230 citations · 559 across the 22 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2023★ 14 cited
Understanding Causality with Large Language Models: Feasibility and Opportunities
Cheng Zhang, Stefan Bauer, Paul Bennett +8
We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question. We believe that…
cs.LG2022★ 19 cited
METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals
Payal Bajaj, Chenyan Xiong, Guolin Ke +7
We present an efficient method of pretraining large-scale autoencoding language models using training signals generated by an auxiliary model. Originated in ELECTRA, this training…
cs.LG2021
Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder
Shuqi Lu, Di He, Chenyan Xiong +6
Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. Autoencoder-based language models are appealing in dense ret…