babylm
for NLP researchers
Why this scored 11
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Outlook
low confidence · estimate, not a guarantee7-day
~7
range 0–18
14-day
~0
range 0–14
30-day
~0
range 0–19
Signal history
7-day window (free)projected trajectory (estimate, not a guarantee)
The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
- 119
Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model
Augustinian BabyLM assesses small-model learning from ostensive definitions. · for LLM researchers
arxivSteadyai5d ago - 215
Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM
Analyzes native-language evaluation and tokenizer sensitivity on French-only BabyLM dataset. · for NLP researchers
arxivSteadyai1h ago