2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
TACO: Task-Aware Column Description Generation Using LLMs
Ting Cai, Rakesh R. Menon, Yiru Chen +8
Generating accurate and informative column descriptions (e.g. "membership status of customers" for the column name "cust_mem") is essential for a wide range of downstream NLP tasks…
APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matching
Kun Qian, Yisi Sang, Farima Fatahi Bayat +11
Prompt engineering is an iterative procedure often requiring extensive manual effort to formulate suitable instructions for effectively directing large language models (LLMs) in sp…
Time Sensitive Knowledge Editing through Efficient Finetuning
Xiou Ge, Ali Mousavi, Edouard Grave +5
Large Language Models (LLMs) have demonstrated impressive capability in different tasks and are bringing transformative changes to many domains. However, keeping the knowledge in L…
Open Domain Knowledge Extraction for Knowledge Graphs
Kun Qian, Anton Belyi, Fei Wu +15
The quality of a knowledge graph directly impacts the quality of downstream applications (e.g. the number of answerable questions using the graph). One ongoing challenge when build…
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge
Farima Fatahi Bayat, Kun Qian, Benjamin Han +6
Detecting factual errors in textual information, whether generated by large language models (LLM) or curated by humans, is crucial for making informed decisions. LLMs' inability to…