22 citations · 24 across the 5 of their papers we have counts for
5 papers · 1 filter
The False Promise of Imitating Proprietary LLMs
Arnav Gudibande, Eric Wallace, Charlie Snell +5
An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Inst…
Benchmarking Language Models for Code Syntax Understanding
Da Shen, Xinyun Chen, Chenguang Wang +2
Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding, which represent the input as a token sequence wi…
IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models
Chenguang Wang, Xiao Liu, Dawn Song
We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, ma…
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Ye Yuan +5
This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM…
Joint Language Semantic and Structure Embedding for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Linyuan Gong +1
The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike…