RNN LM

Johns Hopkins · 1986 · LLM · USA

A to Z

A · App: No
B · Built By: Johns Hopkins
C · CEO & Politics: N/A (academic research) — No public partisan activity documented
D · Date: 1986
E · Expertise: Intermediate+
F · Free Tier: No free tier
G · Generates: Text only
H · Deployment: Research
I · Inputs: Text
J · Jurisdiction: USA
K · Key Staffers: Tomáš Mikolov (Lead author), Sanjeev Khudanpur (Senior author)
L · Languages: English-primary
M · Model Type: LLM
N · Safety Issues: None reported
O · Open Source: Proprietary
P · Pricing: N/A (research)
Q · AIDb Rating: 4/10 · AIDb v2.0 (rule-based): Capability 4/10 · Currency 4/10 · Access 2/10 · Significance 4/10 → 3.70
R · What Makes It Special: Recurrent neural network language model (RNNLM): Mikolov, Karafiát, Burget and Černocký (Brno University of Technology) with Khudanpur (Johns Hopkins) showed at Interspeech 2010 that recurrent-network LMs, which carry context in a hidden state, beat n-gram models: a mixture of them cut perplexity by about 50% versus a state-of-the-art backoff model, and word error rate fell about 18% on the Wall Street Journal task.
S · Stock / Status: University
T · Training Cutoff: [UNKNOWN]
U · Users: [UNKNOWN]
V · Voice Chat: No
W · Weights: API only
X · X Handle: [UNKNOWN]
Y · Year Founded: [UNKNOWN]
Z · Bottom Line: Proprietary LLM from Johns Hopkins, released 1986.

Data completeness 85% · Last verified 2026-06-11 · Primary source ↗

“RNN LM.” AIDb — The AI Database. Verified 2026-06-11. https://theaidatabase.com/m/rnn-lm-johns-hopkins.html