative loss function
consistently outperforms traditional methods, showcasing its potential in image
denoising and other related tasks in image reconstruction. It demonstrates
gains up to 4.7dB. The results presented in this study highlight the efficacy
of LuminanceL1Loss for various image restoration tasks.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Symbolic Planning and Code Generation for Grounded Dialogue
Abstract: Large language models (LLMs) excel at processing and generating both text and
code. However, LLMs have had limited applicability in grounded task-oriented
dialogue as they are difficult to steer toward task objectives and fail to
handle novel grounding. We present a modular and interpretable grounded
dialogue system that addresses these shortcomings by composing LLMs with a
symbolic planner and grounded code execution. Our system consists of a reader
and planner: the reader leverages an LLM to convert partner utterances into
executable code, calling functions that perform grounding. The translated
code's output is stored to track dialogue state, while a symbolic planner
determines the next appropriate response. We evaluate our system's performance
on the demanding OneCommon dialogue task, involving collaborative reference
resolution on abstract images of scattered dots. Our system substantially
outperforms the previous state-of-the-art, including improving task success in
human evaluations from 56% to 69% in the most challenging setting.

Output: Computational Linguistics