Instruction: What field is the article from?

Prompt:
Title: Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
Abstract: We present BYOKG, a universal question-answering (QA) system that can operate
on any knowledge graph (KG), requires no human-annotated training data, and can
be ready to use within a day -- attributes that are out-of-scope for current
KGQA systems. BYOKG draws inspiration from the remarkable ability of humans to
comprehend information present in an unseen KG through exploration -- starting
at random nodes, inspecting the labels of adjacent nodes and edges, and
combining them with their prior world knowledge. In BYOKG, exploration
leverages an LLM-backed symbolic agent that generates a diverse set of
query-program exemplars, which are then used to ground a retrieval-augmented
reasoning procedure to predict programs for arbitrary questions. BYOKG is
effective over both small- and large-scale graphs, showing dramatic gains in QA
accuracy over a zero-shot baseline of 27.89 and 58.02 F1 on GrailQA and MetaQA,
respectively. On GrailQA, we further show that our unsupervised BYOKG
outperforms a supervised in-context learning method, demonstrating the
effectiveness of exploration. Lastly, we find that performance of BYOKG
reliably improves with continued exploration as well as improvements in the
base LLM, notably outperforming a state-of-the-art fine-tuned model by 7.08 F1
on a sub-sampled zero-shot split of GrailQA.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Evolutionary City: Towards a Flexible, Agile and Symbiotic System
Abstract: Urban growth sometimes leads to rigid infrastructure that struggles to adapt
to changing demand. This paper introduces a novel approach, aiming to enable
cities to evolve and respond more effectively to such dynamic demand. It
identifies the limitations arising from the complexity and inflexibility of
existing urban systems. A framework is presented for enhancing the city's
adaptability perception through advanced sensing technologies, conducting
parallel simulation via graph-based techniques, and facilitating autonomous
decision-making across domains through decentralized and autonomous
organization and operation. Notably, a symbiotic mechanism is employed to
implement these technologies practically, thereby making urban management more
agile and responsive. In the case study, we explore how this approach can
optimize traffic flow by adjusting lane allocations. This case not only
enhances traffic efficiency but also reduces emissions. The proposed
evolutionary city offers a new perspective on sustainable urban development,
highliting the importance of integrated intelligence within urban systems.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Improved Face Representation via Joint Label Classification and Supervised Contrastive Clustering
Abstract: Face clustering tasks can learn hierarchical semantic information from
large-scale data, which has the potential to help facilitate face recognition.
However, there are few works on this problem. This paper explores it by
proposing a joint optimization task of label classification and supervised
contrastive clustering to introduce the cluster knowledge to the traditional
face recognition task in two ways. We first extend ArcFace with a
cluster-guided angular margin to adjust the within-class feature distribution
according to the hard level of face clustering. Secondly, we propose a
supervised contrastive clustering approach to pull the features to the cluster
center and propose the cluster-aligning procedure to align the cluster center
and the learnable class center in the classifier for joint training. Finally,
extensive qualitative and quantitative experiments on popular facial benchmarks
demonstrate the effectiveness of our paradigm and its superiority over the
existing approaches to face recognition.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Vision Encoder-Decoder Models for AI Coaching
Abstract: This research paper introduces an innovative AI coaching approach by
integrating vision-encoder-decoder models. The feasibility of this method is
demonstrated using a Vision Transformer as the encoder and GPT-2 as the
decoder, achieving a seamless integration of visual input and textual
interaction. Departing from conventional practices of employing distinct models
for image recognition and text-based coaching, our integrated architecture
directly processes input images, enabling natural question-and-answer dialogues
with the AI coach. This unique strategy simplifies model architecture while
enhancing the overall user experience in human-AI interactions. We showcase
sample results to demonstrate the capability of the model. The results
underscore the methodology's potential as a promising paradigm for creating
efficient AI coach models in various domains involving visual inputs.
Importantly, this potential holds true regardless of the particular visual
encoder or text decoder chosen. Additionally, we conducted experiments with
different sizes of GPT-2 to assess the impact on AI coach performance,
providing valuable insights into the scalability and versatility of our
proposed methodology.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Solving large flexible job shop scheduling instances by generating a diverse set of scheduling policies with deep reinforcement learning
Abstract: The Flexible Job Shop Scheduling Problem (FJSSP) has been extensively studied
in the literature, and multiple approaches have been proposed within the
heuristic, exact, and metaheuristic methods. However, the industry's demand to
be able to respond in real-time to disruptive events has generated the
necessity to be able to generate new schedules within a few seconds. Among
these methods, under this constraint, only dispatching rules (DRs) are capable
of generating schedules, even though their quality can be improved. To improve
the results, recent methods have been proposed for modeling the FJSSP as a
Markov Decision Process (MDP) and employing reinforcement learning to create a
policy that generates an optimal solution assigning operations to machines.
