4 citations · 10 across the 6 of their papers we have counts for
6 papers
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text
Wenting Zhao, Ye Liu, Tong Niu +5
Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when solely relying on their internal knowledge, especially when…
L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models
Ansong Ni, Pengcheng Yin, Yilun Zhao +11
Recently, large language models (LLMs), especially those that are pretrained on code, have demonstrated strong capabilities in generating programs from natural language inputs in a…
Investigating Answerability of LLMs for Long-Form Question Answering
Meghana Moorthy Bhat, Rui Meng, Ye Liu +2
As we embark on a new era of LLMs, it becomes increasingly crucial to understand their capabilities, limitations, and differences. Toward making further progress in this direction,…
XGen-7B Technical Report
Erik Nijkamp, Tian Xie, Hiroaki Hayashi +22
Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LL…
Few-shot Unified Question Answering: Tuning Models or Prompts?
Srijan Bansal, Semih Yavuz, Bo Pang +2
Question-answering (QA) tasks often investigate specific question types, knowledge domains, or reasoning skills, leading to specialized models catering to specific categories of QA…
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control
Haopeng Zhang, Semih Yavuz, Wojciech Kryscinski +2
Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets. However, such models have been shown to be more pron…