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Instruction-Following Evaluation for Large Language Models
arXiv.org, 2023One core capability of Large Language Models (LLMs) is to follow natural language instructions. However, the evaluation of such abilities is not standardized: Human evaluations are expensive, slow, and not objectively reproducible, while LLM-based auto ...
Jeffrey Zhou+7 more
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On Instruction Sets and Their Fornmts
IEEE Transactions on Computers, 1985Central to instruction set design is the issue of the instruction format. We discuss some common format encoding techniques, and introduce a method of representing high-level language parse trees by means of formats that cover successively larger portions of a tree.
Flynn, Wakefield, Johnson
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Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning
arXiv.org, 2023Instruction tuning of Large Vision-language Models (LVLMs) has revolutionized the development of versatile models with zero-shot generalization across a wide range of downstream vision-language tasks. However, the diversity of training tasks of different
Yunhao Gou+8 more
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Security Analysis of Processor Instruction Set Architecture for Enforcing Control-Flow Integrity
HASP@ISCA, 2019Intel has developed Control-flow Enforcement Technology (CET) [27] that provides CPU instruction set architecture (ISA) capabilities to defend against Return-oriented Programming (ROP) and call/jmp-oriented programming (COP/JOP) style control-flow ...
Vedvyas Shanbhogue, D. Gupta, R. Sahita
semanticscholar +1 more source
Verifying Instruction Set Simulators using Coverage-guided Fuzzing*
Design, Automation and Test in Europe, 2019Verification of Instruction Set Simulators (ISSs) is crucial. Predominantly simulation-based approaches are used. They require a comprehensive testset to ensure a thorough verification.We propose a novel coverage-guided fuzzing (CGF) approach to improve ...
V. Herdt+3 more
semanticscholar +1 more source
Instructional Sets and MMPI Items
Journal of Personality Assessment, 1972Summary The MMPI was administered to three groups of nurses (total N = 33) under three different instructional sets: fake good, ideal self, and one year from now. The data were analyzed for the influence of set and the influence of order on Zero items and X items separately.
Beth Wales, William Seeman
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