Nonetheless, there is still room for improvement, particularly in the larger
FJSSP instances which are common in real-world scenarios. Therefore, the
objective of this paper is to propose a method capable of robustly solving
large instances of the FJSSP. To achieve this, we propose a novel way of
modeling the FJSSP as an MDP using graph neural networks. We also present two
methods to make inference more robust: generating a diverse set of scheduling
policies that can be parallelized and limiting them using DRs. We have tested
our approach on synthetically generated instances and various public benchmarks
and found that our approach outperforms dispatching rules and achieves better
results than three other recent deep reinforcement learning methods on larger
FJSSP instances.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Can ChatGPT support software verification?
Abstract: Large language models have become increasingly effective in software
engineering tasks such as code generation, debugging and repair. Language
models like ChatGPT can not only generate code, but also explain its inner
workings and in particular its correctness. This raises the question whether we
can utilize ChatGPT to support formal software verification.
  In this paper, we take some first steps towards answering this question. More
specifically, we investigate whether ChatGPT can generate loop invariants. Loop
invariant generation is a core task in software verification, and the
generation of valid and useful invariants would likely help formal verifiers.
To provide some first evidence on this hypothesis, we ask ChatGPT to annotate
106 C programs with loop invariants. We check validity and usefulness of the
generated invariants by passing them to two verifiers, Frama-C and CPAchecker.
Our evaluation shows that ChatGPT is able to produce valid and useful
invariants allowing Frama-C to verify tasks that it could not solve before.
Based on our initial insights, we propose ways of combining ChatGPT (or large
language models in general) and software verifiers, and discuss current
limitations and open issues.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison
Abstract: Public opinion is shaped by the information news media provide, and that
information in turn may be shaped by the ideological preferences of media
outlets. But while much attention has been devoted to media bias via overt
ideological language or topic selection, a more unobtrusive way in which the
media shape opinion is via the strategic inclusion or omission of partisan
events that may support one side or the other. We develop a latent
variable-based framework to predict the ideology of news articles by comparing
multiple articles on the same story and identifying partisan events whose
inclusion or omission reveals ideology. Our experiments first validate the
existence of partisan event selection, and then show that article alignment and
cross-document comparison detect partisan events and article ideology better
than competitive baselines. Our results reveal the high-level form of media
bias, which is present even among mainstream media with strong norms of
objectivity and nonpartisanship. Our codebase and dataset are available at
https://github.com/launchnlp/ATC.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Levels of AGI: Operationalizing Progress on the Path to AGI
Abstract: We propose a framework for classifying the capabilities and behavior of
Artificial General Intelligence (AGI) models and their precursors. This
framework introduces levels of AGI performance, generality, and autonomy. It is
our hope that this framework will be useful in an analogous way to the levels
of autonomous driving, by providing a common language to compare models, assess
risks, and measure progress along the path to AGI. To develop our framework, we
analyze existing definitions of AGI, and distill six principles that a useful
ontology for AGI should satisfy. These principles include focusing on
capabilities rather than mechanisms; separately evaluating generality and
performance; and defining stages along the path toward AGI, rather than
focusing on the endpoint. With these principles in mind, we propose 'Levels of
AGI' based on depth (performance) and breadth (generality) of capabilities, and
reflect on how current systems fit into this ontology. We discuss the
challenging requirements for future benchmarks that quantify the behavior and
capabilities of AGI models against these levels. Finally, we discuss how these
levels of AGI interact with deployment considerations such as autonomy and
risk, and emphasize the importance of carefully selecting Human-AI Interaction
paradigms for responsible and safe deployment of highly capable AI systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters
Abstract: Coding of unstructured clinical free-text to produce interoperable structured
data is essential to improve direct care, support clinical communication and to
enable clinical research.However, manual clinical coding is difficult and time
consuming, which motivates the development and use of natural language
processing for automated coding. This work evaluates the quality and
consistency of both manual and automated clinical coding of diagnoses from
hospital outpatient letters. Using 100 randomly selected letters, two human
clinicians performed coding of diagnosis lists to SNOMED CT. Automated coding
was also performed using IMO's Concept Tagger. A gold standard was constructed
by a panel of clinicians from a subset of the annotated diagnoses. This was
used to evaluate the quality and consistency of both manual and automated
coding via (1) a distance-based metric, treating SNOMED CT as a graph, and (2)
a qualitative metric agreed upon by the panel of clinicians. Correlation
between the two metrics was also evaluated. Comparing human and
computer-generated codes to the gold standard, the results indicate that humans
slightly out-performed automated coding, while both performed notably better
when there was only a single diagnosis contained in the free-text description.
