Cover Image

Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Balepur, Nishant, Ravichander, Abhilasha, Rudinger, Rachel
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?
by: Balepur, Nishant, et al.
Published: (2024)

Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?
by: Balepur, Nishant, et al.
Published: (2024)

Test-Time Reasoners Are Strategic Multiple-Choice Test-Takers
by: Balepur, Nishant, et al.
Published: (2025)

Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning
by: Palta, Shramay, et al.
Published: (2024)

Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above
by: Balepur, Nishant, et al.
Published: (2025)

It's Not Easy Being Wrong: Large Language Models Struggle with Process of Elimination Reasoning
by: Balepur, Nishant, et al.
Published: (2023)

Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions
by: Wiegreffe, Sarah, et al.
Published: (2024)

On the Influence of Gender and Race in Romantic Relationship Prediction from Large Language Models
by: Sancheti, Abhilasha, et al.
Published: (2024)

BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks
by: Balepur, Nishant, et al.
Published: (2026)

DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering
by: Srikanth, Neha, et al.
Published: (2026)

Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation
by: Tang, Xuemei, et al.
Published: (2026)

Polyglots or Multitudes? Multilingual LLM Answers to Value-laden Multiple-Choice Questions
by: Labat, Léo, et al.
Published: (2026)

On the Mutual Influence of Gender and Occupation in LLM Representations
by: An, Haozhe, et al.
Published: (2025)

Whose Boat Does it Float? Improving Personalization in Preference Tuning via Inferred User Personas
by: Balepur, Nishant, et al.
Published: (2025)

Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations
by: Sun, Yiyou, et al.
Published: (2025)

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions
by: Park, Yoonah, et al.
Published: (2025)

Right Answer, Wrong Score: Uncovering the Inconsistencies of LLM Evaluation in Multiple-Choice Question Answering
by: Molfese, Francesco Maria, et al.
Published: (2025)

Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions
by: Li, Ruizhe, et al.
Published: (2024)

What Has Been Lost with Synthetic Evaluation?
by: Gill, Alexander, et al.
Published: (2025)

LLMs Provide Unstable Answers to Legal Questions
by: Blair-Stanek, Andrew, et al.
Published: (2025)

Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering
by: Srikanth, Neha, et al.
Published: (2023)

From Answers to Questions: EQGBench for Evaluating LLMs' Educational Question Generation
by: Zhou, Chengliang, et al.
Published: (2025)

Take Out Your Calculators: Estimating the Real Difficulty of Question Items with LLM Student Simulations
by: Acquaye, Christabel, et al.
Published: (2026)

Question: How do Large Language Models perform on the Question Answering tasks? Answer:
by: Fischer, Kevin, et al.
Published: (2024)

Mind the Gap: A Closer Look at Tokenization for Multiple-Choice Question Answering with LLMs
by: Sanz-Guerrero, Mario, et al.
Published: (2025)

Do LLMs Provide Consistent Answers to Health-Related Questions across Languages?
by: Schlicht, Ipek Baris, et al.
Published: (2025)

Interpreting Answers to Yes-No Questions in Dialogues from Multiple Domains
by: Wang, Zijie, et al.
Published: (2024)

Evaluating and Calibrating LLM Confidence on Questions with Multiple Correct Answers
by: Wang, Yuhan, et al.
Published: (2026)

Differentiating Choices via Commonality for Multiple-Choice Question Answering
by: Deng, Wenqing, et al.
Published: (2024)

Multiple-Choice Questions are Efficient and Robust LLM Evaluators
by: Zhang, Ziyin, et al.
Published: (2024)

HALoGEN: Fantastic LLM Hallucinations and Where to Find Them
by: Ravichander, Abhilasha, et al.
Published: (2025)

Question Answering with LLMs and Learning from Answer Sets
by: Borroto, Manuel, et al.
Published: (2025)

Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG
by: Qiu, Longpeng, et al.
Published: (2025)

A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users
by: Balepur, Nishant, et al.
Published: (2025)

Can They Dixit? Yes they Can! Dixit as a Playground for Multimodal Language Model Capabilities
by: Balepur, Nishant, et al.
Published: (2025)

Question Difficulty Ranking for Multiple-Choice Reading Comprehension
by: Raina, Vatsal, et al.
Published: (2024)

Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit
by: Seegmiller, Parker, et al.
Published: (2024)

UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions
by: Wang, Xunzhi, et al.
Published: (2024)

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering
by: Sutanto, Patrick, et al.
Published: (2024)

How often are errors in natural language reasoning due to paraphrastic variability?
by: Srikanth, Neha, et al.
Published: (2024)