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20232026
most citedChallenges and Contributing Factors in the Utilization of Large Language Models (LLMs)

5 citations · 5 across the 6 of their papers we have counts for

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cs.CL2026

Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering

Wuzhenghong Wen, Bowen Zhou, Jinwen Huang +5

Question Answering over Temporal Knowledge Graphs (TKGQA) has attracted growing interest for handling time-sensitive queries. However, existing methods still struggle with: 1) weak…

cs.CL2025

MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation

Yile Liu, Ziwei Ma, Xiu Jiang +3

With the rapid adoption of large language models (LLMs) in natural language processing, the ability to follow instructions has emerged as a key metric for evaluating their practica…

cs.CL2024

ChainStream: An LLM-based Framework for Unified Synthetic Sensing

Jiacheng Liu, Yuanchun Li, Liangyan Li +5

Many applications demand context sensing to offer personalized and timely services. Yet, developing sensing programs can be challenging for developers and using them is privacy-con…

cs.CL2024

Unifying Structured Data as Graph for Data-to-Text Pre-Training

Shujie Li, Liang Li, Ruiying Geng +8

Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yiel…

cs.CL20235 cited

Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs)

Xiaoliang Chen, Liangbin Li, Le Chang +4

With the development of large language models (LLMs) like the GPT series, their widespread use across various application scenarios presents a myriad of challenges. This review ini…