250 research outputs found
DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions
General large language models (LLMs) such as ChatGPT have shown remarkable
success, but it has also raised concerns among people about the misuse of
AI-generated texts. Therefore, an important question is how to detect whether
the texts are generated by ChatGPT or by humans. Existing detectors are built
on the assumption that there is a distribution gap between human-generated and
AI-generated texts. These gaps are typically identified using statistical
information or classifiers. In contrast to prior research methods, we find that
large language models such as ChatGPT exhibit strong self-consistency in text
generation and continuation. Self-consistency capitalizes on the intuition that
AI-generated texts can still be reasoned with by large language models using
the same logical reasoning when portions of the texts are masked, which differs
from human-generated texts. Using this observation, we subsequently proposed a
new method for AI-generated texts detection based on self-consistency with
masked predictions to determine whether a text is generated by LLMs. This
method, which we call DetectGPT-SC. We conducted a series of experiments to
evaluate the performance of DetectGPT-SC. In these experiments, we employed
various mask scheme, zero-shot, and simple prompt for completing masked texts
and self-consistency predictions. The results indicate that DetectGPT-SC
outperforms the current state-of-the-art across different tasks.Comment: 7 pages, 3 figure
Stable and verifiable state estimation methods and systems with spacecraft applications
The stability of a recursive estimator process (e.g., a Kalman filter is assured for long time periods by periodically resetting an error covariance P(t.sub.n) of the system to a predetermined reset value P.sub.r. The recursive process is thus repetitively forced to start from a selected covariance and continue for a time period that is short compared to the system's total operational time period. The time period in which the process must maintain its numerical stability is significantly reduced as is the demand on the system's numerical stability. The process stability for an extended operational time period T.sub.o is verified by performing the resetting step at the end of at least one reset time period T.sub.r whose duration is less than the operational time period T.sub.o and then confirming stability of the process over the reset time period T.sub.r. Because the recursive process starts from a selected covariance at the beginning of each reset time period T.sub.r, confirming stability of the process over at least one reset time period substantially confirms stability over the longer operational time period T.sub.o
System for star catalog equalization to enhance attitude determination
An apparatus for star catalog equalization to enhance attitude determination includes a star tracker, a star catalog and a controller. The star tracker is used to sense the positions of stars and generate signals corresponding to the positions of the stars as seen in its field of view. The star catalog contains star location data that is stored using a primary and multiple secondary arrays sorted by both declination (DEC) and right ascension (RA), respectively. The star location data stored in the star catalog is predetermined by calculating a plurality of desired star locations, associating one of a plurality of stars with each of the plurality of desired star locations based upon a neighborhood association angle to generate an associated plurality of star locations: If an artificial star gap occurs during association, then the neighborhood association angle for reassociation is increased. The controller uses the star catalog to determine which stars to select to provide star measurement residuals for correcting gyroscope bias and spacecraft attitude
Spacecraft attitude control systems with dynamic methods and structures for processing star tracker signals
Methods are provided for dynamically processing successively-generated star tracker data frames and associated valid flags to generate processed star tracker signals that have reduced noise and a probability greater than a selected probability P.sub.slctd of being valid. These methods maintain accurate spacecraft attitude control in the presence of spurious inputs (e.g., impinging protons) that corrupt collected charges in spacecraft star trackers. The methods of the invention enhance the probability of generating valid star tracker signals because they respond to a current frame probability P.sub.frm by dynamically selecting the largest valid frame combination whose combination probability P.sub.cmb satisfies a selected probability P.sub.slctd. Noise is thus reduced while the probability of finding a valid frame combination is enhanced. Spacecraft structures are also provided for practicing the methods of the invention
System and method for calibrating inter-star-tracker misalignments in a stellar inertial attitude determination system
A method and apparatus for determining star tracker misalignments is disclosed. The method comprises the steps of defining a defining a reference frame for the star tracker assembly according to a boresight of the primary star tracker and a boresight of a second star tracker wherein the boresight of the primary star tracker and a plane spanned by the boresight of the primary star tracker and the boresight of the second star tracker at least partially define a datum for the reference frame for the star tracker assembly; and determining the misalignment of the at least one star tracker as a rotation of the defined reference frame
Retrieval-Augmented Meta Learning for Low-Resource Text Classification
Meta learning have achieved promising performance in low-resource text
classification which aims to identify target classes with knowledge transferred
from source classes with sets of small tasks named episodes. However, due to
the limited training data in the meta-learning scenario and the inherent
properties of parameterized neural networks, poor generalization performance
has become a pressing problem that needs to be addressed. To deal with this
issue, we propose a meta-learning based method called Retrieval-Augmented Meta
Learning(RAML). It not only uses parameterization for inference but also
retrieves non-parametric knowledge from an external corpus to make inferences,
which greatly alleviates the problem of poor generalization performance caused
by the lack of diverse training data in meta-learning. This method differs from
previous models that solely rely on parameters, as it explicitly emphasizes the
importance of non-parametric knowledge, aiming to strike a balance between
parameterized neural networks and non-parametric knowledge. The model is
required to determine which knowledge to access and utilize during inference.
Additionally, our multi-view passages fusion network module can effectively and
efficiently integrate the retrieved information into low-resource
classification task. The extensive experiments demonstrate that RAML
significantly outperforms current SOTA low-resource text classification models.Comment: Under Revie
Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection
Generalized Few-Shot Intent Detection (GFSID) is challenging and realistic
because it needs to categorize both seen and novel intents simultaneously.
Previous GFSID methods rely on the episodic learning paradigm, which makes it
hard to extend to a generalized setup as they do not explicitly learn the
classification of seen categories and the knowledge of seen intents. To address
the dilemma, we propose to convert the GFSID task into the class incremental
learning paradigm. Specifically, we propose a two-stage learning framework,
which sequentially learns the knowledge of different intents in various periods
via prompt learning. And then we exploit prototypes for categorizing both seen
and novel intents. Furthermore, to achieve the transfer knowledge of intents in
different stages, for different scenarios we design two knowledge preservation
methods which close to realistic applications. Extensive experiments and
detailed analyses on two widely used datasets show that our framework based on
the class incremental learning paradigm achieves promising performance.Comment: Under Revie
Decision Diagram Based Symbolic Algorithm for Evaluating the Reliability of a Multistate Flow Network
Evaluating the reliability of Multistate Flow Network (MFN) is an NP-hard problem. Ordered binary decision diagram (OBDD) or variants thereof, such as multivalued decision diagram (MDD), are compact and efficient data structures suitable for dealing with large-scale problems. Two symbolic algorithms for evaluating the reliability of MFN, MFN_OBDD and MFN_MDD, are proposed in this paper. In the algorithms, several operating functions are defined to prune the generated decision diagrams. Thereby the state space of capacity combinations is further compressed and the operational complexity of the decision diagrams is further reduced. Meanwhile, the related theoretical proofs and complexity analysis are carried out. Experimental results show the following: (1) compared to the existing decomposition algorithm, the proposed algorithms take less memory space and fewer loops. (2) The number of nodes and the number of variables of MDD generated in MFN_MDD algorithm are much smaller than those of OBDD built in the MFN_OBDD algorithm. (3) In two cases with the same number of arcs, the proposed algorithms are more suitable for calculating the reliability of sparse networks
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