Automated coding was considered acceptable by the panel of clinicians in
approximately 90% of cases.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Assessing AI Impact Assessments: A Classroom Study
Abstract: Artificial Intelligence Impact Assessments ("AIIAs"), a family of tools that
provide structured processes to imagine the possible impacts of a proposed AI
system, have become an increasingly popular proposal to govern AI systems.
Recent efforts from government or private-sector organizations have proposed
many diverse instantiations of AIIAs, which take a variety of forms ranging
from open-ended questionnaires to graded score-cards. However, to date that has
been limited evaluation of existing AIIA instruments. We conduct a classroom
study (N = 38) at a large research-intensive university (R1) in an elective
course focused on the societal and ethical implications of AI. We assign
students to different organizational roles (for example, an ML scientist or
product manager) and ask participant teams to complete one of three existing AI
impact assessments for one of two imagined generative AI systems. In our
thematic analysis of participants' responses to pre- and post-activity
questionnaires, we find preliminary evidence that impact assessments can
influence participants' perceptions of the potential risks of generative AI
systems, and the level of responsibility held by AI experts in addressing
potential harm. We also discover a consistent set of limitations shared by
several existing AIIA instruments, which we group into concerns about their
format and content, as well as the feasibility and effectiveness of the
activity in foreseeing and mitigating potential harms. Drawing on the findings
of this study, we provide recommendations for future work on developing and
validating AIIAs.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation
Abstract: Current text-to-image editing models often encounter challenges with smoothly
manipulating multiple attributes using a single instruction. Taking inspiration
from the Chain-of-Thought prompting technique utilized in language models, we
present an innovative concept known as Chain-of-Instruct Editing (CoIE), which
enhances the capabilities of these models through step-by-step editing using a
series of instructions. In particular, in the context of face manipulation, we
leverage the contextual learning abilities of a pretrained Large Language Model
(LLM), such as GPT-4, to generate a sequence of instructions from the original
input, utilizing a purpose-designed 1-shot template. To further improve the
precision of each editing step, we conduct fine-tuning on the editing models
using our self-constructed instruction-guided face editing dataset,
Instruct-CelebA. And additionally, we incorporate a super-resolution module to
mitigate the adverse effects of editability and quality degradation.
Experimental results across various challenging cases confirm the significant
boost in multi-attribute facial image manipulation using chain-of-instruct
editing. This is evident in enhanced editing success rates, measured by CLIPSim
and Coverage metrics, improved by 17.86% and 85.45% respectively, and
heightened controllability indicated by Preserve L1 and Quality metrics,
improved by 11.58% and 4.93% respectively.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: BCN: Batch Channel Normalization for Image Classification
Abstract: Normalization techniques have been widely used in the field of deep learning
due to their capability of enabling higher learning rates and are less careful
in initialization. However, the effectiveness of popular normalization
technologies is typically limited to specific areas. Unlike the standard Batch
Normalization (BN) and Layer Normalization (LN), where BN computes the mean and
variance along the (N,H,W) dimensions and LN computes the mean and variance
along the (C,H,W) dimensions (N, C, H and W are the batch, channel, spatial
height and width dimension, respectively), this paper presents a novel
normalization technique called Batch Channel Normalization (BCN). To exploit
both the channel and batch dependence and adaptively and combine the advantages
of BN and LN based on specific datasets or tasks, BCN separately normalizes
inputs along the (N, H, W) and (C, H, W) axes, then combines the normalized
outputs based on adaptive parameters. As a basic block, BCN can be easily
integrated into existing models for various applications in the field of
computer vision. Empirical results show that the proposed technique can be
seamlessly applied to various versions of CNN or Vision Transformer
architecture. The code is publicly available at
https://github.com/AfifaKhaled/BatchChannel-Normalization

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Woodpecker: Hallucination Correction for Multimodal Large Language Models
Abstract: Hallucination is a big shadow hanging over the rapidly evolving Multimodal
Large Language Models (MLLMs), referring to the phenomenon that the generated
text is inconsistent with the image content. In order to mitigate
hallucinations, existing studies mainly resort to an instruction-tuning manner
