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pro vyhledávání: '"Neo, Clement"'
Large Language Models (LLMs) generate longform text by successively sampling the next token based on the probability distribution of the token vocabulary at each decoding step. Current popular truncation sampling methods such as top-$p$ sampling, als
Externí odkaz:
http://arxiv.org/abs/2407.01082
In this paper, we investigate the interplay between attention heads and specialized "next-token" neurons in the Multilayer Perceptron that predict specific tokens. By prompting an LLM like GPT-4 to explain these model internals, we can elucidate atte
Externí odkaz:
http://arxiv.org/abs/2402.15055
Language Models (LMs) are increasingly used for a wide range of prediction tasks, but their training can often neglect rare edge cases, reducing their reliability. Here, we define a stringent standard of trustworthiness whereby the task algorithm and
Externí odkaz:
http://arxiv.org/abs/2402.02619
Autor:
Marks, Luke, Abdullah, Amir, Neo, Clement, Arike, Rauno, Krueger, David, Torr, Philip, Barez, Fazl
Reinforcement learning from human feedback (RLHF) is widely used to train large language models (LLMs). However, it is unclear whether LLMs accurately learn the underlying preferences in human feedback data. We coin the term \textit{Learned Feedback
Externí odkaz:
http://arxiv.org/abs/2310.08164