Copertina

What Patients Really Ask: Exploring the Effect of False Assumptions in Patient Information Seeking

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Xiong, Raymond, Jia, Furong, Wong, Lionel, Agrawal, Monica
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Diagnosing our datasets: How does my language model learn clinical information?
di: Jia, Furong, et al.
Pubblicazione: (2025)

Retrieval-augmented systems can be dangerous medical communicators
di: Wong, Lionel, et al.
Pubblicazione: (2025)

Identifying and Answering Questions with False Assumptions: An Interpretable Approach
di: Wang, Zijie, et al.
Pubblicazione: (2025)

Counting Clues: A Lightweight Probabilistic Baseline Can Match an LLM
di: Jia, Furong, et al.
Pubblicazione: (2025)

Ask or Assume? Uncertainty-Aware Clarification-Seeking in Coding Agents
di: Edwards, Nicholas, et al.
Pubblicazione: (2026)

"What's Up, Doc?": Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets
di: Paruchuri, Akshay, et al.
Pubblicazione: (2025)

What Language is This? Ask Your Tokenizer
di: Meister, Clara, et al.
Pubblicazione: (2026)

Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions
di: Zhu, Wang Bill, et al.
Pubblicazione: (2025)

When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification
di: Zhao, Jiale, et al.
Pubblicazione: (2026)

Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning
di: Zhu, Jiayuan, et al.
Pubblicazione: (2025)

Syn-QA2: Evaluating False Assumptions in Long-tail Questions with Synthetic QA Datasets
di: Daswani, Ashwin, et al.
Pubblicazione: (2024)

MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication
di: Sambara, Sraavya, et al.
Pubblicazione: (2026)

What Really is Commonsense Knowledge?
di: Do, Quyet V., et al.
Pubblicazione: (2024)

What You See is What You Ask: Evaluating Audio Descriptions
di: Kala, Divy, et al.
Pubblicazione: (2025)

Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents?
di: Gulati, Anmol, et al.
Pubblicazione: (2026)

Do Proactive Agents Really Need an LLM to Decide When to Wake and What to Anchor?
di: Liu, Xiaoze, et al.
Pubblicazione: (2026)

The Effect of Surprisal on Reading Times in Information Seeking and Repeated Reading
di: Klein, Keren Gruteke, et al.
Pubblicazione: (2024)

Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
di: An, Bang, et al.
Pubblicazione: (2024)

Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain
di: Mazzaccara, Davide, et al.
Pubblicazione: (2024)

What Do Indonesians Really Need from Language Technology? A Nationwide Survey
di: Kautsar, Muhammad Dehan Al, et al.
Pubblicazione: (2025)

What Really Counts? Examining Step and Token Level Attribution in Multilingual CoT Reasoning
di: Ferrao, Jeremias, et al.
Pubblicazione: (2025)

Exploring a New Competency Modeling Process with Large Language Models
di: Du, Silin, et al.
Pubblicazione: (2026)

Can ChatGPT Really Understand Modern Chinese Poetry?
di: Wang, Shanshan, et al.
Pubblicazione: (2026)

Exploring the Inquiry-Diagnosis Relationship with Advanced Patient Simulators
di: Liu, Zhaocheng, et al.
Pubblicazione: (2025)

Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models
di: Mieleszczenko-Kowszewicz, Wiktoria, et al.
Pubblicazione: (2025)

DeepWideSearch: Benchmarking Depth and Width in Agentic Information Seeking
di: Lan, Tian, et al.
Pubblicazione: (2025)

IntelliAsk: Learning to Ask High-Quality Research Questions via RLVR
di: Sharma, Karun, et al.
Pubblicazione: (2026)

A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models
di: Hegselmann, Stefan, et al.
Pubblicazione: (2024)

Ask LLMs Directly, "What shapes your bias?": Measuring Social Bias in Large Language Models
di: Shin, Jisu, et al.
Pubblicazione: (2024)

Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion
di: Lan, Tian, et al.
Pubblicazione: (2026)

Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions
di: Wang, Ziyue, et al.
Pubblicazione: (2023)

Shoot First, Ask Questions Later? Building Rational Agents that Explore and Act Like People
di: Grand, Gabriel, et al.
Pubblicazione: (2025)

ShoppingComp: Are LLMs Really Ready for Your Shopping Cart?
di: Tou, Huaixiao, et al.
Pubblicazione: (2025)

What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code
di: Zhao, Yuze, et al.
Pubblicazione: (2026)

What Really Controls Temporal Reasoning in Large Language Models: Tokenisation or Representation of Time?
di: Bhatia, Gagan, et al.
Pubblicazione: (2026)

Reward Models Can Improve Themselves: Reward-Guided Adversarial Failure Mode Discovery for Robust Reward Modeling
di: Pathmanathan, Pankayaraj, et al.
Pubblicazione: (2025)

Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist
di: Zhou, Zihao, et al.
Pubblicazione: (2024)

InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
di: Xi, Yunjia, et al.
Pubblicazione: (2025)

WebDancer: Towards Autonomous Information Seeking Agency
di: Wu, Jialong, et al.
Pubblicazione: (2025)

Query Understanding in LLM-based Conversational Information Seeking
di: Yuan, Yifei, et al.
Pubblicazione: (2025)