activity
20232026
most citedSeqXGPT: Sentence-Level AI-Generated Text Detection

6 citations · 9 across the 16 of their papers we have counts for

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Showing cs.CLShow all

8 papers · 1 filter

cs.CL2026

WESR: Scaling and Evaluating Word-level Event-Speech Recognition

Chenchen Yang, Kexin Huang, Liwei Fan +8

Speech conveys not only linguistic information but also rich non-verbal vocal events such as laughing and crying. While semantic transcription is well-studied, the precise localiza…

cs.CL2025

UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets

Pengyu Wang, Shaojun Zhou, Chenkun Tan +7

Unified vision large language models (VLLMs) have recently achieved impressive advancements in both multimodal understanding and generation, powering applications such as visual qu…

cs.CL2025

Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLM

Chenkun Tan, Pengyu Wang, Shaojun Zhou +6

Multimodal large language models (MLLMs) have gained significant attention due to their impressive ability to integrate vision and language modalities. Recent advancements in MLLMs…

cs.CL2024★ 1 cited

Understanding the Role of LLMs in Multimodal Evaluation Benchmarks

Botian Jiang, Lei Li, Xiaonan Li +5

The rapid advancement of Multimodal Large Language Models (MLLMs) has been accompanied by the development of various benchmarks to evaluate their capabilities. However, the true na…

cs.CL2024★ 1 cited

UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model

Zhaowei Li, Wei Wang, YiQing Cai +7

Significant advancements has recently been achieved in the field of multi-modal large language models (MLLMs), demonstrating their remarkable capabilities in understanding and reas…

cs.CL2024

InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance

Pengyu Wang, Dong Zhang, Linyang Li +5

With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the…

cs.CL2023

Watermarking LLMs with Weight Quantization

Linyang Li, Botian Jiang, Pengyu Wang +3

Abuse of large language models reveals high risks as large language models are being deployed at an astonishing speed. It is important to protect the model weights to avoid malicio…

cs.CL2023★ 6 cited

SeqXGPT: Sentence-Level AI-Generated Text Detection

Pengyu Wang, Linyang Li, Ke Ren +3

Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, it is important to build strong AI-generated text…