Update README.md

README.md

@@ -10,9 +10,31 @@ pretty_name: bridge
 size_categories:
 - n<1K
 ---
+TLDR: This dataset is a real-world math tutoring dataset from the NAACL 2024 paper ``Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes''.
+The dataset targets scenarios where the student makes a math mistake.
 
-This dataset is a real-world math tutoring dataset from the NAACL 2024 paper ``Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes''.
+# 🌁 Bridging the Novice-Expert Gap via Models of Decision-Making
+
+[Paper Link](https://arxiv.org/abs/2310.10648), [Code Link](https://github.com/rosewang2008/bridge/)
+
+**NAACL 2024**
+
+**Title:** Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes
+
+**Authors:** Rose E. Wang, Qingyang Zhang, Carly Robinson, Susanna Loeb, Dorottya Demszky
+
+**Main Idea: We contribute Bridge 🌁, a method that uses cognitive task analysis to translate an expert's implicit thought process into an explicit decision-making model**.
+
+Scaling high-quality tutoring remains a major challenge in education.
+Due to growing demand, many platforms employ novice tutors who, unlike experienced educators, struggle to address student mistakes and thus fail to seize prime learning opportunities.
+Our work explores the potential of large language models (LLMs) to close the novice-expert knowledge gap in remediating math mistakes.
+**Bridge 🌁 leverages cognitive task analysis to model an expert's internal decision-making in remediation: Experts internally identify (A) the student's error, (B) a remediation strategy, and (C) their intention before generating a response.**
+We construct a dataset of 700 real tutoring conversations, annotated by experts with their decisions.
+We evaluate state-of-the-art LLMs on our dataset and find that the expert's decision-making model is critical for LLMs to close the gap: 
+responses from GPT4 with expert decisions (e.g., ``simplify the problem'') are +76% more preferred than without.
+Additionally, context-sensitive decisions are critical to closing pedagogical gaps: 
+random decisions decrease GPT4's response quality by -97% than expert decisions.
+Our work shows the potential of embedding expert thought processes in LLM generations to enhance their capability to bridge novice-expert knowledge gaps. 
 
-The dataset targets scenarios where the student makes a math mistake.
 
 For more information about how the dataset is curated, please check out our codebase: https://github.com/rosewang2008/bridge/, and paper: https://arxiv.org/pdf/2310.10648