Overwhelmed by Choice: Studying LLM Decision Making at Scale

arXiv:2609.32809 · cs.AI, cs.CL · Submitted 2026-09-26 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Accuracy — 65× in this paper · explained in 1 episode
• Robustness — 16× in this paper · explained in 5 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Positional Bias — 7× in this paper · explained in 2 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• International Conference on Machine Learning — 2× in this paper
• Selection Bias — 2× in this paper · explained in 4 episodes
• Agentic System — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• RAG — 1× in this paper · explained in 3 episodes

Sources

• KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration
• On the Measure of Intelligence
• RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation
• Measuring Massive Multitask Language Understanding
• MCPVerse: An Expansive, Real-World Benchmark for Agentic Tool Use
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark
• Multiple-Choice Questions are Efficient and Robust LLM Evaluators
• Large Language Models Are Not Robust Multiple Choice Selectors
• Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO
• Large Language Models for Information Retrieval: A Survey

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