Image de couverture de livre

Can LLMs Generate High-Quality Task-Specific Conversations?

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Li, Shengqi, Gupta, Amarnath
Format:	Preprint
Publié:	2025
Sujets:	Computation and Language Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Generating High Quality Synthetic Data for Dutch Medical Conversations
par: Kuan, Cecilia, et autres
Publié: (2026)

OSVBench: Benchmarking LLMs on Specification Generation Tasks for Operating System Verification
par: Li, Shangyu, et autres
Publié: (2025)

Actionable Conversational Quality Indicators for Improving Task-Oriented Dialog Systems
par: Higgins, Michael, et autres
Publié: (2021)

Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks
par: Pan, Wenbo, et autres
Publié: (2025)

Exploring Design Choices for Building Language-Specific LLMs
par: Tejaswi, Atula, et autres
Publié: (2024)

What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models
par: Zhu, Shengqi, et autres
Publié: (2024)

Reading between the Lines: Can LLMs Identify Cross-Cultural Communication Gaps?
par: Saha, Sougata, et autres
Publié: (2025)

LLMs and Memorization: On Quality and Specificity of Copyright Compliance
par: Mueller, Felix B, et autres
Publié: (2024)

Dialogue You Can Trust: Human and AI Perspectives on Generated Conversations
par: Ebubechukwu, Ike, et autres
Publié: (2024)

MedMT-Bench: Can LLMs Memorize and Understand Long Multi-Turn Conversations in Medical Scenarios?
par: Yang, Lin, et autres
Publié: (2026)

Can Large Language Models Generate Effective Datasets for Emotion Recognition in Conversations?
par: Kaplan, Burak Can, et autres
Publié: (2025)

Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation
par: Schleifer, Abigail Victoria Gurin, et autres
Publié: (2026)

GneissWeb: Preparing High Quality Data for LLMs at Scale
par: Gohari, Hajar Emami, et autres
Publié: (2025)

Identifying High-Confidence Social Biases in LLMs for Trustworthy Conversational Tutoring Agents
par: Alvarez, Aitor Arronte, et autres
Publié: (2026)

Provable Benefits of Task-Specific Prompts for In-context Learning
par: Chang, Xiangyu, et autres
Publié: (2025)

Large-Scale Constraint Generation -- Can LLMs Parse Hundreds of Constraints?
par: Boffa, Matteo, et autres
Publié: (2025)

Strategic Prompting for Conversational Tasks: A Comparative Analysis of Large Language Models Across Diverse Conversational Tasks
par: Joshi, Ratnesh Kumar, et autres
Publié: (2024)

MISCON: A Mission-Driven Conversational Consultant for Pre-Venture Entrepreneurs in Food Deserts
par: Dasgupta, Subhasis, et autres
Publié: (2025)

Pitfalls of Conversational LLMs on News Debiasing
par: Schlicht, Ipek Baris, et autres
Publié: (2024)

Outlier Dimensions Encode Task-Specific Knowledge
par: Rudman, William, et autres
Publié: (2023)

Structured Thinking Matters: Improving LLMs Generalization in Causal Inference Tasks
par: Sun, Wentao, et autres
Publié: (2025)

Can LLMs Generate Visualizations with Dataless Prompts?
par: Coelho, Darius, et autres
Publié: (2024)

Adversarial Attacks and Defense for Conversation Entailment Task
par: Yang, Zhenning, et autres
Publié: (2024)

Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations
par: Dutt, Ritam, et autres
Publié: (2024)

Qworld: Question-Specific Evaluation Criteria for LLMs
par: Gao, Shanghua, et autres
Publié: (2026)

PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
par: Liu, An, et autres
Publié: (2024)

Conversation Kernels: A Flexible Mechanism to Learn Relevant Context for Online Conversation Understanding
par: Agarwal, Vibhor, et autres
Publié: (2025)

Can LLMs Ask Good Questions?
par: Zhang, Yueheng, et autres
Publié: (2025)

Can LLMs Explain Themselves Counterfactually?
par: Dehghanighobadi, Zahra, et autres
Publié: (2025)

Can GNN be Good Adapter for LLMs?
par: Huang, Xuanwen, et autres
Publié: (2024)

Can LLMs Capture Human Preferences?
par: Goli, Ali, et autres
Publié: (2023)

Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?
par: Klievtsova, Nataliia, et autres
Publié: (2023)

All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
par: Lu, Lei, et autres
Publié: (2024)

Protoknowledge Shapes Behaviour of LLMs in Downstream Tasks: Memorization and Generalization with Knowledge Graphs
par: Ranaldi, Federico, et autres
Publié: (2025)

Task-Specific Knowledge Distillation via Intermediate Probes
par: Brown, Ryan, et autres
Publié: (2026)

AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning
par: Cui, Jiaxi, et autres
Publié: (2024)

Can LLMs be Good Graph Judge for Knowledge Graph Construction?
par: Huang, Haoyu, et autres
Publié: (2024)

"I know myself better, but not really greatly": How Well Can LLMs Detect and Explain LLM-Generated Texts?
par: Ji, Jiazhou, et autres
Publié: (2025)

Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs
par: Tie, Guiyao, et autres
Publié: (2025)

Can LLMs Infer Conversational Agent Users' Personality Traits from Chat History?
par: Cögendez, Derya, et autres
Publié: (2026)