Saved in:
| Main Authors: | Li, Shengqi, Gupta, Amarnath |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.02931 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Generating High Quality Synthetic Data for Dutch Medical Conversations
by: Kuan, Cecilia, et al.
Published: (2026)
by: Kuan, Cecilia, et al.
Published: (2026)
OSVBench: Benchmarking LLMs on Specification Generation Tasks for Operating System Verification
by: Li, Shangyu, et al.
Published: (2025)
by: Li, Shangyu, et al.
Published: (2025)
MISCON: A Mission-Driven Conversational Consultant for Pre-Venture Entrepreneurs in Food Deserts
by: Dasgupta, Subhasis, et al.
Published: (2025)
by: Dasgupta, Subhasis, et al.
Published: (2025)
Actionable Conversational Quality Indicators for Improving Task-Oriented Dialog Systems
by: Higgins, Michael, et al.
Published: (2021)
by: Higgins, Michael, et al.
Published: (2021)
What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models
by: Zhu, Shengqi, et al.
Published: (2024)
by: Zhu, Shengqi, et al.
Published: (2024)
Exploring Design Choices for Building Language-Specific LLMs
by: Tejaswi, Atula, et al.
Published: (2024)
by: Tejaswi, Atula, et al.
Published: (2024)
LLMs and Memorization: On Quality and Specificity of Copyright Compliance
by: Mueller, Felix B, et al.
Published: (2024)
by: Mueller, Felix B, et al.
Published: (2024)
Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks
by: Pan, Wenbo, et al.
Published: (2025)
by: Pan, Wenbo, et al.
Published: (2025)
Reading between the Lines: Can LLMs Identify Cross-Cultural Communication Gaps?
by: Saha, Sougata, et al.
Published: (2025)
by: Saha, Sougata, et al.
Published: (2025)
Dialogue You Can Trust: Human and AI Perspectives on Generated Conversations
by: Ebubechukwu, Ike, et al.
Published: (2024)
by: Ebubechukwu, Ike, et al.
Published: (2024)
MedMT-Bench: Can LLMs Memorize and Understand Long Multi-Turn Conversations in Medical Scenarios?
by: Yang, Lin, et al.
Published: (2026)
by: Yang, Lin, et al.
Published: (2026)
Can Large Language Models Generate Effective Datasets for Emotion Recognition in Conversations?
by: Kaplan, Burak Can, et al.
Published: (2025)
by: Kaplan, Burak Can, et al.
Published: (2025)
Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation
by: Schleifer, Abigail Victoria Gurin, et al.
Published: (2026)
by: Schleifer, Abigail Victoria Gurin, et al.
Published: (2026)
GneissWeb: Preparing High Quality Data for LLMs at Scale
by: Gohari, Hajar Emami, et al.
Published: (2025)
by: Gohari, Hajar Emami, et al.
Published: (2025)
Provable Benefits of Task-Specific Prompts for In-context Learning
by: Chang, Xiangyu, et al.
Published: (2025)
by: Chang, Xiangyu, et al.
Published: (2025)
Identifying High-Confidence Social Biases in LLMs for Trustworthy Conversational Tutoring Agents
by: Alvarez, Aitor Arronte, et al.
Published: (2026)
by: Alvarez, Aitor Arronte, et al.
Published: (2026)
Can LLMs Generate Visualizations with Dataless Prompts?
by: Coelho, Darius, et al.
Published: (2024)
by: Coelho, Darius, et al.
Published: (2024)
Strategic Prompting for Conversational Tasks: A Comparative Analysis of Large Language Models Across Diverse Conversational Tasks
by: Joshi, Ratnesh Kumar, et al.
Published: (2024)
by: Joshi, Ratnesh Kumar, et al.
Published: (2024)
Large-Scale Constraint Generation -- Can LLMs Parse Hundreds of Constraints?
by: Boffa, Matteo, et al.
Published: (2025)
by: Boffa, Matteo, et al.
Published: (2025)
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning
by: Singh, Ayush, et al.
Published: (2024)
by: Singh, Ayush, et al.
