arXiv Machine Learning

Misconception Diagnosis From Student-Tutor Dialogue: Generate, Retrieve, Rerank

arXiv:2602. 02414v2 Announce Type: replace-cross Abstract: Timely and accurate identification of student misconceptions is key to improving learning outcomes and pre-empting the compounding of student errors.

arXiv AI

Aug 11

Enhancing Knowledge Tracing through Leakage-Free and Recency-Aware Embeddings

arXiv:2508. 17092v2 Announce Type: replace-cross Abstract: Knowledge Tracing (KT) aims to predict a student's future performance based on their sequence of interactions with learning content.

By Yahya Badran, Christine Preisach

rag benchmarks

arXiv AI

Jun 2

Identifying High-Confidence Social Biases in LLMs for Trustworthy Conversational Tutoring Agents

arXiv:2606. 01584v1 Announce Type: cross Abstract: Conversational tutoring agents have been shown to improve learning engagement and student outcomes, and large language models (LLMs) are increasingly used in these systems to provide scalable, personalized feedback.

By Aitor Arronte Alvarez, Naiyi Xie Fincham

llms agents benchmarks safety

arXiv AI

Jun 9

Margin-Adaptive Confidence Ranking for Reliable LLM Judgement

arXiv:2605. 15416v2 Announce Type: replace-cross Abstract: Jung et al.

By Gaojie Jin, Yong Tao, Lijia Yu, Tianjin Huang

llms

arXiv Machine Learning

Jun 30

MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment

arXiv:2606. 29049v1 Announce Type: new Abstract: Knowledge Tracing (KT) is important for personalized education but traditionally suffers from two key limitations: a reliance on shallow ID-based representations that neglect semantic depth and a restriction to single-granularity mastery estimation that overlooks hierarchical knowledge dependencies.

By Xinjin Li, Mengyue Wang, Yuzhen Lin, Pengbin Feng, Ziqi Sha, Yeyang Zhou, Yu Ma

llms rag benchmarks safety

arXiv AI

Jun 2

The Role of Ambiguity in Error Prediction via Uncertainty Quantification

arXiv:2606. 02093v1 Announce Type: cross Abstract: The task of Error Prediction, namely predicting whether a model output is correct, is commonly tackled with Uncertainty Quantification (UQ).

By Ieva Raminta Stali\=unait\.e, James Bishop, Andreas Vlachos

llms nlp

arXiv AI

Jun 30

ORCA: Open-ended Response Correctness Assessment for Audio Question Answering

arXiv:2512. 09066v2 Announce Type: replace-cross Abstract: Reliable assessment of the abilities of large audio language models (LALMs) is essential to advancing the state of the art.

By \v{S}imon Sedl\'a\v{c}ek, Sara Barahona, Bolaji Yusuf, Laura Herrera-Alarc\'on, Santosh Kesiraju, Cecilia Bola\~nos, Alicia Lozano-Diez, Sathvik Udupa, Fernando L\'opez, Allison Ferner, Ramani Duraiswami, Jan \v{C}ernock\'y

llms nlp benchmarks

arXiv AI

Jul 29

Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on a Math Textbook

arXiv:2509. 16780v3 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise as educational aids but often lack alignment with specific course materials.

By Eason Chen, Chuangji Li, Eric Li, Zimo Xiao, Jionghao Lin, Kenneth R. Koedinger

llms rag nlp safety

arXiv AI

Aug 5

TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring

arXiv:2608. 03952v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to provide conversational practice for English-as-a-second-language (ESL) learners.

By Dongjie Yang, Siyan Lin, Leixian Shen, Rui Sheng, Huamin Qu, Zixin Chen

llms fine-tuning benchmarks

arXiv AI

Jul 9

Future Confidence Distillation in Large Language Models

arXiv:2607. 07626v1 Announce Type: cross Abstract: Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as retrieval, tool use, and adaptive computation depend on accurately estimating answer reliability.

By Sahil Kale

llms agents efficiency

arXiv AI

Jun 10

RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty

arXiv:2602. 12424v2 Announce Type: replace-cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving advancements in the field.

By Ziqian Zhang, Xingjian Hu, Yue Huang, Kai Zhang, Ruoxi Chen, Yixin Liu, Qingsong Wen, Kaidi Xu, Xiangliang Zhang, Neil Zhenqiang Gong, Lichao Sun

llms benchmarks

arXiv AI

Jun 16

A Self Consistency Based Reranking for Narrative Question Answering

arXiv:2606. 15741v1 Announce Type: cross Abstract: Narrative question answering (NQA) is a challenging task in natural language processing that requires models to understand long textual contexts, capture relationships across events, and generate coherent responses.

By Molham Mohamed, Ali Hamdi

llms rag nlp fine-tuning

arXiv AI

Jul 31

DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English

arXiv:2601. 22888v4 Announce Type: replace-cross Abstract: More than 80% of the 1.

By Jio Oh, Paul Vicinanza, Thomas Butler, Steven Euijong Whang, Dezhi Hong, Amani Namboori

llms benchmarks data-engineering

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