Large Visual Language Models (LVLMs) struggle with hallucinations in visual
instruction following task(s), limiting their trustworthiness and real-world
applicability. We propose Pelican -- a novel framework designed to detect and
mitigate hallucinations through claim verification. Pelican first decomposes
the visual claim into a chain of sub-claims based on first-order predicates.
These sub-claims consist of (predicate, question) pairs and can be
conceptualized as nodes of a computational graph. We then use
Program-of-Thought prompting to generate Python code for answering these
questions through flexible composition of external tools. Pelican improves over
prior work by introducing (1) intermediate variables for precise grounding of
object instances, and (2) shared computation for answering the sub-question to
enable adaptive corrections and inconsistency identification. We finally use
reasoning abilities of LLM to verify the correctness of the the claim by
considering the consistency and confidence of the (question, answer) pairs from
each sub-claim. Our experiments reveal a drop in hallucination rate by
$\sim$8%-32% across various baseline LVLMs and a 27% drop compared to
approaches proposed for hallucination mitigation on MMHal-Bench. Results on two
other benchmarks further corroborate our results.

通过索引验证，Pelican 可以检测和消除 LVLMs 在图像指令任务中的幻觉，并依靠中间变量、共享计算和推理能力更精确定位对象实例，从而提高其准确性。

Pelican：通过声明分解和思维验证程序修正视觉 LLM 中的幻觉

Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition  and Program of Thought Verification

As particle accelerators increase their collision rates, and deep learning
solutions prove their viability, there is a growing need for lightweight and
fast neural network architectures for low-latency tasks such as triggering. We
examine the potential of one recent Lorentz- and permutation-symmetric
architecture, PELICAN, and present its instances with as few as 19 trainable
parameters that outperform generic architectures with tens of thousands of
parameters when compared on the binary classification task of top quark jet
tagging.

粒子加速器和深度学习的低延迟任务中，轻量级快速的神经网络架构的需求增加。我们研究了一种最近的洛伦兹 - 排列对称架构 PELICAN，并提出了它的实例，只需 19 个可训练参数，当在顶夸克喷注的二分类任务中与成千上万个参数的通用架构进行对比时，表现更好。

只需 19 个参数：粒子物理学的微型神经网络

19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics

We present a comprehensive study of the PELICAN machine learning algorithm
architecture in the context of both tagging (classification) and reconstructing
(regression) Lorentz-boosted top quarks, including the difficult task of
specifically identifying and measuring the $W$-boson inside the dense
environment of the boosted hadronic final state. PELICAN is a novel permutation
equivariant and Lorentz invariant or covariant aggregator network designed to
overcome common limitations found in architectures applied to particle physics
problems. Compared to many approaches that use non-specialized architectures
that neglect underlying physics principles and require very large numbers of
parameters, PELICAN employs a fundamentally symmetry group-based architecture
that demonstrates benefits in terms of reduced complexity, increased
interpretability, and raw performance. When tested on the standard task of
Lorentz-boosted top quark tagging, PELICAN outperforms existing competitors
with much lower model complexity and high sample efficiency. On the less common
and more complex task of four-momentum regression, PELICAN also outperforms
hand-crafted algorithms. We discuss the implications of symmetry-restricted
architectures for the wider field of machine learning for physics.

PELICAN 是一种新颖的置换等变和洛伦兹不变或协变的拼接网络，它在分类和回归洛伦兹增强顶夸克的上下文中，特别是在识别和测量密集环境中 $W$ 玻色子的困难任务中，相比其他方法表现出更少的复杂性、更高的可解释性和原始性能。

可解释的等变神经网络用于粒子物理学：PELICAN

Explainable Equivariant Neural Networks for Particle Physics: PELICAN

We developed a deeP architecturE for the LIght Curve ANalysis (PELICAN) for
the characterization and the classification of light curves. It takes light
curves as input, without any additional features. PELICAN can deal with the
sparsity and the irregular sampling of light curves. It is designed to remove
the problem of non-representativeness between the training and test databases
coming from the limitations of the spectroscopic follow-up. We applied our
methodology on different supernovae light curve databases. First, we evaluated
PELICAN on the Supernova Photometric Classification Challenge for which we
obtained the best performance ever achieved with a non-representative training
database, by reaching an accuracy of 0.811. Then we tested PELICAN on simulated
light curves of the LSST Deep Fields for which PELICAN is able to detect 87.4%
of supernovae Ia with a precision higher than 98%, by considering a
non-representative training database of 2k light curves. PELICAN can be trained
on light curves of LSST Deep Fields to classify light curves of LSST main
survey, that have a lower sampling rate and are more noisy. In this scenario,
it reaches an accuracy of 96.5% with a training database of 2k light curves of
the Deep Fields. It constitutes a pivotal result as type Ia supernovae
candidates from the main survey might then be used to increase the statistics
without additional spectroscopic follow-up. Finally we evaluated PELICAN on
real data from the Sloan Digital Sky Survey. PELICAN reaches an accuracy of
86.8% with a training database composed of simulated data and a fraction of 10%
of real data. The ability of PELICAN to deal with the different causes of
non-representativeness between the training and test databases, and its
robustness against survey properties and observational conditions, put it on
the forefront of the light curves classification tools for the LSST era.

本文介绍了 PELICAN 基于深度神经网络的光变曲线分类器， 并应用于超新星光变曲线数据，其中在不同的数据集上轻松完成了分类任务。