Google Deepmind and other researchers present advancements in language models, showing that Transformers can handle input lengths beyond their training, extending test lengths significantly. The new models aim to increase effective context length for Large Language Models by segmenting, compressing, and looking up passages as needed, similar to human reading behavior.
📢 New preprint: Can transformers extrapolate to input lengths beyond their training input lengths? https://t.co/pNqAWYFnPw For the first time, we show that Transformers can handle decimal digit addition beyond their training lengths, extending test lengths up to 2.5x with the… https://t.co/wdmqsepivn
Excited to share our work (https://t.co/GGKi08Vz65) for reading long documents way exceeding the context window (up to 20x). Inspired by human reading paradigm, Read Agent summarizes the input episodically as gist memories, and uses them to retrieve relevant details when needed. https://t.co/RTfBMxU6Nd
We propose ReadAgent 📖, a LLM agent that reads and reasons over text up to 20x more than the raw context length. Like humans, it decides where to pause, keeps fuzzy episodic memories of past readings, and looks up detail info as needed. Just by prompting. https://t.co/7RuZE7ThQs https://t.co/scTGg8X03n
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts Uses documents similar to humans- segmenting, compressing, and looking up passages when needed - to increase the effective context length for LLMs by 3-20x. 📝https://t.co/aGip6rkf63 👨🏽💻https://t.co/bhozRrxUF8 https://t.co/RegPKnA720
Google presents A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts paper page: https://t.co/TTvVZukXjx Current Large Language Models (LLMs) are not only limited to some maximum context length, but also are not able to robustly consume long inputs. To address… https://t.co/QpJi28B0E4
Google presents: A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts proj: https://t.co/2r4VTpq3G9 abs: https://t.co/vajC2kft8r https://t.co/iyJaQwJyMQ
📢 New preprint: Can transformers extrapolate to input lengths beyond their training input lengths? https://t.co/pNqAWYFnPw For the first time, we show that Transformers can handle decimal digit addition beyond their training lengths, extending test lengths up to 2.5x with the… https://t.co/a5CbYpCZZR
Google Deepmind presents Transformers Can Achieve Length Generalization But Not Robustly paper page: https://t.co/RZPUz5j0jH Length generalization, defined as the ability to extrapolate from shorter training sequences to longer test ones, is a significant challenge for language… https://t.co/cuY1Ip1Tdy
Google presents Premise Order Matters in Reasoning with Large Language Models paper page: https://t.co/tK0aEXXaau Large language models (LLMs) have accomplished remarkable reasoning performance in various domains. However, in the domain of reasoning tasks, we discover a… https://t.co/cRbj4pfI1a