I evaluated my self-trained LLM what 31% accuracy actually means
The author evaluated their self-trained language model, achieving a 31% accuracy on a test set of 200 questions. This performance is better than random guessing but significantly lower than advanced models like GPT-4. The author emphasizes the importance of sharing honest evaluation results to provide transparency in AI projects.
- ▪The model achieved 31% accuracy, outperforming random guessing by 6 percentage points.
- ▪Compared to GPT-4, which scores around 90%, the author's model shows room for improvement.
- ▪The author suggests that using a larger base model and better knowledge sources could enhance performance.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1358056) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Akhilesh Posted on May 16 I evaluated my self-trained LLM what 31% accuracy actually means #ai #sideprojects #productivity #beginners Most AI projects don't include evaluation. They show a nice demo, pick cherry-picked examples, and call it done. I wanted to be honest, so I tested my model on 200 questions it had never seen. How I evaluated The test set has 1,273 questions that were never used in training.
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