2,104 research outputs found
AARCTIC: Autonomic Analytics Research for Corrections Technology, Institutional and in the Community
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language
Models (LLMs). As LLMs find applications across diverse domains, the
reliability and accuracy of their outputs become vital. We define the
Factuality Issue as the probability of LLMs to produce content inconsistent
with established facts. We first delve into the implications of these
inaccuracies, highlighting the potential consequences and challenges posed by
factual errors in LLM outputs. Subsequently, we analyze the mechanisms through
which LLMs store and process facts, seeking the primary causes of factual
errors. Our discussion then transitions to methodologies for evaluating LLM
factuality, emphasizing key metrics, benchmarks, and studies. We further
explore strategies for enhancing LLM factuality, including approaches tailored
for specific domains. We focus two primary LLM configurations standalone LLMs
and Retrieval-Augmented LLMs that utilizes external data, we detail their
unique challenges and potential enhancements. Our survey offers a structured
guide for researchers aiming to fortify the factual reliability of LLMs.Comment: 62 pages; 300+ reference
Belief functions contextual discounting and canonical decompositions
AbstractIn this article, the contextual discounting of a belief function, a classical discounting generalization, is extended and its particular link with the canonical disjunctive decomposition is highlighted. A general family of correction mechanisms allowing one to weaken the information provided by a source is then introduced, as well as the dual of this family allowing one to strengthen a belief function
Tweet, but Verify: Epistemic Study of Information Verification on Twitter
While Twitter provides an unprecedented opportunity to learn about breaking
news and current events as they happen, it often produces skepticism among
users as not all the information is accurate but also hoaxes are sometimes
spread. While avoiding the diffusion of hoaxes is a major concern during
fast-paced events such as natural disasters, the study of how users trust and
verify information from tweets in these contexts has received little attention
so far. We survey users on credibility perceptions regarding witness pictures
posted on Twitter related to Hurricane Sandy. By examining credibility
perceptions on features suggested for information verification in the field of
Epistemology, we evaluate their accuracy in determining whether pictures were
real or fake compared to professional evaluations performed by experts. Our
study unveils insight about tweet presentation, as well as features that users
should look at when assessing the veracity of tweets in the context of
fast-paced events. Some of our main findings include that while author details
not readily available on Twitter feeds should be emphasized in order to
facilitate verification of tweets, showing multiple tweets corroborating a fact
misleads users to trusting what actually is a hoax. We contrast some of the
behavioral patterns found on tweets with literature in Psychology research.Comment: Pre-print of paper accepted to Social Network Analysis and Mining
(Springer
Automated image tagging through tag propagation
Trabalho apresentado no âmbito do Mestrado em
Engenharia Informática, como requisito parcial
Para obtenção do grau de Mestre em Engenharia
InformáticaToday, more and more data is becoming available on the Web. In particular, we have recently witnessed an exponential increase of multimedia content within various content sharing websites. While this content is widely available, great challenges have arisen to effectively search and browse such vast amount of content. A solution to this problem is to annotate information, a task that without computer aid requires a large-scale human effort. The goal of this thesis is to automate the task of annotating multimedia information with machine learning algorithms.
We propose the development of a machine learning framework capable of doing automated image annotation in large-scale consumer photos. To this extent a study on state of art algorithms was conducted, which concluded with a baseline implementation of a k-nearest neighbor algorithm. This baseline was used to implement a more advanced algorithm capable of annotating images in the situations with limited training images and a large set of test images – thus, a semi-supervised approach.
Further studies were conducted on the feature spaces used to describe images towards a successful integration in the developed framework. We first analyzed the semantic gap between the visual feature spaces and concepts present in an image, and how to avoid or mitigate this gap. Moreover, we examined how users perceive images by performing a statistical analysis of the image tags inserted by users. A linguistic and statistical expansion of image tags was also implemented.
The developed framework withstands uneven data distributions that occur in consumer datasets, and scales accordingly, requiring few previously annotated data. The principal mechanism that allows easier scaling is the propagation of information between the annotated data and un-annotated data
Trust Dynamics: A Case-study on Railway Sensors
International audienceSensors constitute information providers which are subject to imperfections and assessing the quality of their outputs, in particular the trust that can be put in them, is a crucial task. Indeed, timely recognising a low-trust sensor output can greatly improve the decision making process at the fusion level, help solving safety issues and avoiding expensive operations such as either unnecessary or delayed maintenance. In this framework, this paper considers the question of trust dynamics, i.e. its temporal evolution with respect to the information flow. The goal is to increase the user understanding of the trust computation model, as well as to give hints about how to refine the model and set its parameters according to specific needs. Considering a trust computation model based on three dimensions, namely reliability, likelihood and credibility, the paper proposes a protocol for the evaluation of the scoring method, in the case when no ground truth is available, using realistic simulated data to analyse the trust evolution at the local level of a single sensor. After a visual and formal analysis, the scoring method is applied to real data at a global level to observe interactions and dependencies among multiple sensors
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