most citedSPRIG: Improving Large Language Model Performance by System Prompt Optimization

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cs.CL20261 cited

DEER: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation

Janghoon Han, Heegyu Kim, Changho Lee +6

Recent advances in large language models have enabled deep research systems that generate expert-level reports through multi-step reasoning and evidence-based synthesis. However, e…

cs.CL2026

Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models

Jiyeon Kim, Sungik Choi, Yongrae Jo +2

Diffusion-based language models (dLLMs) have emerged as a promising alternative to autoregressive language models, offering the potential for parallel token generation and bidirect…

cs.CL20263 cited

SPRIG: Improving Large Language Model Performance by System Prompt Optimization

Lechen Zhang, Tolga Ergen, Lajanugen Logeswaran +2

Large Language Models (LLMs) have shown impressive capabilities in many scenarios, but their performance depends, in part, on the choice of prompt. Past research has focused on opt…

cs.CL2025

Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages

Lechen Zhang, Yusheng Zhou, Tolga Ergen +3

System prompts provide a lightweight yet powerful mechanism for conditioning large language models (LLMs) at inference time. While prior work has focused on English-only settings,…

cs.CL2025

Revisiting LLM Value Probing Strategies: Are They Robust and Expressive?

Siqi Shen, Mehar Singh, Lajanugen Logeswaran +3

There has been extensive research on assessing the value orientation of Large Language Models (LLMs) as it can shape user experiences across demographic groups. However, several ch…