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Richard Baraniuk

Richard Baraniuk has 4 papers in our library, in 2023–2025, most of them on Language models & pretraining, Dialogue systems, Reasoning; often with Naiming Liu, Shashank Sonkar, Alexander Scarlatos.

4 papers in our library · 2023–2025

OpenAlex · ORCID · Wikidata · h-index 100 · 801 works · 52,567 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 100 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7022

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Rice UniversityHouston · United States1993–2026 · 576 papersEmploymentcurrent🏥Houston MethodistHouston · United States1989–2020 · 76 papers🎓University of Illinois Urbana-ChampaignUrbana · United States1989–2020 · 9 papersEducation🎓École Normale Supérieure de LyonLyon · France1993–2003 · 5 papers🎓Columbia UniversityNew York · United States2016–2018 · 3 papers🎓Baylor College of MedicineHouston · United States2016–2019 · 2 papers🔬Centre National de la Recherche ScientifiqueParis · France1993–2008 · 2 papers🎓University of Wisconsin–MadisonMadison · United StatesEducation

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 16 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 4 worksInformation Systems · 4 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksMedicine · 1 workHealth Informatics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksDialogue systems · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper20241 paper20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles (2023 · 71 citations)
    Building intelligent tutoring systems (ITS) has traditionally depended on rules or constraints written by human experts.
  2. Pedagogical Alignment of Large Language Models (2024 · 54 citations)
    Chat-based LLMs used as tutors tend to give students the final answer right away. Good teaching instead breaks a problem into steps and offers hints. The authors call the goal of training models toward this tutoring behaviour "pedagogical alignment." They treat it as a learning from human preferences (LHP) problem…
  3. Training LLM-Based Tutors to Improve Student Learning Outcomes in Dialogues (2025 · 38 citations)
    LLM tutors are usually built by prompting or fine-tuning models to imitate good teaching practice. They are rarely optimized for whether students actually learn. The authors propose training a small open-source model, Llama 3.1 8B, to produce tutor turns that make a correct student reply more likely while still…
  4. Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions (2023 · 14 citations)
    Standard math benchmarks for LLMs only check whether a model reaches the right answer. This paper asks whether models understand mathematical concepts well enough to reason about wrong answers. It uses multiple-choice grade-school questions from the Eedi platform, where each distractor is tied to a known student…

All works

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. WIRE: Wavelet Implicit Neural Representations ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetVishwanath Saragadam et al.2023 · 187 citations
  2. CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principlesFindings of the Association for Computational Linguistics: EMNLP 2023Shashank Sonkar, Naiming Liu, Debshila Mallick, et al.2023 · 71 citations
  3. Pedagogical Alignment of Large Language ModelsFindings of the Association for Computational Linguistics: EMNLP 2024Shashank Sonkar, Kangqi Ni, Sapana Chaudhary, et al.2024 · 54 citations
  4. Training LLM-Based Tutors to Improve Student Learning Outcomes in DialoguesLecture notes in computer scienceAlexander Scarlatos, Naiming Liu, Jaewook Lee, et al.2025 · 38 citations
  5. Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with MisconceptionsarXiv:2310.02439Naiming Liu, Shashank Sonkar, Zichao Wang, et al.2023 · 14 citations
  6. Perspectives on the State and Future of Deep Learning - 2023 ↗arXiv (Cornell University) · no summary yetMicah Goldblum et al.2023 · 5 citations

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 100 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

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