that requires retraining the models with specific data. In this paper, we pave
a different way, introducing a training-free method named Woodpecker. Like a
woodpecker heals trees, it picks out and corrects hallucinations from the
generated text. Concretely, Woodpecker consists of five stages: key concept
extraction, question formulation, visual knowledge validation, visual claim
generation, and hallucination correction. Implemented in a post-remedy manner,
Woodpecker can easily serve different MLLMs, while being interpretable by
accessing intermediate outputs of the five stages. We evaluate Woodpecker both
quantitatively and qualitatively and show the huge potential of this new
paradigm. On the POPE benchmark, our method obtains a 30.66%/24.33% improvement
in accuracy over the baseline MiniGPT-4/mPLUG-Owl. The source code is released
at https://github.com/BradyFU/Woodpecker.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Decoupled DETR For Few-shot Object Detection
Abstract: Few-shot object detection (FSOD), an efficient method for addressing the
severe data-hungry problem, has been extensively discussed. Current works have
significantly advanced the problem in terms of model and data. However, the
overall performance of most FSOD methods still does not fulfill the desired
accuracy. In this paper we improve the FSOD model to address the severe issue
of sample imbalance and weak feature propagation. To alleviate modeling bias
from data-sufficient base classes, we examine the effect of decoupling the
parameters for classes with sufficient data and classes with few samples in
various ways. We design a base-novel categories decoupled DETR (DeDETR) for
FSOD. We also explore various types of skip connection between the encoder and
decoder for DETR. Besides, we notice that the best outputs could come from the
intermediate layer of the decoder instead of the last layer; therefore, we
build a unified decoder module that could dynamically fuse the decoder layers
as the output feature. We evaluate our model on commonly used datasets such as
PASCAL VOC and MSCOCO. Our results indicate that our proposed module could
achieve stable improvements of 5% to 10% in both fine-tuning and meta-learning
paradigms and has outperformed the highest score in recent works.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation
Abstract: Understanding how humans leverage semantic knowledge to navigate unfamiliar
environments and decide where to explore next is pivotal for developing robots
capable of human-like search behaviors. We introduce a zero-shot navigation
approach, Vision-Language Frontier Maps (VLFM), which is inspired by human
reasoning and designed to navigate towards unseen semantic objects in novel
environments. VLFM builds occupancy maps from depth observations to identify
frontiers, and leverages RGB observations and a pre-trained vision-language
model to generate a language-grounded value map. VLFM then uses this map to
identify the most promising frontier to explore for finding an instance of a
given target object category. We evaluate VLFM in photo-realistic environments
from the Gibson, Habitat-Matterport 3D (HM3D), and Matterport 3D (MP3D)
datasets within the Habitat simulator. Remarkably, VLFM achieves
state-of-the-art results on all three datasets as measured by success weighted
by path length (SPL) for the Object Goal Navigation task. Furthermore, we show
that VLFM's zero-shot nature enables it to be readily deployed on real-world
robots such as the Boston Dynamics Spot mobile manipulation platform. We deploy
VLFM on Spot and demonstrate its capability to efficiently navigate to target
objects within an office building in the real world, without any prior
knowledge of the environment. The accomplishments of VLFM underscore the
promising potential of vision-language models in advancing the field of
semantic navigation. Videos of real-world deployment can be viewed at
naoki.io/vlfm.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Simplifying Neural Network Training Under Class Imbalance
Abstract: Real-world datasets are often highly class-imbalanced, which can adversely
impact the performance of deep learning models. The majority of research on
training neural networks under class imbalance has focused on specialized loss
functions, sampling techniques, or two-stage training procedures. Notably, we
demonstrate that simply tuning existing components of standard deep learning
pipelines, such as the batch size, data augmentation, optimizer, and label
smoothing, can achieve state-of-the-art performance without any such
specialized class imbalance methods. We also provide key prescriptions and
considerations for training under class imbalance, and an understanding of why
imbalance methods succeed or fail.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
Abstract: Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to
extend the in-domain model to the distinctive target domains where the data
distributions differ. Most prior works focus on capturing the inter-domain
transferability but largely overlook rich intra-domain structures, which
empirically results in even worse discriminability. In this work, we introduce
