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20242026
most citedBeyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement

3 citations · 6 across the 10 of their papers we have counts for

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9 papers · 1 filter

cs.CL2026

Bridging What the Model Thinks and How It Speaks: Expressive Speech Generation via Self-Aware Intent-Realization Alignment

Kuang Wang, Lai Wei, Ping Lin +8

Speech Language Models (SLMs) exhibit strong semantic understanding, yet often fail to translate this capacity into expressive acoustic realization, producing speech with flattened…

cs.CL2025

EchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Models

Li Zhou, Lutong Yu, You Lyu +6

Speech Language Models (SLMs) have made significant progress in spoken language understanding. Yet it remains unclear whether they can fully perceive non lexical vocal cues alongsi…

cs.CL20251 cited

MTalk-Bench: Evaluating Speech-to-Speech Models in Multi-Turn Dialogues via Arena-style and Rubrics Protocols

Yuhao Du, Qianwei Huang, Guo Zhu +9

The rapid advancement of speech-to-speech (S2S) large language models (LLMs) has significantly improved real-time spoken interaction. However, current evaluation frameworks remain…

cs.CL2025

Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations

Li Zhou, Hao Jiang, Junjie Li +4

Explicit structural information has been proven to be encoded by Graph Neural Networks (GNNs), serving as auxiliary knowledge to enhance model capabilities and improve performance…

cs.CL2025

Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural Understanding and Transcreation

Li Zhou, Lutong Yu, Dongchu Xie +3

Culture is a rich and dynamic domain that evolves across both geography and time. However, existing studies on cultural understanding with vision-language models (VLMs) primarily e…

cs.CL2025

From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test

Xunlian Dai, Li Zhou, Benyou Wang +1

The human-centered word association test (WAT) serves as a cognitive proxy, revealing sociocultural variations through culturally shared semantic expectations and implicit linguist…