# "You might think about slightly revising the title": identifying hedges in peer-tutoring interactions

arXiv: https://arxiv.org/abs/2306.14911 · HTML: https://mlsanity.com/paper/2306.14911/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 18 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Exploring Different Dimensions of Attention for Uncertainty Detection](https://mlsanity.com/paper/1612.06549/) (2016)

## Built on by (most cited first, 4 of 4)

- [Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations](https://mlsanity.com/paper/2404.10960/) (2024)
- [How About Kind of Generating Hedges using End-to-End Neural Models?](https://mlsanity.com/paper/2306.14696/) (2023)
- [When to generate hedges in peer-tutoring interactions](https://mlsanity.com/paper/2307.15582/) (2023)
- [Training LLMs to Recognize Hedges in Spontaneous Narratives](https://mlsanity.com/paper/2408.03319/) (2024)

## Abstract (arXiv)

Hedges play an important role in the management of conversational interaction. In peer tutoring, they are notably used by tutors in dyads (pairs of interlocutors) experiencing low rapport to tone down the impact of instructions and negative feedback. Pursuing the objective of building a tutoring agent that manages rapport with students in order to improve learning, we used a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We compared approaches relying on pre-trained resources with others that integrate insights from the social science literature. Our best performance involved a hybrid approach that outperforms the existing baseline while being easier to interpret. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations, and we identify some novel features, and the benefits of such a hybrid model approach.