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REVIEW 2 major objections 1 minor 48 references

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

T0 review · 2 major / 1 minor · reviewed 2026-06-28 · grok-4.3

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Pith's one-line read State-of-the-art LLMs are overconfident when they fail to spot stereotypical biases in tutoring conversations.

desk verdict The paper gives a workable method to generate conversational tutoring data with inserted biases and claims LLMs are overconfident on missed stereotypes, but the evidence details are missing and the insertion approach may not capture real bias dynamics. read the letter →

arxiv 2606.01584 v1 pith:FNWZ2LPI submitted 2026-06-01 cs.CL cs.AI

Aitor Arronte Alvarez , Naiyi Xie Fincham This is my paper · ORCID
classification cs.CLcs.AI
keywords largelanguagemodelssocialbiasdetectionconversationaltutoringmodelconfidenceeducationalAIstereotypicaldialoguegeneration
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

This paper introduces a method for generating tutoring dialogues that include controlled instances of social bias drawn from existing benchmarks. It then tests multiple large language models on identifying those biases during ongoing student-tutor exchanges. The evaluation shows that models detect bias less reliably in conversational tutoring than on isolated benchmark statements. When models err on bias questions they tend to do so with high confidence, and that confidence level shapes the explanations and feedback they produce.

What carries the argument

The dataset generation method that regenerates student-AI tutor interactions and introduces turns with controlled bias derived from a benchmark dataset.

What would settle it

Running the same LLMs on a collection of real recorded tutoring sessions that contain known stereotypical biases and measuring whether their detection accuracy and confidence scores match the patterns seen in the generated data.

Watch

Extended reading notes

Core claim

Using a new method to generate naturalistic tutoring interactions with inserted biased statements, the evaluation reveals that bias detection is substantially more challenging in conversational tutoring contexts than in benchmark-based evaluations, and that state-of-the-art LLMs are overconfident in their incorrect assessments of stereotypical bias statements. Model confidence strongly influences reasoning and feedback.

Load-bearing premise

Regenerating tutoring interactions and inserting bias from benchmarks creates conditions that match how biases would naturally affect real instructional conversations.

Editorial extensions

If this is right

  • Bias detection accuracy declines when models move from static benchmark statements to back-and-forth tutoring exchanges.
  • Incorrect bias judgments made with high confidence can distort the explanations and feedback given to learners.
  • Model confidence scores correlate with the quality and direction of reasoning steps in responses to bias-related prompts.
  • Educational applications that rely on LLMs carry an elevated risk of perpetuating stereotypes when confidence calibration is absent.

Reading between the lines

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

  • Tutoring platforms could add explicit confidence thresholds that trigger human review or alternative responses when bias detection is uncertain.
  • The same generation technique could be applied to other conversational domains such as medical or legal advice to test whether overconfidence appears there as well.
  • Longer-term studies could track whether repeated exposure to overconfident model feedback changes student attitudes toward the biased topics.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 1 minor

Summary. The paper introduces a dataset generation method that regenerates student-AI tutor interactions and inserts controlled stereotypical bias statements derived from benchmarks to evaluate LLMs as conversational tutoring agents. It claims that bias detection is substantially more challenging in these naturalistic instructional contexts than in standard benchmarks, that state-of-the-art LLMs are overconfident in their incorrect assessments of biased statements, and that model confidence strongly influences reasoning and feedback, posing risks for educational applications.

Significance. If the central claims hold after addressing methodological concerns, the work would be significant for highlighting overconfidence risks in LLM-based tutoring systems and for providing an evaluation framework that moves beyond static benchmarks toward more realistic conversational settings. This could inform mitigation strategies in educational AI, though the current evidence base appears limited.

major comments (2)
  1. [Abstract / Methods] The headline claims that bias detection is substantially more challenging in conversational tutoring contexts and that SOTA LLMs are overconfident in incorrect assessments depend on the generated dataset faithfully replicating conditions where stereotypical bias shapes reasoning and feedback. The regeneration-plus-insertion method (described in the abstract) creates a risk that spliced benchmark-derived turns produce artificial, low-context insertions whose surface features differ from gradual, contextually entangled biases in genuine instructional exchanges; without validation (e.g., human ratings of naturalness or comparison to real tutoring logs), both the comparative difficulty result and the overconfidence finding rest on an untested proxy.
  2. [Results / Evaluation] The reported findings on overconfidence and the influence of confidence on reasoning lack any mention of sample sizes, statistical tests, error analysis, or inter-annotator agreement for the human evaluations, making it impossible to determine whether the evidence supports the claims that models are overconfident or that confidence strongly influences feedback.
minor comments (1)
  1. [Abstract] The abstract states the method enables evaluation 'under naturalistic instructional conditions' without defining what counts as naturalistic or providing any operational criteria.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our methodological approach and evaluation rigor. We address each major comment below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract / Methods] The headline claims that bias detection is substantially more challenging in conversational tutoring contexts and that SOTA LLMs are overconfident in incorrect assessments depend on the generated dataset faithfully replicating conditions where stereotypical bias shapes reasoning and feedback. The regeneration-plus-insertion method (described in the abstract) creates a risk that spliced benchmark-derived turns produce artificial, low-context insertions whose surface features differ from gradual, contextually entangled biases in genuine instructional exchanges; without validation (e.g., human ratings of naturalness or comparison to real tutoring logs), both the comparative difficulty result and the overconfidence finding rest on an untested proxy.

