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Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:55

Self-Assessment of Technical Skills with ChatGPT

Published:Jan 3, 2026 06:20
1 min read
Qiita ChatGPT

Analysis

The article describes an experiment using ChatGPT's 'learning mode' to assess the author's IT engineering skills. It provides context by explaining the motivation behind the self-assessment, likely related to career development or self-improvement. The focus is on practical application of an LLM for personal evaluation.
Reference

“The article mentions using ChatGPT's 'learning mode' and the motivation behind the assessment, which is related to the author's experience.”

PermalinkQiita ChatGPT
Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 12:35

LLMs for Vulnerable Code: Generation vs. Refactoring

Published:Dec 9, 2025 11:15
1 min read
ArXiv

Analysis

This ArXiv article explores the application of Large Language Models (LLMs) to the detection and mitigation of vulnerabilities in code, specifically comparing code generation and refactoring approaches. The research offers insights into the strengths and weaknesses of different LLM-based techniques in addressing software security flaws.
Reference

“The article likely discusses the use of LLMs for code vulnerability analysis.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:37

Knots: A Large-Scale Multi-Agent Enhanced Expert-Annotated Dataset and LLM Prompt Optimization for NOTAM Semantic Parsing

Published:Nov 16, 2025 14:52
1 min read
ArXiv

Analysis

This article presents a research paper focused on improving the performance of Large Language Models (LLMs) in understanding and processing NOTAMs (Notices to Airmen). The core contribution is a new dataset, 'Knots,' which is large-scale, expert-annotated, and enhanced with a multi-agent approach. The research also explores prompt optimization techniques for LLMs to improve their semantic parsing capabilities specifically for NOTAMs. The focus is on a specialized domain (aviation) and the application of LLMs to a practical task.
Reference

“The article's focus on NOTAM semantic parsing suggests a practical application of LLMs in a safety-critical domain. The use of a multi-agent approach and prompt optimization indicates a sophisticated approach to improving LLM performance.”

PermalinkArXiv