Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read LLMs learn new concepts more effectively when they actively ask a teacher questions, reaching up to 25% better quiz scores than students who passively receive a summary.

desk verdict Strong benchmark, load-bearing confound: the dynamic condition includes quiz-feedback that the static baseline lacks, so the 'question-driven' claim is unsupported as-is. read the letter →

arxiv 2412.11388 v2 pith:UZPSJDJB submitted 2024-12-16 cs.CL

Aum Kendapadi , Kerem Zaman , Rakesh R. Menon , Shashank Srivastava This is my paper · ORCID
classification cs.CL MSC 68T5068T05
keywords interactivelearningquestion-driventeacher-studentdialoguelargelanguagemodelsconceptacquisitionstaticvsdynamicbenchmarkdatasetcold-start
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that large language models can learn new concepts through interactive, question-driven dialogue rather than passive absorption of static summaries. The INTERACT framework lets a student LLM ask a teacher LLM questions about a concept, take a quiz after each turn, and keep asking until it understands. Across 1,347 post-training-cutoff contexts — song lyrics, news articles, movie plots, academic papers, and images — interactive students consistently improved, by up to 25 percentage points, and cold-start students matched static-lesson baselines in as few as five turns. The authors argue that the student's own asking, not the quality of the teacher or the lesson, is what drives learning.

What carries the argument

The load-bearing mechanism is the INTERACT dialogue loop: at each turn the student LLM generates a single question, the teacher LLM answers using only the ground-truth context document, the student's conversation history (plus optionally a static lesson) is appended to its context, and the student completes a nine-question quiz before asking again. The framework isolates student-driven inquiry by testing on contexts published after the models' pretraining cutoff, so answers cannot be memorized, and by comparing against static-lesson and teacher-upper-bound baselines. The key ablation replaces real interactions with random ones, which collapses performance, showing the student actually uses the exchanged information.

What would settle it

Run the dynamic condition with the same quiz-performance feedback but with the student forced to receive teacher answers to a fixed sequence of questions instead of asking its own; if quiz scores improve as much as in the free-question condition, the learning gains are due to feedback, not questioning.

Watch

Extended reading notes

Core claim

The central claim is that student-initiated questioning is a genuine mechanism for concept acquisition in LLMs. When a student asks its own questions of a teacher that answers from the ground-truth context, quiz performance rises across seven model families and five domains, with absolute gains of 12 to 26 percentage points over five turns. Swapping in a stronger teacher or a higher-quality static lesson changes final performance by only about one percent, and passively reading another student's high-quality interaction transcripts does not reproduce the benefit of asking. The authors conclude that the act of asking questions — not the quality of the content consumed — is what makes interactive learning work, although students still remain below teacher-level quiz scores.

Load-bearing premise

The central causal claim would collapse if the same gains came from the quiz-performance feedback the student receives before each new question, rather than from the student's freedom to ask its own questions.

Editorial extensions

If this is right

  • Interactive LLM students can bootstrap knowledge about a brand-new topic in about five questions, reaching most of the way to teacher-level performance without any curated lesson.
  • Since teacher strength matters little after interaction, even a smaller or weaker teacher model can support effective learning, lowering the compute barrier for tutors.
  • Because passively reading strong students' transcripts does not help, interactive learners should generate their own questions rather than receive explanations.
  • The 1,347-context benchmark provides a reusable testbed for comparing conversational learning methods across lyrical, journalistic, cinematic, scientific, and visual content.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's prompt includes the student's own quiz score before question generation, so a control that gives identical feedback with fixed questions is needed to separate 'asking well' from 'knowing what to review'; this control is not reported.
  • If the gains are largely iterative exposure, then a five-hint or five-answer baseline that is not student-chosen might close much of the gap, making the comparison against a single static lesson a best-case framing.
  • A natural extension is to run more than five turns: the data suggest continued gains, so the 'matches static baselines in five turns' result may understate what interaction can achieve at ten or twenty turns.
  • The feature-analysis result (variance explained near zero except for lyrics) implies that the field still lacks a good predictive measure of what makes a question useful; measuring whether a question targets a quiz-covered fact the student got wrong might be more direct.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces INTERACT, a framework in which a 'student' LLM learns a new concept by asking a 'teacher' LLM questions over multiple dialogue turns, optionally starting from a static generated lesson. The authors construct 1,347 contexts from after the models' knowledge cutoffs (song lyrics, news articles, movie plots, academic papers, and COCO images), generate nine-question quizzes per concept with gpt-4o plus adversarial filtering, and compare static-lesson, dynamic-without-lesson, and dynamic-with-lesson conditions across seven text LLMs and two multimodal models. They report that dynamic students without a lesson improve by about 12-26 points over five turns, that dynamic students with a lesson improve modestly over the static lesson, and that passive exposure to borrowed transcripts does not substitute for interaction. RQ5 attempts to predict learning gains from 53 transcript features.

