201 research outputs found
Shaping Social Activity by Incentivizing Users
Events in an online social network can be categorized roughly into endogenous
events, where users just respond to the actions of their neighbors within the
network, or exogenous events, where users take actions due to drives external
to the network. How much external drive should be provided to each user, such
that the network activity can be steered towards a target state? In this paper,
we model social events using multivariate Hawkes processes, which can capture
both endogenous and exogenous event intensities, and derive a time dependent
linear relation between the intensity of exogenous events and the overall
network activity. Exploiting this connection, we develop a convex optimization
framework for determining the required level of external drive in order for the
network to reach a desired activity level. We experimented with event data
gathered from Twitter, and show that our method can steer the activity of the
network more accurately than alternatives
CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models
We are currently in an era of fierce competition among various large language
models (LLMs) continuously pushing the boundaries of benchmark performance.
However, genuinely assessing the capabilities of these LLMs has become a
challenging and critical issue due to potential data contamination, and it
wastes dozens of time and effort for researchers and engineers to download and
try those contaminated models. To save our precious time, we propose a novel
and useful method, Clean-Eval, which mitigates the issue of data contamination
and evaluates the LLMs in a cleaner manner. Clean-Eval employs an LLM to
paraphrase and back-translate the contaminated data into a candidate set,
generating expressions with the same meaning but in different surface forms. A
semantic detector is then used to filter the generated low-quality samples to
narrow down this candidate set. The best candidate is finally selected from
this set based on the BLEURT score. According to human assessment, this best
candidate is semantically similar to the original contamination data but
expressed differently. All candidates can form a new benchmark to evaluate the
model. Our experiments illustrate that Clean-Eval substantially restores the
actual evaluation results on contaminated LLMs under both few-shot learning and
fine-tuning scenarios
A Novel Method for Acquiring Engineering-Oriented Operational Empirical Knowledge
The operational knowledge of skilled technicians gained from years of experience is invaluable for an enterprise. Possession of such knowledge will facilitate an enterprise sharing technician’s know-how and training of new employees effectively. However, until now there is rare efficient quantitative method to obtain this kind of tacit knowledge. In this paper we propose a concept of engineering-oriented operational empirical knowledge (OEK) to describe this kind of knowledge and design a framework to acquire OEK from skilled technician’s operations. The framework integrates motion analysis, motion elicitation, and intent analysis. The modular arrangement of predetermined time standards (MODAPTS) is used to divide the technician’s operational process into basic motion elements; and the variable precision rough set (VPRS) algorithm is used to extract the technician’s OEK content, which combined with the technician’s intent elicited via interview; the completed OEK is obtained. At the end of our study, an engineering case is used to validate the feasibility of the proposed method, which shows that satisfactory results have been reached for the study
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