Content Deep Dive
Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment
Blog post from Arize
Post Details
Company
Date Published
Author
Sarah Welsh
Word Count
8,093
Company Posts That Month
Language
English
Hacker News Points
-
Post removed?
No
Summary
In this paper review, we discussed how to create a golden dataset for evaluating LLMs using evals from alignment tasks. The process involves running eval tasks, gathering examples, and fine-tuning or prompt engineering based on the results. We also touched upon the use of RAG systems in AI observability and the importance of evals in improving model performance.
Trends Found in this Post
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 72 | 3,051 | 363 | 148 | -19% |
| RAG | 18 | 893 | 155 | 66 | -52% |
| AI Model Fine-tuning | 8 | 505 | 104 | 68 | -36% |
| Observability | 5 | 1,068 | 237 | 96 | -24% |
| AI Guardrails | 1 | 123 | 51 | 25 | -28% |
| Real-time | 1 | 2,475 | 678 | 222 | -5% |
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