3 papers
cs.CL2024
Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset
Chongjian Yue, Xinrun Xu, Xiaojun Ma +5
Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text,…
cs.SE2024
SECRET: Towards Scalable and Efficient Code Retrieval via Segmented Deep Hashing
Wenchao Gu, Ensheng Shi, Yanlin Wang +5
Code retrieval, which retrieves code snippets based on users' natural language descriptions, is widely used by developers and plays a pivotal role in real-world software developmen…
cs.CL2024
TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning
Yuan Sui, Jiaru Zou, Mengyu Zhou +4
Table reasoning tasks have shown remarkable progress with the development of large language models (LLMs), which involve interpreting and drawing conclusions from tabular data base…