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This paper proposes Multi-Stream LLMs, which transition from sequential message-based instruction tuning to parallel stream processing. This approach allows language models to simultaneously read, think, and generate across multiple concurrent data flows, addressing bottlenecks in autonomous agent applications.
This academic paper analyzes the syntactic and lexical diversity of two generations of LLMs compared to human-authored news text, finding that newer, aligned models exhibit reduced diversity.
This paper proposes Badit, a method that decomposes large language model parameters into orthogonal high-singular-value LoRA experts to mitigate cross-task interference during multi-task instruction tuning.
Talkie-1930-13b-it is a 13B parameter instruction-tuned language model trained on pre-1931 text and fine-tuned using reinforcement learning with DPO.