Tag: LLaMA
RLHF vs. Diversity
2026-07-27 LLaMA alignment fine-tuning text Turning a base model into a chatbot or agent usually involves extensive Reinforcement Learning with Human Feedback (RLHF). Does that impair the model's ability to provide diverse output and avoid the Mad Libs phenomenon? Access: $$$ Pro
Automatic prompt injection
2026-07-20 LLaMA applications prompting text tokenization training utility Discrete optimization can be used to create universal prompt injections that subvert multiple different task prompts. Access: $ Basic
Truthiness-focused search
2026-02-09 LLaMA evaluation hallucination sampling text truthfulness It appears that the earlier, shallower layers of a transformer-type language model learn syntax, and later, deeper layers learn factual information. So can we boost factual accuracy by boosting the effect of deeper layers? I take the view that that's analogous to dosing the model with a mind-altering drug. Access: $$$ Pro
Quis custodiet reward models
2025-09-29 alignment training text LLaMA Gemma Large language models are "aligned" using smaller, specially trained reward models. These are often secret, and poorly studied even if public. This paper opens the door to exploring reward models by asking them about their values. Access: Free account
LLaMA introduction
2025-09-22 model-intro text LLaMA Facebook's entry into the LLM game: the first "open" version of LLaMA from 2023. This is a fairly conventional Transformer-type architecture, influential on the field because it created pressure for everybody to release weights of their announced models. Access: $$$ Pro
Better (than) tokenization with BLTs
2025-08-01 theory text LLaMA tokenization Using "patches" of input bytes, instead of a fixed token list, allows better scalability and improves performance on some tasks that are hard for token-based LLMs. Access: $ Basic