Augmenting Language Models with Long-Term Memory Existing large language models (LLMs) can only afford fix-sized inputs due to the input length limit, preventing them from utilizing rich long-context information from past inputs. To address this, we propose a framework, Decoupled-Memor...
Augmenting Language Models with Long-Term Memory O网页链接ChatPaper综述:本文介绍了当前大型语言模型受到输入长度限制的困境,无法利用过去丰富的长上下文信息的问题,并提出了一种解决方案——LongMem框架,通过一种解耦的网络结构,能够实现将长期历史信息存储在固定的内存中,可以缓存和更新过去的上下文信息以便于在后续...
Large language models (LLMs) have demonstrated remarkable predictive performance across a growing range of diverse tasks1,2,3. However, their proliferation has led to two burgeoning problems. First, like most deep neural nets, LLMs have become increasingly difficult to interpret, often leading to ...
Official implementation of our paper "Augmenting Language Models with Long-Term Memory". Please cite our paper if you find this repository interesting or helpful: @article{LongMem, title={Augmenting Language Models with Long-Term Memory}, author={Wang, Weizhi and Dong, Li and Cheng, Hao and ...
GEAR: Augmenting Language Models with Generalizable and Efficient Tool Resolution paper: https://arxiv.org/abs/2307.08775 TL;DR: GEAR is a computationally efficient query-tool grounding algorithm that is generalizable to various tasks that require tool use while not relying on task-specific demonstrati...
Retrieval augmented language models have recently become the standard for knowledge intensive tasks. Rather than relying purely on latent semantics within the parameters of large neural models, these methods enlist a semi-parametric memory to encode an index of knowledge for the model to retrieve over...
By playing around withBuffLenandThreads, I found out that the system will never deadlock as long asThreads <= 2*BuffLen. So for a buffer length of four, you need 9 threads to have a deadlock. Wells needed an extra two (and multiple runs) to because the state space is so big, whi...
In the middle, we have tools that act like a good pair of running sneakers. They give you a temporary boost in the moment, but there are no long-term consequences when you take them away. Here, you might think of something like saving time with cumbersome syntax using autocomplete. And ...
Let us look within the process structure for the LAM/T ingredients, to get a better "feel" for our models. Consider the process of writing an important memo.There is a particular concept associated with this process -- that of putting information into a formal package and distributing it to...
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale, TACL (2022) - google-deepmind/transformer_grammars