31 citations · 97 across the 18 of their papers we have counts for
5 papers · 1 filter
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization
Yixin Liu, Alexander R. Fabbri, Jiawen Chen +7
While large language models (LLMs) can already achieve strong performance on standard generic summarization benchmarks, their performance on more complex summarization task setting…
Improving Large Language Model Fine-tuning for Solving Math Problems
Yixin Liu, Avi Singh, C. Daniel Freeman +2
Despite their success in many natural language tasks, solving math problems remains a significant challenge for large language models (LLMs). A large gap exists between LLMs' pass-…
ODSum: New Benchmarks for Open Domain Multi-Document Summarization
Yijie Zhou, Kejian Shi, Wencai Zhang +3
Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries. With a more inter-related document set…
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…