מושגי ליבה
LLMs' performance on technical language tasks is evaluated for reliability and citability.
תקציר
The content discusses the evaluation study of Large Language Models (LLMs) on Technical Language Processing tasks, focusing on the ability to provide reliable and citable answers. The study used a range of LLMs with chat interfaces to answer questions based on Title 47 of the United States Code of Federal Regulations related to Wireless Spectrum Governance. The paper highlights the challenges in automating Knowledge Graph creation and emphasizes the importance of human augmentation in developing ML workflows. The evaluation involved novice to expert evaluators, revealing varying perceptions of response quality based on expertise levels. Results showed that while some models provided convincing answers, they lacked precision and accuracy, especially in technical information domains like wireless spectrum regulation.
Introduction:
- Study evaluates LLMs' performance on Technical Language Processing tasks.
- Focuses on providing reliable and citable answers.
- Uses Title 47 CFR as a body of text for evaluation.
Tools Evaluation:
- Various LLMs were evaluated for their ability to create Knowledge Graphs.
- Human evaluators rated responses for comprehensibility and correctness.
- Responses varied in quality based on model size and complexity of questions.
Questions Evaluation:
- Specific domain-specific questions were used for evaluation.
- Participants' expertise influenced their perception of answer quality.
- Models like ChatGPT performed better than others but still had limitations.
Discussion:
- LLMs' responses were often middling or inaccurate, posing challenges in technical information retrieval.
- Expertise level impacted participants' perception of answer quality.
- Caution is advised when relying on LLMs for technical information.
סטטיסטיקה
The release of ChatGPT was based on GPT 3.5 LLM in late 2022.
H20GPT model size: 65 Billion parameters.
ChatGPT estimated at 170 Billion+ parameters.
ציטוטים
"Large language models can be a valuable tool, but they should assist human expertise rather than replace it."
"Significant research effort should be devoted to making question-answering reliable with sub-document level citability."