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20242026
most citedA Survey of Context Engineering for Large Language Models

13 citations · 29 across the 37 of their papers we have counts for

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Showing 2025 · cs.CLShow all

7 papers · 2 filters

cs.CL2025

Not in Sync: Unveiling Temporal Bias in Audio Chat Models

Jiayu Yao, Shenghua Liu, Yiwei Wang +5

Large Audio Language Models (LALMs) are increasingly applied to audio understanding and multimodal reasoning, yet their ability to locate when events occur remains underexplored. W…

cs.CL2025★ 13 cited

A Survey of Context Engineering for Large Language Models

Lingrui Mei, Jiayu Yao, Yuyao Ge +12

The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a f…

cs.CL2025

RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs

Baolong Bi, Shenghua Liu, Xingzhang Ren +7

The foundational capabilities of large language models (LLMs) are deeply influenced by the quality of their pre-training corpora. However, enhancing data quality at scale remains a…

cs.CL2025

Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation

Jiayu Yao, Shenghua Liu, Yiwei Wang +5

Multimodal Retrieval-Augmented Generation (RAG) systems have become essential in knowledge-intensive and open-domain tasks. As retrieval complexity increases, ensuring the robustne…

cs.CL2025

a1: Steep Test-time Scaling Law via Environment Augmented Generation

Lingrui Mei, Shenghua Liu, Yiwei Wang +5

Large Language Models (LLMs) have made remarkable breakthroughs in reasoning, yet continue to struggle with hallucinations, logical errors, and inability to self-correct during com…

cs.CL2025★ 1 cited

Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models

Baolong Bi, Shenghua Liu, Yiwei Wang +4

Retrieval-Augmented Generation (RAG) mitigates hallucinations in Large Language Models (LLMs) by integrating external knowledge. However, conflicts between parametric knowledge and…

cs.CL2025

Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach

Yurong Wu, Fangwen Mu, Qiuhong Zhang +8

Prompt trading has emerged as a significant intellectual property concern in recent years, where vendors entice users by showcasing sample images before selling prompt templates th…