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20242026
most citedA Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

18 citations · 18 across the 1 of their papers we have counts for

collaborators

7 papers

cs.SE2026

GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoning

Fali Wang, Chenglin Weng, Xianren Zhang +3

The growing demand for automated graph algorithm reasoning has attracted increasing attention in the large language model (LLM) community. Recent LLM-based graph reasoning methods…

cs.CL2026

MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval

Delvin Ce Zhang, Suhan Cui, Zhelin Chu +2

Verifying the truthfulness of claims usually requires joint multi-modal reasoning over both textual and visual evidence, such as analyzing both textual caption and chart image for…

cs.CL2025

A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domains

Xianren Zhang, Shreyas Prasad, Di Wang +4

Web agents have shown great promise in performing many tasks on ecommerce website. To assess their capabilities, several benchmarks have been introduced. However, current benchmark…

cs.LG2025

SUA: Stealthy Multimodal Large Language Model Unlearning Attack

Xianren Zhang, Hui Liu, Delvin Ce Zhang +4

Multimodal Large Language Models (MLLMs) trained on massive data may memorize sensitive personal information and photos, posing serious privacy risks. To mitigate this, MLLM unlear…

cs.CV2025

Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models

Zongyu Wu, Minhua Lin, Zhiwei Zhang +4

Large vision-language models (LVLMs) have demonstrated outstanding performance in many downstream tasks. However, LVLMs are trained on large-scale datasets, which can pose privacy…

cs.CL202418 cited

A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Fali Wang, Zhiwei Zhang, Xianren Zhang +11

Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their profi…