# Self-Correcting Large Language Models: Generation vs. Multiple Choice

arXiv: https://arxiv.org/abs/2511.09381 · HTML: https://mlsanity.com/paper/2511.09381/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- First posted on arXiv 12 Nov 2025.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Built on by (most cited first, 6 of 6)

- [ABCD: All Biases Come Disguised](https://mlsanity.com/paper/2602.17445/) (2026)
- [When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints](https://mlsanity.com/paper/2604.16916/) (2026)
- [The Benchmark Illusion: Pruned LLMs Can Pass Multiple Choice but Fail to Answer](https://mlsanity.com/paper/2606.17609/) (2026)
- [iFlip: Iterative Feedback-driven Counterfactual Example Refinement](https://mlsanity.com/paper/2601.01446/) (2026)
- [Knowing but Not Saying: Preventing Factual Access Failures in LLM SFT via Recall-Anchored Distillation](https://mlsanity.com/paper/2608.20794/) (2026)
- [Alignment of LRMs via Counter-Aligned Few-Shot Conversation Exposure](https://mlsanity.com/paper/2609.27763/) (2026)

## Abstract (arXiv)

Large language models have recently demonstrated remarkable abilities to self-correct their responses through iterative refinement, often referred to as self-consistency or self-reflection. However, the dynamics of this self-correction mechanism may differ substantially depending on whether the model is tasked with open-ended text generation or with selecting the most appropriate response from multiple predefined options. In this paper, we conduct a systematic investigation of these two paradigms by comparing performance trends and error-correction behaviors across various natural language understanding and reasoning tasks, covering language models of different scales and families. Our experimental results reveal distinct patterns of improvement and failure modes: textitWhile open-ended generation often benefits from the flexibility of re-interpretation and compositional refinement, multiple-choice selection can leverage clearer solution boundaries but may be limited by the provided options. This contrast also reflects the dual demands faced by emerging agentic LLM applications: effective agents must not only generate and refine open-ended plans or explanations, but also make reliable discrete choices when operating within constrained action spaces. Our findings, therefore, highlight that the design of self-correction mechanisms should take into account the interaction between task structure and output space, with implications for both knowledge-intensive reasoning and decision-oriented applications of LLMs.