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20232026
most citedA Comprehensive Study of Knowledge Editing for Large Language Models

21 citations · 51 across the 55 of their papers we have counts for

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Showing 2024Show all

15 papers · 1 filter

cs.LG2024

STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading

Yilei Zhao, Wentao Zhang, Tingran Yang +3

In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. Recently, we have witnessed the rise of variational autoencoder-base…

cs.CL2024

KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models

Zhen Zhang, Xinyu Wang, Yong Jiang +7

Large Language Models (LLMs) often struggle with dynamically changing knowledge and handling unknown static information. Retrieval-Augmented Generation (RAG) is employed to tackle…

cs.CL2024

An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms

Ziyang Chen, Xiaobin Wang, Yong Jiang +4

Question Answering (QA) systems face challenges in handling complex questions that require multi-domain knowledge synthesis. The naive RAG models, although effective in information…

cs.CL2024★ 2 cited

Benchmarking Agentic Workflow Generation

Shuofei Qiao, Runnan Fang, Zhisong Qiu +6

Large Language Models (LLMs), with their exceptional ability to handle a wide range of tasks, have driven significant advancements in tackling reasoning and planning tasks, wherein…

cs.CL2024

Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process

Peng Wang, Xiaobin Wang, Chao Lou +3

In-context learning (ICL) is a few-shot learning paradigm that involves learning mappings through input-output pairs and appropriately applying them to new instances. Despite the r…

cs.CL2024

Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation

Chaoyi Ai, Yong Jiang, Shen Huang +2

Named entity recognition (NER) models often struggle with noisy inputs, such as those with spelling mistakes or errors generated by Optical Character Recognition processes, and lea…