1k citations · 2.7k across the 61 of their papers we have counts for
10 papers · 1 filter
Alignment for Honesty
Yuqing Yang, Ethan Chern, Xipeng Qiu +2
Recent research has made significant strides in aligning large language models (LLMs) with helpfulness and harmlessness. In this paper, we argue for the importance of alignment for…
FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
I-Chun Chern, Steffi Chern, Shiqi Chen +6
The emergence of generative pre-trained models has facilitated the synthesis of high-quality text, but it has also posed challenges in identifying factual errors in the generated t…
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
I-Chun Chern, Zhiruo Wang, Sanjan Das +3
Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastiv…
DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions
Vijay Viswanathan, Luyu Gao, Tongshuang Wu +2
Modern machine learning relies on datasets to develop and validate research ideas. Given the growth of publicly available data, finding the right dataset to use is increasingly dif…
Multi-Dimensional Evaluation of Text Summarization with In-Context Learning
Sameer Jain, Vaishakh Keshava, Swarnashree Mysore Sathyendra +4
Evaluation of natural language generation (NLG) is complex and multi-dimensional. Generated text can be evaluated for fluency, coherence, factuality, or any other dimensions of int…
GlobalBench: A Benchmark for Global Progress in Natural Language Processing
Yueqi Song, Catherine Cui, Simran Khanuja +9
Despite the major advances in NLP, significant disparities in NLP system performance across languages still exist. Arguably, these are due to uneven resource allocation and sub-opt…
LIMA: Less Is More for Alignment
Chunting Zhou, Pengfei Liu, Puxin Xu +12
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and re…
On Learning to Summarize with Large Language Models as References
Yixin Liu, Kejian Shi, Katherine S He +5
Recent studies have found that summaries generated by large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarizat…
Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation
Yixin Liu, Alexander R. Fabbri, Yilun Zhao +5
Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics. In this work, we develop strong-performing automatic metrics for refe…
GPTScore: Evaluate as You Desire
Jinlan Fu, See-Kiong Ng, Zhengbao Jiang +1
Generative Artificial Intelligence (AI) has enabled the development of sophisticated models that are capable of producing high-caliber text, images, and other outputs through the u…