Published: (2024)
Pitfalls of Conversational LLMs on News Debiasing
by: Schlicht, Ipek Baris, et al.
Published: (2024)
by: Schlicht, Ipek Baris, et al.
Published: (2024)
Outlier Dimensions Encode Task-Specific Knowledge
by: Rudman, William, et al.
Published: (2023)
by: Rudman, William, et al.
Published: (2023)
The Battle of LLMs: A Comparative Study in Conversational QA Tasks
by: Rangapur, Aryan, et al.
Published: (2024)
by: Rangapur, Aryan, et al.
Published: (2024)
Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations
by: Dutt, Ritam, et al.
Published: (2024)
by: Dutt, Ritam, et al.
Published: (2024)
Conversation Kernels: A Flexible Mechanism to Learn Relevant Context for Online Conversation Understanding
by: Agarwal, Vibhor, et al.
Published: (2025)
by: Agarwal, Vibhor, et al.
Published: (2025)
Can LLMs Infer Conversational Agent Users' Personality Traits from Chat History?
by: Cögendez, Derya, et al.
Published: (2026)
by: Cögendez, Derya, et al.
Published: (2026)
Adversarial Attacks and Defense for Conversation Entailment Task
by: Yang, Zhenning, et al.
Published: (2024)
by: Yang, Zhenning, et al.
Published: (2024)
Structured Thinking Matters: Improving LLMs Generalization in Causal Inference Tasks
by: Sun, Wentao, et al.
Published: (2025)
by: Sun, Wentao, et al.
Published: (2025)
Enough Coin Flips Can Make LLMs Act Bayesian
by: Gupta, Ritwik, et al.
Published: (2025)
by: Gupta, Ritwik, et al.
Published: (2025)
Qworld: Question-Specific Evaluation Criteria for LLMs
by: Gao, Shanghua, et al.
Published: (2026)
by: Gao, Shanghua, et al.
Published: (2026)
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
by: Liu, An, et al.
Published: (2024)
by: Liu, An, et al.
Published: (2024)
Can LLMs Ask Good Questions?
by: Zhang, Yueheng, et al.
Published: (2025)
by: Zhang, Yueheng, et al.
Published: (2025)
Can LLMs Explain Themselves Counterfactually?
by: Dehghanighobadi, Zahra, et al.
Published: (2025)
by: Dehghanighobadi, Zahra, et al.
Published: (2025)
Can GNN be Good Adapter for LLMs?
by: Huang, Xuanwen, et al.
Published: (2024)
by: Huang, Xuanwen, et al.
Published: (2024)
Can LLMs Capture Human Preferences?
by: Goli, Ali, et al.
Published: (2023)
by: Goli, Ali, et al.
Published: (2023)
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements
by: Li, Ming, et al.
Published: (2024)
by: Li, Ming, et al.
Published: (2024)
All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
by: Lu, Lei, et al.
Published: (2024)
by: Lu, Lei, et al.
Published: (2024)
Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?
by: Klievtsova, Nataliia, et al.
Published: (2023)
by: Klievtsova, Nataliia, et al.
Published: (2023)
Task-Specific Knowledge Distillation via Intermediate Probes
by: Brown, Ryan, et al.
Published: (2026)
by: Brown, Ryan, et al.
Published: (2026)
AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning
by: Cui, Jiaxi, et al.
Published: (2024)
by: Cui, Jiaxi, et al.
Published: (2024)
Similar Items
-
Generating High Quality Synthetic Data for Dutch Medical Conversations
by: Kuan, Cecilia, et al.
Published: (2026) -
OSVBench: Benchmarking LLMs on Specification Generation Tasks for Operating System Verification
by: Li, Shangyu, et al.
Published: (2025) -
MISCON: A Mission-Driven Conversational Consultant for Pre-Venture Entrepreneurs in Food Deserts
by: Dasgupta, Subhasis, et al.
Published: (2025) -
Actionable Conversational Quality Indicators for Improving Task-Oriented Dialog Systems
by: Higgins, Michael, et al.
Published: (2021) -
What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models
by: Zhu, Shengqi, et al.
Published: (2024)