8 citations · 24 across the 7 of their papers we have counts for
9 papers · 1 filter
Can Language Models Solve Graph Problems in Natural Language?
Heng Wang, Shangbin Feng, Tianxing He +3
Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit graphical structures, such as planning in robotics, multi-hop question answering or knowl…
Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models
Shangbin Feng, Weijia Shi, Yuyang Bai +3
By design, large language models (LLMs) are static general-purpose models, expensive to retrain or update frequently. As they are increasingly adopted for knowledge-intensive tasks…
PCFG-based Natural Language Interface Improves Generalization for Controlled Text Generation
Jingyu Zhang, James Glass, Tianxing He
Existing work on controlled text generation (CTG) assumes a control interface of categorical attributes. In this work, we propose a natural language (NL) interface, where we craft…
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models
Tianxing He, Bryan McCann, Caiming Xiong +1
In this work, we explore joint energy-based model (EBM) training during the finetuning of pretrained text encoders (e.g., Roberta) for natural language understanding (NLU) tasks. O…
A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation
Moin Nadeem, Tianxing He, Kyunghyun Cho +1
This work studies the widely adopted ancestral sampling algorithms for auto-regressive language models, which is not widely studied in the literature. We use the quality-diversity…
AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering
Seunghak Yu, Tianxing He, James Glass
Knowledge graphs (KGs) have the advantage of providing fine-grained detail for question-answering systems. Unfortunately, building a reliable KG is time-consuming and expensive as…