Significance. The dataset and multi-model evaluation are potentially useful resources for studying interactive concept acquisition. The authors are careful about post-cutoff data, provide code and data, use adversarial quiz filtering, and include several ablations (random interactions, borrowed transcripts, teacher/lesson quality) that go beyond a simple static-versus-dynamic comparison. However, the central causal claim—that student-initiated questioning itself drives the gains—is not supported by the current design. The dynamic condition couples questioning with quiz-performance feedback and with additional teacher-generated content, so the reported improvements cannot be attributed specifically to question-driven learning. As a description of an interactive-learning pipeline, the resource has value; as evidence for the paper's headline claim, it needs substantial additional controls.

major comments (4)
  1. [§3.3/§4.2 and Appendix D Listing 8] The dynamic-versus-static comparison is not a clean test of question-driven learning. The dynamic student's question-generation prompt (Listing 8) includes 'Quiz Performance: {{ quiz_performance }}' whenever a quiz score is available, while the static prompts (Listings 4 and 5) contain no such feedback. The dynamic condition therefore differs from the static baseline on at least three axes: the ability to pose questions, access to additional teacher answers grounded in the full context, and receipt of one's own quiz score before choosing the next question. Any of these could explain the gains in Figure 4 and Table 2, so the improvement cannot be attributed specifically to student-initiated questioning. The quiz-feedback channel is especially problematic because it lets the student target its remaining questions at the exact evaluation instrument, making part of the measured gain circular. Please add (a) a dynamic condition without quiz feedback, (b) a static condition with the same quiz feedback, and (c) a static full-context condition, or an equivalent factorial design, before claiming that interaction itself causes the improvement.
  2. [Abstract and Table 2] The abstract and introduction state that cold-start students 'match static learning baselines in as few as five dialogue turns,' but Table 2 reports recovery ratios of 83-94% of the static-lesson performance (e.g., 91.23% for gpt-4o-mini and 83.21% for Gemma-9B), not parity. Section 4.2 itself acknowledges that 'most dynamic students do not surpass the static-lesson baseline within five rounds.' The unqualified 'matching' claim should be replaced with a statement that interactive students approach but generally do not match the static-lesson baseline within five turns, with the qualification depending on domain and model.
  3. [§4.4, Tables 6-7] The RQ4 borrowed-interaction control does not hold feedback fixed. The passive transcript condition provides the dialogue to a weaker student, but the original dynamic student used its own quiz-performance feedback to choose later questions, and the passive student never receives that feedback. The conclusion that 'passive exposure ... cannot substitute for pro-active engagement' therefore conflates activity with feedback access. A passive condition that receives the same feedback signal, or a factorial manipulation that separates feedback from questioning, is needed to support this conclusion.
  4. [§4.5 and Appendix Table 12] RQ5's predictive analysis uses features defined with respect to the quiz itself, including 'Student Quiz Coverage,' 'Teacher Quiz Coverage,' and 'Student Semantic Alignment' (overlap or embedding similarity with quiz question tokens). Because the quiz is the outcome measure, these features introduce label leakage, so the reported R2 values (up to 0.14 in Song Lyrics) cannot be interpreted as evidence for genuinely predictive interaction features. Please re-run the analysis excluding all quiz-overlap/quiz-similarity features and report both sets of results, or justify why the overlap is not leakage.
minor comments (5)
  1. [Figure 1] The caption contains a typo: 'pretaining' should be 'pretraining.'
  2. [Figures 3-8] The axis labels in the supplied PDF appear as escaped Unicode sequences such as '/uni0030/uni0025' instead of readable text; these need to be rendered correctly.
  3. [Appendix A / References] Appendix A cites Srivastava and Goodman (2021), Zhou et al. (2024), Wu et al. (2024), Kim and Rush (2016), Sanh et al. (2019), and Agarwal et al. (2023), but these references are missing from the reference list.
  4. [Appendix B.3 / Table 10] The text says 'Table 10 provides some example questions the gpt-4o student LLM asked,' but Section 3.4 states gpt-4o was not evaluated as a student due to cost, and the rows of Table 10 are for gpt-4o-mini. Please correct this discrepancy.
  5. [Table 2] The column header 'Recovery of Student w/o Lesson (%) wrt S w/ L Start wrt Teacher' is ambiguous; clarify whether the denominator is the static-lesson start performance or the teacher performance, and state the convention in the caption.

