Reducing Toxicity in Language Models

·Lilian Weng··

Large pretrained language models are trained over a sizable collection of online data. They unavoidably acquire certain toxic behavior and biases from the Internet. Pretrained language models are very powerful and have shown great success in many NLP tasks. However, to safely deploy them for practical real-world applications demands a strong safety control over the model generation process.

Read full article →

Related Articles

OpenAI’s o1 correctly diagnosed 67% of ER patients vs. 50-55% by triage doctors
donsupreme · Hacker News · 2mo ago
Accelerating Gemma 4: faster inference with multi-token prediction drafters
amrrs · Hacker News · 2mo ago
A couple million lines of Haskell: Production engineering at Mercury
unignorant · Hacker News · 2mo ago
Show HN: I trained a language model that thinks the capital of Japan is Paris
farisallafi · Hacker News · 9h ago
Using “underdrawings” for accurate text and numbers
samcollins · Hacker News · 2mo ago