a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The
core of our method is briefly condensed as follows: (i)-by casting the DA
problem to graph primitives, SPA composes a coarse graph alignment mechanism
with a novel spectral regularizer towards aligning the domain graphs in
eigenspaces; (ii)-we further develop a fine-grained message propagation module
-- upon a novel neighbor-aware self-training mechanism -- in order for enhanced
discriminability in the target domain. On standardized benchmarks, the
extensive experiments of SPA demonstrate that its performance has surpassed the
existing cutting-edge DA methods. Coupled with dense model analysis, we
conclude that our approach indeed possesses superior efficacy, robustness,
discriminability, and transferability. Code and data are available at:
https://github.com/CrownX/SPA.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Post-Training Quantization with Low-precision Minifloats and Integers on FPGAs
Abstract: Post-Training Quantization (PTQ) is a powerful technique for model
compression, reducing the precision of neural networks without additional
training overhead. Recent works have investigated adopting 8-bit floating-point
quantization (FP8) in the context of PTQ for model inference. However, the
exploration of floating-point formats smaller than 8 bits and their comparison
with integer quantization remains relatively limited. In this work, we present
minifloats, which are reduced-precision floating-point formats capable of
further reducing the memory footprint, latency, and energy cost of a model
while approaching full-precision model accuracy. Our work presents a novel PTQ
design-space exploration, comparing minifloat and integer quantization schemes
across a range of 3 to 8 bits for both weights and activations. We examine the
applicability of various PTQ techniques to minifloats, including weight
equalization, bias correction, SmoothQuant, gradient-based learned rounding,
and the GPTQ method. Our experiments validate the effectiveness of
low-precision minifloats when compared to their integer counterparts across a
spectrum of accuracy-precision trade-offs on a set of reference deep learning
vision workloads. Finally, we evaluate our results against an FPGA-based
hardware cost model, showing that integer quantization often remains the
Pareto-optimal option, given its relatively smaller hardware resource
footprint.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Incorporating Worker Perspectives into MTurk Annotation Practices for NLP
Abstract: Current practices regarding data collection for natural language processing
on Amazon Mechanical Turk (MTurk) often rely on a combination of studies on
data quality and heuristics shared among NLP researchers. However, without
considering the perspectives of MTurk workers, these approaches are susceptible
to issues regarding workers' rights and poor response quality. We conducted a
critical literature review and a survey of MTurk workers aimed at addressing
open questions regarding best practices for fair payment, worker privacy, data
quality, and considering worker incentives. We found that worker preferences
are often at odds with received wisdom among NLP researchers. Surveyed workers
preferred reliable, reasonable payments over uncertain, very high payments;
reported frequently lying on demographic questions; and expressed frustration
at having work rejected with no explanation. We also found that workers view
some quality control methods, such as requiring minimum response times or
Master's qualifications, as biased and largely ineffective. Based on the survey
results, we provide recommendations on how future NLP studies may better
account for MTurk workers' experiences in order to respect workers' rights and
improve data quality.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Data-Free Distillation of Language Model by Text-to-Text Transfer
Abstract: Data-Free Knowledge Distillation (DFKD) plays a vital role in compressing the
model when original training data is unavailable. Previous works for DFKD in
NLP mainly focus on distilling encoder-only structures like BERT on
classification tasks, which overlook the notable progress of generative
language modeling. In this work, we propose a novel DFKD framework, namely
DFKD-T$^{3}$, where the pretrained generative language model can also serve as
a controllable data generator for model compression. This novel framework
DFKD-T$^{3}$ leads to an end-to-end learnable text-to-text framework to
transform the general domain corpus to compression-friendly task data,
targeting to improve both the \textit{specificity} and \textit{diversity}.
Extensive experiments show that our method can boost the distillation
performance in various downstream tasks such as sentiment analysis, linguistic
acceptability, and information extraction. Furthermore, we show that the
generated texts can be directly used for distilling other language models and
outperform the SOTA methods, making our method more appealing in a general DFKD
setting. Our code is available at
https://gitee.com/mindspore/models/tree/master/research/nlp/DFKD\_T3.

Output: Computational Linguistics