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Inference-time intervention: Eliciting truthful answers from a language model
Abstract We introduce Inference-Time Intervention (ITI), a technique designed to enhance
the" truthfulness" of large language models (LLMs). ITI operates by shifting model activations …
the" truthfulness" of large language models (LLMs). ITI operates by shifting model activations …
Self-play fine-tuning converts weak language models to strong language models
Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …
Camel: Communicative agents for" mind" exploration of large language model society
The rapid advancement of chat-based language models has led to remarkable progress in
complex task-solving. However, their success heavily relies on human input to guide the …
complex task-solving. However, their success heavily relies on human input to guide the …
Open problems and fundamental limitations of reinforcement learning from human feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …
to align with human goals. RLHF has emerged as the central method used to finetune state …
Language models can solve computer tasks
Agents capable of carrying out general tasks on a computer can improve efficiency and
productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally …
productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally …