• Journal of Korean Elementary Science Education
• /
• v.21 no.1
• /
• pp.13-24
• /
• 2002

This study examined the knowledge structure constructed by children before formal instruction, and successive changes in the structural complexity of knowledge during the learning of the 'Structure and Function of Plants' unit. The researchers let the 5th graders draw the first draft of their concept maps in order to examine the pre-existing knowledge structure concerned with the unit and also four concept maps after completing every fourth lesson. Each concept map drawn by children on the basis of the previous one showed the degree of their current understanding on the structure and function of plants. The results revealed that only two levels of hierarchy and five relationships among the components of the first concept map(relationship, hierarchy, cross link and example) were proven to be valid in terms of conceptual relevance. According to the standard map, there was no change in hierarchy from the 2nd to the 3rd map, and in example from the 2nd to the 4th map. However, the gradual and successive increases of the scores in all components appeared in the children's maps throughout the unit. Knowledge restructuring occurred strongly in the early periods from the 1st to the 6th lesson, and the significant stable changes in tuning and accretion appeared throughout the whole lessons. The results also showed that there were no significant gender differences on the 5th grader's knowledge structuring.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.5
• /
• pp.1829-1846
• /
• 2021

Transient faults occur in computation units of a processor, which can cause control flow errors (CFEs) and compromise system reliability. The software-based methods perform illegal control flow detection by inserting redundant instructions and monitoring signature. However, the existing methods not only have drawbacks in terms of performance overhead, but also lack of configurability. We propose a configurable approach CCFCA for detecting CFEs. The configurability of CCFCA is implemented by analyzing the criticality of each region and tuning the detecting granularity. For critical regions, program blocks are divided according to space-time overhead and reliability constraints, so that protection intensity can be configured flexibly. For other regions, signature detection algorithms are only used in the first basic block and last basic block. This helps to improve the fault-tolerant efficiency of the CCFCA. At the same time, CCFCA also has the function of solving confusion and instruction self-detection. Our experimental results show that CCFCA incurs only 10.61% performance overhead on average for several C benchmark program and the average undetected error rate is only 9.29%. CCFCA has high error coverage and low overhead compared with similar algorithms. This helps to meet different cost requirements and reliability requirements.

• Electronics and Telecommunications Trends
• /
• v.38 no.6
• /
• pp.1-11
• /
• 2023

Large language models seem promising for handling reasoning problems, but their underlying solving mechanisms remain unclear. Large language models will establish a new paradigm in artificial intelligence and the society as a whole. However, a major challenge of large language models is the massive resources required for training and operation. To address this issue, researchers are actively exploring compact large language models that retain the capabilities of large language models while notably reducing the model size. These research efforts are mainly focused on improving pretraining, instruction tuning, and alignment. On the other hand, chain-of-thought prompting is a technique aimed at enhancing the reasoning ability of large language models. It provides an answer through a series of intermediate reasoning steps when given a problem. By guiding the model through a multistep problem-solving process, chain-of-thought prompting may improve the model reasoning skills. Mathematical reasoning, which is a fundamental aspect of human intelligence, has played a crucial role in advancing large language models toward human-level performance. As a result, mathematical reasoning is being widely explored in the context of large language models. This type of research extends to various domains such as geometry problem solving, tabular mathematical reasoning, visual question answering, and other areas.

• Annual Conference on Human and Language Technology
• /
• 2023.10a
• /
• pp.448-453
• /
• 2023

대규모 언어 모델 (Large Language Model, LLM)을 인간의 선호도 관점에서 평가하는 것은 기존의 벤치마크 평가와는 다른 도전적인 과제이다. 이를 위해, 기존 연구들은 강력한 LLM을 평가자로 사용하여 접근하였지만, 높은 비용 문제가 부각되었다. 또한, 평가자로서 LLM이 사용하는 주관적인 점수 기준은 모호하여 평가 결과의 신뢰성을 저해하며, 단일 모델에 의한 평가 결과는 편향될 가능성이 있다. 본 논문에서는 엄격한 기준을 활용하여 편향되지 않은 평가를 수행할 수 있는 평가 프레임워크 및 평가자 모델 'FubaoLM'을 제안한다. 우리의 평가 프레임워크는 심층적인 평가 기준을 통해 다수의 강력한 한국어 LLM을 활용하여 연쇄적 사고(Chain-of-Thought) 기반 평가를 수행한다. 이러한 평가 결과를 다수결로 통합하여 편향되지 않은 평가 결과를 도출하며, 지시 조정 (instruction tuning)을 통해 FubaoLM은 다수의 LLM으로 부터 평가 지식을 증류받는다. 더 나아가 본 논문에서는 전문가 기반 평가 데이터셋을 구축하여 FubaoLM 효과성을 입증한다. 우리의 실험에서 앙상블된 FubaoLM은 GPT-3.5 대비 16% 에서 23% 향상된 절대 평가 성능을 가지며, 이항 평가에서 인간과 유사한 선호도 평가 결과를 도출한다. 이를 통해 FubaoLM은 비교적 적은 비용으로도 높은 신뢰성을 유지하며, 편향되지 않은 평가를 수행할 수 있음을 보인다.

• Journal of The Korean Association For Science Education
• /
• v.22 no.4
• /
• pp.796-805
• /
• 2002

This study examined the knowledge structure constructed by children before formal instruction, and successive changes in the structural complexity of knowledge during and after the learning of 'Structure and Function of Plant' unit. It also investigated how those changes were affected by children's learning modes. The researchers made the 5th graders draw the first draft of their concept map to see the pre-existing knowledge structure concerned with the unit and four more concept maps after completing every fourth lesson. And to see how long their knowledge structures were preserved, the researchers made children draw additional concept maps in 3 days, 3 months, and 7 months after completing the unit. Children drew their current concept maps on the basis of the previous one while learning the unit and without the previous one after completing the unit. Each concept map drawn by children showed the degree of their current understanding on the structures and functions of plants. The results revealed that only two levels of hierarchy and five relationships among the components of the first concept map(relationship, hierarchy, cross link and example) were proven to be valid in terms of conceptual relevance. Growth in the structural complexity of knowledge took place progressively throughout the unit and the effects of learning mode on the growth were favorably reflected in concept map scores of meaningful learners over time(relationship, cross link, example: p<.01, hierarchy: p<.05). Although there were some differences on the concept map scores between two types of learners, they commonly showed that knowledge restructuring had occurred apparently in the early periods from the 1st to the 6th lesson and had not occurred at all in the last period of the unit. The frequency of tuning was higher in rote learners than in meaningful learners throughout the unit, but the frequency of accretion was reverse. Concept map scores of rote learners constructed in the course of learning of the unit decreased little by little gradually in all the categories after completing the unit. However, the average total map score of meaningful learners increased a little more in 7 months than in 3 months after completing the unit. Therefore it can be inferred that meaningful learners construct more stable and well-differentiated knowledge structures than the rote learners.