Large Language Model
6fdf57c71bc1f1ee29014b8dc52e723f-Paper-Conference.pdf
First, counterintuitively, we observe that pretrain-ing on more data shows no significant improvement in the model's capability to
Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models
We find that a small subset of "cherry" parameters exhibit a
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
Given the description of an environment and a task, we use an LLM guided by the GIF-MCTS method to iteratively generate and refine a candidate CWM. The candidate's correctness is evaluated by checking if it correctly
88dddaf430b5bc38ab8228902bb61821-Supplemental-Conference.pdf
Supplementary figure 1. Ablanullon study, each row represents the ablated layer and each column the module that is ablated from that layer, for example the first panel shows ablanullon of anullennullon - key in layer 5. Different layers in GPT2 - XL model were ablated and the consequence of ablanullon on curvature measured for 2000 sentences in UD corpus. Red bar shows the layer where ablanullon was applied. AB Supplementary figure 3. A. curvature values for sampled 2000 sentence in RWKV model ( RNN) for both trained an untrained version. B correlanullon between model generated surprisal and curvature in RWKV model. Diamonds: syntacnullc surprisal Supplementary figure 5: E ffect of different decoding strategies in GPT2 - XL sequence generanullon and its comparison to ground - truth(true) same as figure 4b in the main manuscript.
A Coordinate Discretization In this work, element coordinates are scaled proportionally into a canvas of size C
The smaller the DocSim, the better the generalization ability.
LayoutPrompter: Awaken the Design Ability of Large Language Models
In this work, we propose Layout-Prompter, which leverages large language models (LLMs) to address the above problems through in-context learning.
