Search Results for author:
Found 6 papers, 4 papers with code
Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?
Since the recent prosperity of Large Language Models (LLMs), there have been interleaved discussions regarding how to reduce hallucinations from LLM responses, how to increase the factuality of LLMs, and whether Knowledge Graphs (KGs), which store the world knowledge in a symbolic form, will be replaced with LLMs.
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite
Importantly, LEDO is computationally efficient compared to methods that require loss function change, and cost-effective as the resulting data can be used in the same continuous training pipeline for production.
Inductive Relation Prediction by BERT
Relation prediction in knowledge graphs is dominated by embedding based methods which mainly focus on the transductive setting.
Logic2Text: High-Fidelity Natural Language Generation from Logical Forms
If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations.
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
3) a hybrid model that combines heterogeneous information to find the answer.
Ranked #4 on
Question Answering
on HybridQA
Global Textual Relation Embedding for Relational Understanding
Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks.
Ranked #142 on
Action Classification
on Kinetics-400
