11 citations · 29 across the 9 of their papers we have counts for
12 papers
CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions
Mourad Heddaya, Kyle MacMillan, Anup Malani +2
This paper introduces CaseSumm, a novel dataset for long-context summarization in the legal domain that addresses the need for longer and more complex datasets for summarization ev…
FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering
Siqiao Xue, Xiaojing Li, Fan Zhou +3
In this paper, we introduce FAMMA, an open-source benchmark for \underline{f}in\underline{a}ncial \underline{m}ultilingual \underline{m}ultimodal question \underline{a}nswering (QA…
GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting
Fan Zhou, Chen Pan, Lintao Ma +9
Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. H…
Transcrib3D: 3D Referring Expression Resolution through Large Language Models
Jiading Fang, Xiangshan Tan, Shengjie Lin +6
If robots are to work effectively alongside people, they must be able to interpret natural language references to objects in their 3D environment. Understanding 3D referring expres…
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning
Shuo Xie, Jiahao Qiu, Ankita Pasad +3
While transferring a pretrained language model, common approaches conventionally attach their task-specific classifiers to the top layer and adapt all the pretrained layers. We inv…
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters
Hongyu Zhao, Hao Tan, Hongyuan Mei
Adapter-tuning is a paradigm that transfers a pretrained language model to downstream tasks by adding and tuning a small number of new parameters. Previously proposed adapter archi…