Circularity Check

2 steps flagged · score 5.0 of 10

Dynamic condition's question-generation prompt feeds the student its current quiz score, so the reported interactive-learning gains are partly circular with respect to the evaluation quiz.

  1. self definitional [Appendix D, Listing 8 (dynamic student prompt); compared against §3.3 static scenarios and Table 2]
    "{% if quiz_performance -%} - ** Quiz Performance **: {{ quiz_performance }} {% - endif %}"

    The dynamic student's question-generation prompt includes the student's score on the quiz that is also the paper's dependent measure. The static baseline (Student with Lesson, §3.3 scenario 1) never receives this feedback. Therefore the reported gains (e.g., +25.77 for gpt-4o-mini in Table 2, attributed to 'interactive, question-driven learning') conflate student-initiated questioning with targeted re-optimization toward the same test: the student can use its prior quiz score to select questions that cover the very topics being tested. The outcome measure is thus partially used as an input to the intervention, making the causal claim circular with respect to the evaluation instrument.

  2. other [§4.5 (RQ5) and Table 12 feature definitions]
    "The top contributing features are cumulative exposure (number of unique tokens), overlap between quiz questions and student questions, semantic alignment between student and quiz questions, response information density, and response correctness."

    The paper's own feature analysis identifies 'overlap between quiz questions and student questions' and 'semantic alignment between student and quiz questions' as top predictors of learning gains. Given that the student is fed its quiz performance before generating each question (Listing 8), this overlap is exactly the channel through which quiz-score feedback can inflate the dynamic condition's measured improvement. It corroborates that part of the 'question-driven' gain is driven by test-aware question targeting rather than by general conceptual learning.

full rationale

No self-citation chain or imported uniqueness theorem is load-bearing here; the empirical comparison itself is the source of concern. The paper's dynamic condition includes the outcome measure in the intervention: Listing 8 inserts 'Quiz Performance' into the student's question-generation prompt, while the static baseline has no equivalent feedback. This makes the headline contrast (dynamic vs. static, up to +25.77 in Table 2) a joint effect of question-asking and quiz-score feedback, so the central causal claim that student-initiated questioning drives learning is partially circular. RQ5's finding that quiz-question overlap is a top gain predictor is consistent with this test-targeting channel. Separately, the abstract's 'matching static learning baselines' overstates Table 2's 81-93% recovery percentages, but that is a correctness/calibration issue rather than circularity. Overall, the design is not fully self-contained for the causal attribution it advertises, but the underlying data and framework are not definitionally forced; score 5 reflects one major confound, not a derivation that reduces to its inputs.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claims rest on the chosen interaction length, the self-generated quiz, and the assumption that temporal cutoff guarantees novelty. No new physical or mathematical entities are introduced.

free parameters (2)
  • num_interaction_rounds = 5
    The conclusion that cold-start students approach static baselines 'in as few as five dialogue turns' depends on this chosen number of rounds; results at other lengths are not systematically reported.
  • adversarial_filtering_max_attempts = 5
    Quiz questions are regenerated up to 5 times until gpt-4o-mini cannot answer them without context; this threshold shapes the difficulty of the evaluation instrument.
assumptions (3)
  • domain assumption Post-December-2023 contexts are unseen by all evaluated models
    The paper assumes the temporal cutoff ensures novelty, but different models have different training cutoffs (e.g., LLaMA-3.1 may have later data), and no per-model leakage test is done (Section 3.2).
  • domain assumption gpt-4o-generated quizzes are a valid measure of concept understanding
    The evaluation instrument is machine-generated and only manually validated on a sample (97% pass criteria), so the quiz may overrepresent content the generator deems important (Section 3.2, Appendix C.2).
  • domain assumption The teacher answers faithfully using only the ground-truth context
    Teacher is prompted to use only concept_knowledge (Listing 9), but faithfulness is not automatically checked; a teacher that volunteers extra information could inflate dynamic gains.

