25 citations · 28 across the 11 of their papers we have counts for
11 papers · 1 filter
Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
Yanick Zengaffinen, Andreas Opedal, Donya Rooein +3
Modeling student misconceptions in a realistic manner is critical for AI in education. In this work, we examine how large language models (LLMs) reason about misconceptions when ge…
LLMs cannot spot math errors, even when allowed to peek into the solution
KV Aditya Srivatsa, Kaushal Kumar Maurya, Ekaterina Kochmar
Large language models (LLMs) demonstrate remarkable performance on math word problems, yet they have been shown to struggle with meta-reasoning tasks such as identifying errors in…
UNVEILING: What Makes Linguistics Olympiad Puzzles Tricky for LLMs?
Mukund Choudhary, KV Aditya Srivatsa, Gaurja Aeron +6
Large language models (LLMs) have demonstrated potential in reasoning tasks, but their performance on linguistics puzzles remains consistently poor. These puzzles, often derived fr…
Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?
KV Aditya Srivatsa, Kaushal Kumar Maurya, Ekaterina Kochmar
Large Language Models (LLMs) are increasingly used as proxy students in the development of Intelligent Tutoring Systems (ITSs) and in piloting test questions. However, to what exte…
Simulating Student-Tutor Interactions with LLMs for Multilingual Math Feedback
Junior Cedric Tonga, KV Aditya Srivatsa, Kaushal Kumar Maurya +2
Large language models (LLMs) have demonstrated the ability to generate formative feedback and instructional hints in English, making them increasingly relevant for AI-assisted educ…
Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors
Kaushal Kumar Maurya, KV Aditya Srivatsa, Kseniia Petukhova +1
In this paper, we investigate whether current state-of-the-art large language models (LLMs) are effective as AI tutors and whether they demonstrate pedagogical abilities necessary…
SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models
Kaushal Kumar Maurya, KV Aditya Srivatsa, Ekaterina Kochmar
Large language models (LLMs) have been widely adopted due to their remarkable performance across various applications, driving the accelerated development of a large number of dive…
Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing
KV Aditya Srivatsa, Kaushal Kumar Maurya, Ekaterina Kochmar
With the rapid development of LLMs, it is natural to ask how to harness their capabilities efficiently. In this paper, we explore whether it is feasible to direct each input query…
What Makes Math Word Problems Challenging for LLMs?
KV Aditya Srivatsa, Ekaterina Kochmar
This paper investigates the question of what makes math word problems (MWPs) in English challenging for large language models (LLMs). We conduct an in-depth analysis of the key lin…
Neural models for Factual Inconsistency Classification with Explanations
Tathagata Raha, Mukund Choudhary, Abhinav Menon +4
Factual consistency is one of the most important requirements when editing high quality documents. It is extremely important for automatic text generation systems like summarizatio…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…