LLMs still struggle at niche-domain labeling: a cluster-then-LLM pipeline with brittle second-pass labels
FanaHOVA · x · 2026-09-21
- A developer testing Magic deck classification found big models still bad at label creation in niche domains, missing small nuances.
- His ideal loop: cluster + LLM creates the first wave of labels, a classifier with an "Other" option filters, then a second LLM pass mines nuances from "Other".
- In practice the second-pass LLM produces very brittle labels — no clean solution yet.
More from coding & agent
- Agent-generated code is a Jenga tower: you can ship it but never maintain it — ryunuck · 2026-09-21
- ReCouPLe: Reason-Augmented Preference Learning Boosts Reward Accuracy 1.5x Under Shift — burny_tech · 2026-09-21
- Siri + Home Assistant + ChatGPT Voice: an automated grocery and meal-planning pipeline — athyuttamre · 2026-09-21
- 99.7% cache hits: engineered DeepSeek Harness with self-hosted GLM-5.3 — burny_tech · 2026-09-21
- Same AI agent scores 97% or 39% depending on how you define success — hugobowne · 2026-09-21
- Solo dev open-sources 4 systems projects, asks engineers to roast them — Accomplished_Row1433 · 2026-09-21