how reviews work

0 comments
Cite this review

Pith. "Pith review of INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models." pith.science (2026). https://pith.science/paper/UZPSJDJB

@misc{pith2026241211388,
  author       = {Pith},
  title        = {Pith review of: INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UZPSJDJB}},
  note         = {Machine review of arXiv:2412.11388}
}
read the original abstract

Large language models (LLMs) excel at answering questions but remain passive learners-absorbing static data without the ability to question and refine knowledge. This paper explores how LLMs can transition to interactive, question-driven learning through student-teacher dialogues. We introduce INTERACT (INTERactive learning for Adaptive Concept Transfer), a framework in which a "student" LLM engages a "teacher" LLM through iterative inquiries to acquire knowledge across 1,347 contexts, including song lyrics, news articles, movie plots, academic papers, and images. Our experiments show that across a wide range of scenarios and LLM architectures, interactive learning consistently enhances performance, achieving up to a 25% improvement, with 'cold-start' student models matching static learning baselines in as few as five dialogue turns. Interactive setups can also mitigate the disadvantages of weaker teachers, showcasing the robustness of question-driven learning.

Figures

Figures reproduced from arXiv: 2412.11388 by the authors.

Figure 1
Figure 1. Overview of the INTERACT framework for concept learning in LLMs. Given a new concept, here, a news article from a time period outside of the LLM pretaining data, non-interactive approaches such as zero-shot prompting (left-top) and static lessons (left-bottom) fail due to lack of information or intricacies in the concept. Through dynamic interaction with a teacher (right), a student can learn about a concept more co… view at source ↗
Figure 2
Figure 2. Workflow of the evaluation process in each di [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Performance of student gpt-4o-mini models across various static and dynamic evaluation settings over five interaction rounds. Errorbars indicate the 95% confidence interval calculated by bootstrap. Performance with other LLMs can be found in App [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Average quiz performance of student and teacher LLMs across different domains. Errorbars indicate the [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Performance of student LLMs across various static and dynamic evaluation settings. Errorbars indicate [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: Performance of student MLLMs across various static and dynamic evaluation settings. Errorbars indicate [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: Performance of gpt-4o-mini across various static and dynamic evaluation settings when provided with more interaction rounds. Errorbars indicate the 95% confidence interval calculated by bootstrap. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up

    cs.LG 2025-05 reject novelty 4.0 of 10

    The framework trains small models on synthetic AI-generated data to produce PFD/PID text, then validates two examples by manual DWSIM setup, leaving the industrial-viability claim unproven.

Reference graph

Works this paper leans on

27 extracted references · 10 canonical work pages · cited by 1 Pith paper

  1. [1]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Zahra Abbasiantaeb, Yifei Yuan, Evangelos Kanoulas, and Mohammad Aliannejadi. 2024. Let the llms talk: Simulating human-to-human conversational qa via zero-shot llm-to-llm interactions. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pages 8--17

    2024

  4. [4]

    Forough Arabshahi, Kathryn Mazaitis, Toby Jia-Jun Li, Brad A Myers, and Tom Mitchell. 2020. Conversational learning. Preprint on webpage at https://forougha. github. io/paperPDF/Conversational Learning. pdf

    work page 2020

  5. [5]

    Xinyun Chen, Maxwell Lin, Nathanael Sch \"a rli, and Denny Zhou. 2024. https://openreview.net/forum?id=KuPixIqPiq Teaching large language models to self-debug . In The Twelfth International Conference on Learning Representations

    2024

  6. [6]

    Brendan Collins, Jia Deng, Kai Li, and Li Fei-Fei. 2008. Towards scalable dataset construction: An active learning approach. In Computer Vision--ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part I 10, pages 86--98. Springer

    work page 2008

  7. [7]

    Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, and Christopher R \'e . 2018. https://doi.org/10.18653/v1/P18-1175 Training classifiers with natural language explanations . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1884--1895, Melbourne, Australia....

    doi:10.18653/v1/p18-1175 2018

  8. [8]

    Kunal Handa, Yarin Gal, Ellie Pavlick, Noah Goodman, Jacob Andreas, Alex Tamkin, and Belinda Z Li. 2024. Bayesian preference elicitation with language models. arXiv preprint arXiv:2403.05534

    arXiv 2024

Show all 27 references
  1. [9]

    Geoffrey Hinton. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531

    2015 arXiv

  2. [10]

    Namgyu Ho, Laura Schmid, and Se-Young Yun. 2023. https://doi.org/10.18653/v1/2023.acl-long.830 Large language models are reasoning teachers . In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14852--14882,...

    2023 doi

  3. [11]

    Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, Kory Mathewson, Mh Tessler, Antonia Creswell, James McClelland, Jane Wang, and Felix Hill. 2022. https://doi.org/10.18653/v1/2022.findings-emnlp.38 Can language models learn from explanations in context? In Findings of the Asso...

    2022 doi

  4. [12]

    Belinda Z Li, Alex Tamkin, Noah Goodman, and Jacob Andreas. 2023. Eliciting human preferences with language models. arXiv preprint arXiv:2310.11589

    2023 arXiv

  5. [13]

    Ilgen, Emma Pierson, Pang Wei Koh, and Yulia Tsvetkov

    Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, Jonathan S. Ilgen, Emma Pierson, Pang Wei Koh, and Yulia Tsvetkov. 2024. https://openreview.net/forum?id=W4pIBQ7bAI Mediq: Question-asking LLM s and a benchmark for reliable interactive clinical reasoning . In The Thirty-...

    2024

  6. [14]

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll \'a r, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, ...

    2014

  7. [15]

    Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023. Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems, 36

    2023

  8. [16]

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 3...

    2022

  9. [17]

    Menon, Sayan Ghosh, and Shashank Srivastava

    Rakesh R. Menon, Sayan Ghosh, and Shashank Srivastava. 2022. https://doi.org/10.18653/v1/2022.acl-long.451 CLUES : A benchmark for learning classifiers using natural language explanations . In Proceedings of the 60th Annual Meeting of the Association for Computational Linguist...

    2022 doi

  10. [18]

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36

    2023

  11. [19]

    Ashwin Ram. 1991. A theory of questions and question asking. Journal of the Learning Sciences, 1(3-4):273--318

    1991

  12. [20]

    Sudha Rao and Hal Daum \'e III. 2018. https://doi.org/10.18653/v1/P18-1255 Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information . In Proceedings of the 56th Annual Meeting of the Association for Computational Lingui...

    2018 doi

  13. [21]

    Adam Roberts, Colin Raffel, and Noam Shazeer. 2020. https://doi.org/10.18653/v1/2020.emnlp-main.437 How much knowledge can you pack into the parameters of a language model? In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages...

    2020 doi

  14. [22]

    Alexis Ross and Jacob Andreas. 2024. https://doi.org/10.18653/v1/2024.acl-long.718 Toward in-context teaching: Adapting examples to students ' misconceptions . In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), p...

    2024 doi

  15. [23]

    Shashank Srivastava, Igor Labutov, and Tom Mitchell. 2017. https://doi.org/10.18653/v1/D17-1161 Joint concept learning and semantic parsing from natural language explanations . In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 152...

    2017 doi

  16. [24]

    Shashank Srivastava, Igor Labutov, and Tom Mitchell. 2019. https://doi.org/10.18653/v1/D19-1426 Learning to ask for conversational machine learning . In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Confe...

    2019 doi

  17. [25]

    Alex Tamkin, Dat Pham Nguyen, Salil Deshpande, Jesse Mu, and Noah Goodman. 2022. https://openreview.net/forum?id=0Ww7UVEoNue Active learning helps pretrained models learn the intended task . In Advances in Neural Information Processing Systems

    2022

  18. [26]

    Lev Semenovich Vygotsky and Michael Cole. 1978. Mind in society: Development of higher psychological processes. Harvard university press

    1978

  19. [27]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824--24837

    2022

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.