2.2k citations · 3.1k across the 54 of their papers we have counts for
10 papers · 2 filters
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…
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…
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…
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
Avi Caciularu, Matthew E. Peters, Jacob Goldberger +2
The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose…
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies
Linyong Nan, Yilun Zhao, Weijin Zou +5
In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context th…
Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios
Yilun Zhao, Haowei Zhang, Shengyun Si +3
Tabular data is prevalent across various industries, necessitating significant time and effort for users to understand and manipulate for their information-seeking purposes. The ad…
A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents
Benjamin Newman, Luca Soldaini, Raymond Fok +2
Many real-world applications (e.g., note taking, search) require extracting a sentence or paragraph from a document and showing that snippet to a human outside of the source docume…
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…
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Rabeeh Karimi Mahabadi, Hamish Ivison, Jaesung Tae +4
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various continuous domains. However, applying continuous diffusion models to na…
LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization
Kalpesh Krishna, Erin Bransom, Bailey Kuehl +4
While human evaluation remains best practice for accurately judging the faithfulness of automatically-generated summaries, few solutions exist to address the increased difficulty a…