302 research outputs found
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT
Recently, ChatGPT has gained significant attention in research due to its
ability to interact with humans effectively. The core idea behind this model is
reinforcement learning (RL) fine-tuning, a new paradigm that allows language
models to align with human preferences, i.e., InstructGPT. In this study, we
propose BadGPT, the first backdoor attack against RL fine-tuning in language
models. By injecting a backdoor into the reward model, the language model can
be compromised during the fine-tuning stage. Our initial experiments on movie
reviews, i.e., IMDB, demonstrate that an attacker can manipulate the generated
text through BadGPT.Comment: This paper is accepted as a poster in NDSS202
Conformal Sensitivity Analysis for Individual Treatment Effects
Estimating an individual treatment effect (ITE) is essential to personalized
decision making. However, existing methods for estimating the ITE often rely on
unconfoundedness, an assumption that is fundamentally untestable with observed
data. To assess the robustness of individual-level causal conclusion with
unconfoundedness, this paper proposes a method for sensitivity analysis of the
ITE, a way to estimate a range of the ITE under unobserved confounding. The
method we develop quantifies unmeasured confounding through a marginal
sensitivity model [Ros2002, Tan2006], and adapts the framework of conformal
inference to estimate an ITE interval at a given confounding strength. In
particular, we formulate this sensitivity analysis problem as a conformal
inference problem under distribution shift, and we extend existing methods of
covariate-shifted conformal inference to this more general setting. The result
is a predictive interval that has guaranteed nominal coverage of the ITE, a
method that provides coverage with distribution-free and nonasymptotic
guarantees. We evaluate the method on synthetic data and illustrate its
application in an observational study.Comment: Journal of the American Statistical Associatio
Weyl type gravity, and its cosmological implications
We consider an type gravity model in which the scalar non-metricity
of the space-time is expressed in its standard Weyl form,
and it is fully determined by a vector field . The field equations of
the theory are obtained under the assumption of the vanishing of the total
scalar curvature, a condition which is added into the gravitational action via
a Lagrange multiplier. The gravitational field equations are obtained from a
variational principle, and they explicitly depend on the scalar nonmetricity
and on the Lagrange multiplier. The covariant divergence of the matter
energy-momentum tensor is also determined, and it follows that the
nonmetricity-matter coupling leads to the nonconservation of the energy and
momentum. The energy and momentum balance equations are explicitly calculated,
and the expressions of the energy source term and of the extra force are found.
We investigate the cosmological implications of the theory, and we obtain the
cosmological evolution equations for a flat, homogeneous and isotropic
geometry, which generalize the Friedmann equations of standard general
relativity. We consider several cosmological models by imposing some simple
functional forms of the function , and we compare the predictions of
the theory with the standard CDM model.Comment: 22 pages, 14 figures, accepted for publication in Eur. Phys. Journal
C. arXiv admin note: text overlap with arXiv:1908.0476
Mutual Authentication and Key Exchange Protocols for Roaming Services in Wireless Mobile Networks
ODSum: New Benchmarks for Open Domain Multi-Document Summarization
Open-domain Multi-Document Summarization (ODMDS) is a critical tool for
condensing vast arrays of documents into coherent, concise summaries. With a
more inter-related document set, there does not necessarily exist a correct
answer for the retrieval, making it hard to measure the retrieving performance.
We propose a rule-based method to process query-based document summarization
datasets into ODMDS datasets. Based on this method, we introduce a novel
dataset, ODSum, a sophisticated case with its document index interdependent and
often interrelated. We tackle ODMDS with the \textit{retrieve-then-summarize}
method, and the performance of a list of retrievers and summarizers is
investigated. Through extensive experiments, we identify variances in
evaluation metrics and provide insights into their reliability. We also found
that LLMs suffer great performance loss from retrieving errors. We further
experimented methods to improve the performance as well as investigate their
robustness against imperfect retrieval. We will release our data and code at
https://github.com/yale-nlp/ODSum
A Collaborative Jamming Algorithm Based on Multi-UAV Scheduling
In this paper, we consider the problem of multi-unmanned aerial vehicles'
scheduling for cooperative jamming, where UAVs equipped with directional
antennas perform collaborative jamming tasks against several targets of
interest. To ensure effective jamming towards the targets, we formulate it as
an non-convex optimization problem, aiming to minimize the communication
performance of the targets by jointly optimizing UAVs' deployment and
directional antenna orientations. Due to the unique structure of the problem,
we derive an equivalent transformation by introducing a set of auxiliary
matrices. Subsequently, we propose an efficient iterative algorithm based on
the alternating direction method of multipliers, which decomposes the problem
into multiple tractable subproblems solved in closed-form or by gradient
projection method. Extensive simulations validate the efficacy of the proposed
algorithm
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