works on

From the 2 of 20 linked papers with an AI index.

activity
20242026
most citedTimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop

2 citations · 3 across the 4 of their papers we have counts for

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10 papers · 1 filter

cs.CL20261 cited

Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation

Haoyue Bai, Haoyu Wang, Shengyu Chen +5

Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA), yet they often struggle in domain-specific scenarios where accurate and up-to-dat…

cs.CL2025

Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning

Renliang Sun, Wei Cheng, Dawei Li +2

Chain-of-Thought (CoT) reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps. However, excessive or red…

cs.CL2025

Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection

Cong Zeng, Shengkun Tang, Yuanzhou Chen +6

The rapid advancement of large language models (LLMs) such as ChatGPT, DeepSeek, and Claude has significantly increased the presence of AI-generated text in digital communication.…

cs.CL2025

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework

Xu Zheng, Zhuomin Chen, Esteban Schafir +7

Despite the success of machine-generated text detectors, the black-box nature remains a critical limitation. Traditional explainability methods rely on token-level saliency, insuff…

cs.CL2025

MixLLM: Dynamic Routing in Mixed Large Language Models

Xinyuan Wang, Yanchi Liu, Wei Cheng +5

Large Language Models (LLMs) exhibit potential artificial generic intelligence recently, however, their usage is costly with high response latency. Given mixed LLMs with their own…

cs.CL2024

InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration

Fali Wang, Runxue Bao, Suhang Wang +4

Large Language Models (LLMs) have achieved exceptional capabilities in open generation across various domains, yet they encounter difficulties with tasks that require intensive kno…