Image de couverture de livre

Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Fu, Tairan, Conde, Javier, Martínez, Gonzalo, Grandury, María, Reviriego, Pedro
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

Why Do Large Language Models (LLMs) Struggle to Count Letters?
par: Fu, Tairan, et autres
Publié: (2024)

Psycholinguistic Word Features: a New Approach for the Evaluation of LLMs Alignment with Humans
par: Conde, Javier, et autres
Publié: (2025)

Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings
par: Conde, Javier, et autres
Publié: (2025)

Have Multimodal Large Language Models (MLLMs) Really Learned to Tell the Time on Analog Clocks?
par: Fu, Tairan, et autres
Publié: (2025)

Is There a Case for Conversation Optimized Tokenizers in Large Language Models?
par: Ferrando, Raquel, et autres
Publié: (2025)

Large Language Models and Book Summarization: Reading or Remembering, Which Is Better?
par: Fu, Tairan, et autres
Publié: (2026)

Evaluating Large Language Models with Tests of Spanish as a Foreign Language: Pass or Fail?
par: Mayor-Rocher, Marina, et autres
Publié: (2024)

Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)
par: Awad, Samer, et autres
Publié: (2026)

It's the same but not the same: Do LLMs distinguish Spanish varieties?
par: Mayor-Rocher, Marina, et autres
Publié: (2025)

The Generative Energy Arena (GEA): Incorporating Energy Awareness in Large Language Model (LLM) Human Evaluations
par: Arriaga, Carlos, et autres
Publié: (2025)

To Words and Beyond: Probing Large Language Models for Sentence-Level Psycholinguistic Norms of Memorability and Reading Times
par: Clark, Thomas Hikaru, et autres
Publié: (2026)

Beyond Reproducibility: Token Probabilities Expose Large Language Model Nondeterminism
par: Fu, Tairan, et autres
Publié: (2026)

Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study
par: Martínez, Gonzalo, et autres
Publié: (2024)

Establishing Vocabulary Tests as a Benchmark for Evaluating Large Language Models
par: Martínez, Gonzalo, et autres
Publié: (2023)

Spanish and LLM Benchmarks: is MMLU Lost in Translation?
par: Plaza, Irene, et autres
Publié: (2024)

When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners
par: Zhao, Weixiang, et autres
Publié: (2025)

Speed and Conversational Large Language Models: Not All Is About Tokens per Second
par: Conde, Javier, et autres
Publié: (2025)

Can ChatGPT Learn to Count Letters?
par: Conde, Javier, et autres
Publié: (2025)

Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans
par: Reviriego, Pedro, et autres
Publié: (2023)

Lost in the Vibrations: Vision Language Models Fail the Dynamic Gauges Test
par: Fu, Tairan, et autres
Publié: (2026)

Open Conversational LLMs do not know most Spanish words
par: Conde, Javier, et autres
Publié: (2024)

Concurrent Linguistic Error Detection (CLED): a New Methodology for Error Detection in Large Language Models
par: Zhu, Jinhua, et autres
Publié: (2024)

Using large language models to estimate features of multi-word expressions: Concreteness, valence, arousal
par: Martínez, Gonzalo, et autres
Publié: (2024)

UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions
par: Wang, Xunzhi, et autres
Publié: (2024)

Right Answer, Wrong Score: Uncovering the Inconsistencies of LLM Evaluation in Multiple-Choice Question Answering
par: Molfese, Francesco Maria, et autres
Publié: (2025)

How does fine-tuning improve sensorimotor representations in large language models?
par: Wu, Minghua, et autres
Publié: (2026)

When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints
par: Chen, Yuheng, et autres
Publié: (2026)

Self-Training Large Language Models with Confident Reasoning
par: Jang, Hyosoon, et autres
Publié: (2025)

When Models Decide and When They Bind: A Two-Stage Computation for Multiple-Choice Question-Answering
par: Wong, Hugh Mee, et autres
Publié: (2026)

Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
par: Lee, Jaewook, et autres
Publié: (2024)

Hierarchical Vision-Language Reasoning for Multimodal Multiple-Choice Question Answering
par: Zhou, Ao, et autres
Publié: (2025)

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions
par: Park, Yoonah, et autres
Publié: (2025)

The #Somos600M Project: Generating NLP resources that represent the diversity of the languages from LATAM, the Caribbean, and Spain
par: Grandury, María
Publié: (2024)

Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors
par: Xue, Mengge, et autres
Publié: (2024)

A Study on Large Language Models' Limitations in Multiple-Choice Question Answering
par: Khatun, Aisha, et autres
Publié: (2024)

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights
par: Biancini, Giorgio, et autres
Publié: (2025)

La Leaderboard: A Large Language Model Leaderboard for Spanish Varieties and Languages of Spain and Latin America
par: Grandury, María, et autres
Publié: (2025)

Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?
par: Balepur, Nishant, et autres
Publié: (2024)

Self-Correcting Large Language Models: Generation vs. Multiple Choice
par: Rahmani, Hossein A., et autres
Publié: (2025)

More Bias, Less Bias: BiasPrompting for Enhanced Multiple-Choice Question Answering
par: Vu, Duc Anh, et autres
Publié: (2025)