# Change My Frame: Reframing in the Wild in r/ChangeMyView

arXiv: https://arxiv.org/abs/2407.02637 · HTML: https://mlsanity.com/paper/2407.02637/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023)

## Built on by (most cited first, 1 of 1)

- [PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process](https://mlsanity.com/paper/2507.04607/) (2025)

## Abstract (arXiv)

Recent work in reframing, within the scope of text style transfer, has so far made use of out-of-context, task-prompted utterances in order to produce neutralizing or optimistic reframes. Our work aims to generalize reframing based on the subreddit r/ChangeMyView (CMV). We build a dataset that leverages CMV's community's interactions and conventions to identify high-value, community-recognized utterances that produce changes of perspective. With this data, we widen the scope of the direction of reframing since the changes in perspective do not only occur in neutral or positive directions. We fine tune transformer-based models, make use of a modern LLM to refine our dataset, and explore challenges in the dataset creation and evaluation around this type of reframing.