LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. For example, training on the context length of 8192 needs 16x computational costs in self-attention layers as […]
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AI startup Gizmo raises $3.5M by using gamified quizzes and flashcards to make learning fun
AI startup Gizmo raises $3.5M by using gamified quizzes and flashcards to make learning fun
AI startup Gizmo raises $3.5M by using gamified quizzes and flashcards to make learning fun
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Chain-of-Verification Reduces Hallucination in Large Language Models
Source: Meta Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first (i) drafts an initial response; then […]
Chain-of-Verification Reduces Hallucination in Large Language Models
Source: Meta Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first (i) drafts an initial response; then […]