Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions
Fuente:
arXiv
Saved in:
| Main Authors: | Tang, Chenming, Wang, Zhixiang, Sun, Hao, Wu, Yunfang |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation
by: Tang, Chenming, et al.
Published: (2024)
by: Tang, Chenming, et al.
Published: (2024)
Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation
by: Tang, Chenming, et al.
Published: (2024)
by: Tang, Chenming, et al.
Published: (2024)
Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task
by: Qu, Fanyi, et al.
Published: (2023)
by: Qu, Fanyi, et al.
Published: (2023)
Lost in the Passage: Passage-level In-context Learning Does Not Necessarily Need a "Passage"
by: Sun, Hao, et al.
Published: (2025)
by: Sun, Hao, et al.
Published: (2025)
Aligning Language Models with Real-time Knowledge Editing
by: Tang, Chenming, et al.
Published: (2025)
by: Tang, Chenming, et al.
Published: (2025)
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction
by: Tang, Chenming, et al.
Published: (2024)
by: Tang, Chenming, et al.
Published: (2024)
Say What You Mean: Natural Language Access Control with Large Language Models for Internet of Things
by: Cheng, Ye, et al.
Published: (2025)
by: Cheng, Ye, et al.
Published: (2025)
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding
by: Qu, Fanyi, et al.
Published: (2024)
by: Qu, Fanyi, et al.
Published: (2024)
Language Models Might Not Understand You: Evaluating Theory of Mind via Story Prompting
by: Getachew, Nathaniel, et al.
Published: (2025)
by: Getachew, Nathaniel, et al.
Published: (2025)
Assessing the Performance of Chinese Open Source Large Language Models in Information Extraction Tasks
by: Cai, Yida, et al.
Published: (2024)
by: Cai, Yida, et al.
Published: (2024)
Democratizing Tool Learning with Environments Fully Simulated by a Free 8B Language Model
by: Tang, Chenming, et al.
Published: (2026)
by: Tang, Chenming, et al.
Published: (2026)
Large Language Models Know What To Say But Not When To Speak
by: Umair, Muhammad, et al.
Published: (2024)
by: Umair, Muhammad, et al.
Published: (2024)
It Is Not About What You Say, It Is About How You Say It: A Surprisingly Simple Approach for Improving Reading Comprehension
by: Shaier, Sagi, et al.
Published: (2024)
by: Shaier, Sagi, et al.
Published: (2024)
What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts
by: Yang, Chenyang, et al.
Published: (2025)
by: Yang, Chenyang, et al.
Published: (2025)
CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark
by: Zhang, Junzhao, et al.
Published: (2026)
by: Zhang, Junzhao, et al.
Published: (2026)
You Know What I'm Saying: Jailbreak Attack via Implicit Reference
by: Wu, Tianyu, et al.
Published: (2024)
by: Wu, Tianyu, et al.
Published: (2024)
Beyond Demonstrations: Dynamic Vector Construction from Latent Representations
by: Cai, Wang, et al.
Published: (2025)
by: Cai, Wang, et al.
Published: (2025)
Unleashing Large Language Models' Proficiency in Zero-shot Essay Scoring
by: Lee, Sanwoo, et al.
Published: (2024)
by: Lee, Sanwoo, et al.
Published: (2024)
When AI Tells You What You Want to Hear: Sycophantic Behavior of Large Language Models in Dementia Care Settings
by: Kolb, Christian
Published: (2026)
by: Kolb, Christian
Published: (2026)
Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality
by: Luo, Wen, et al.
Published: (2026)
by: Luo, Wen, et al.
Published: (2026)
What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
by: Feng, Shangbin, et al.
Published: (2024)
by: Feng, Shangbin, et al.
Published: (2024)
You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks
by: Gaznepoglu, Ünal Ege, et al.
Published: (2025)
by: Gaznepoglu, Ünal Ege, et al.
Published: (2025)
Large Language Models Often Say One Thing and Do Another
by: Xu, Ruoxi, et al.
Published: (2025)
by: Xu, Ruoxi, et al.
Published: (2025)
ActTraitBench: Quantifying the Knowledge-Decision Gap in Large Language Models via Human-Grounded Behavioral Validation
by: Yang, Yutong, et al.
Published: (2026)
by: Yang, Yutong, et al.
Published: (2026)
Rank-Then-Score: Enhancing Large Language Models for Automated Essay Scoring
by: Cai, Yida, et al.
Published: (2025)
by: Cai, Yida, et al.
Published: (2025)
Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing
by: Wu, Zichen, et al.
Published: (2025)
by: Wu, Zichen, et al.
Published: (2025)
FPT: Feature Prompt Tuning for Few-shot Readability Assessment
by: Wang, Ziyang, et al.
Published: (2024)
by: Wang, Ziyang, et al.
Published: (2024)
Do What I Say: A Spoken Prompt Dataset for Instruction-Following
by: Züfle, Maike, et al.
Published: (2026)
by: Züfle, Maike, et al.
Published: (2026)
HearSay Benchmark: Do Audio LLMs Leak What They Hear?
by: Wang, Jin, et al.
Published: (2026)
by: Wang, Jin, et al.
Published: (2026)
Are You Doubtful? Oh, It Might Be Difficult Then! Exploring the Use of Model Uncertainty for Question Difficulty Estimation
by: Zotos, Leonidas, et al.
Published: (2024)
by: Zotos, Leonidas, et al.
Published: (2024)
InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents
by: Zhan, Qiusi, et al.
Published: (2024)
by: Zhan, Qiusi, et al.
Published: (2024)
What You See is What You Ask: Evaluating Audio Descriptions
by: Kala, Divy, et al.
Published: (2025)
by: Kala, Divy, et al.
Published: (2025)
What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Text
by: Kanepajs, Arturs, et al.
Published: (2025)
by: Kanepajs, Arturs, et al.
Published: (2025)
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding
by: Wu, Zichen, et al.
Published: (2024)
by: Wu, Zichen, et al.
Published: (2024)
Optimal Brain Iterative Merging: Mitigating Interference in LLM Merging
by: Wang, Zhixiang, et al.
Published: (2025)
by: Wang, Zhixiang, et al.
Published: (2025)
Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis
by: Hellwig, Nils Constantin, et al.
Published: (2026)
by: Hellwig, Nils Constantin, et al.
Published: (2026)
Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models
by: Liao, Haoran, et al.
Published: (2024)
by: Liao, Haoran, et al.
Published: (2024)
Grimoire is All You Need for Enhancing Large Language Models
by: Chen, Ding, et al.
Published: (2024)
by: Chen, Ding, et al.
Published: (2024)
R-Tuning: Instructing Large Language Models to Say `I Don't Know'
by: Zhang, Hanning, et al.
Published: (2023)
by: Zhang, Hanning, et al.
Published: (2023)
Soft Prompting for Unlearning in Large Language Models
by: Bhaila, Karuna, et al.
Published: (2024)
by: Bhaila, Karuna, et al.
Published: (2024)
Similar Items
-
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation
by: Tang, Chenming, et al.
Published: (2024) -
Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation
by: Tang, Chenming, et al.
Published: (2024) -
Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task
by: Qu, Fanyi, et al.
Published: (2023) -
Lost in the Passage: Passage-level In-context Learning Does Not Necessarily Need a "Passage"
by: Sun, Hao, et al.
Published: (2025) -
Aligning Language Models with Real-time Knowledge Editing
by: Tang, Chenming, et al.
Published: (2025)