    Authors: We agree that explicit validation of the generated dataset's naturalness would strengthen the claims. The regeneration step was intended to produce contextually coherent tutoring dialogues prior to controlled bias insertion, but the original submission did not include human ratings of naturalness or direct comparisons to real tutoring logs. In the revised manuscript, we will add a human evaluation assessing the naturalness of the interactions and the contextual fit of the bias statements. revision: yes

  2. Referee: [Results / Evaluation] The reported findings on overconfidence and the influence of confidence on reasoning lack any mention of sample sizes, statistical tests, error analysis, or inter-annotator agreement for the human evaluations, making it impossible to determine whether the evidence supports the claims that models are overconfident or that confidence strongly influences feedback.

    Authors: We acknowledge the omission of these details in the original manuscript. In the revision, we will report the sample sizes for all evaluations, include appropriate statistical tests with results, provide error analysis, and report inter-annotator agreement metrics for the human evaluations to allow proper assessment of the evidence strength. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Empirical evaluation on generated dataset with no derivations or self-referential reductions

full rationale

The paper presents an empirical study: it generates a new dataset by regenerating student-AI interactions and inserting controlled bias statements from an external benchmark, then evaluates multiple LLMs on bias detection, confidence, and reasoning via computational and human assessments. No equations, fitted parameters, predictions, or derivations are described that reduce to inputs by construction. No load-bearing self-citations or uniqueness theorems are invoked. The central claims rest on direct measurement of model outputs against the generated data, which is independent of the evaluation metrics themselves. This matches the default expectation of no significant circularity.

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

This is an empirical study on LLM bias detection with no mathematical models, free parameters, or new postulated entities.

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Cite this review

Pith. "Pith review of Identifying High-Confidence Social Biases in LLMs for Trustworthy Conversational Tutoring Agents." pith.science (2026). https://pith.science/paper/FNWZ2LPI

@misc{pith2026260601584,
  author       = {Pith},
  title        = {Pith review of: Identifying High-Confidence Social Biases in LLMs for Trustworthy Conversational Tutoring Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FNWZ2LPI}},
  note         = {Machine review of arXiv:2606.01584}
}
read the original 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. However, LLMs may perpetuate or amplify stereotypical social biases, posing particular risks in educational settings. In this study, we evaluate LLMs in conversational tutoring scenarios to identify high-confidence social biases, instances where models are unable to identify biased judgments in tutoring conversations while maintaining strong confidence in their assessments, potentially affecting their reasoning and the feedback they provide to learners. We present a new dataset generation method that enables bias evaluation under naturalistic instructional conditions by regenerating student-AI tutor interactions and introducing turns with controlled bias derived from a benchmark dataset. Using this data, we assess multiple LLMs' ability to detect stereotypical biases and analyze the confidence and reasoning underlying their responses through computational and human evaluations. We find that bias detection is substantially more challenging in conversational tutoring contexts than in benchmark-based evaluations, and that state-of-the-art LLMs are overconfident in their incorrect assessments of stereotypical bias statements. Moreover, model confidence strongly influences reasoning and feedback, highlighting the risks of overconfident, biased behavior in LLM-based tutoring agents. We conclude by discussing implications, mitigation considerations, and directions for future research.

Figures

Figures reproduced from arXiv: 2606.01584 by the authors.

Figure 1
Figure 1. Stereotypical conversational tutoring dataset generation process. [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Two confidence elicitation prompts provided to the language model. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Sonnet 4.5. confidence and model’s coherence evaluation. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

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Works this paper leans on

48 extracted references · 1 canonical work pages

  1. [1]

    In: Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025), Association for Computational Linguistics, Vienna, Austria

    Alvarez, A.A., Fincham, N.X.: Automated L2 proficiency scoring: Weak supervi- sion, large language models, and statistical guarantees. In: Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025), Association for Computational Linguistics, Vienna, Austria. pp. 384–397 (2025)

    2025

  2. [2]

    In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

    Chen, G., Chen, S., Liu, Z., Jiang, F., Wang, B.: Humans or LLMs as the judge? a study on judgement bias. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. pp. 8301–8327 (November 2024)

    2024

  3. [3]

    Proceedings of the National Academy of Sciences 122(25), e2412015122 (2025)

    Cheung, V., Maier, M., Lieder, F.: Large language models show amplified cognitive biases in moral decision-making. Proceedings of the National Academy of Sciences 122(25), e2412015122 (2025)

    2025

  4. [4]

    Educational and psycho- logical measurement20(1), 37–46 (1960)

    Cohen, J.: A coefficient of agreement for nominal scales. Educational and psycho- logical measurement20(1), 37–46 (1960)

    1960

  5. [5]

    Educational Philosophy and Theory21(1), 1–19 (1989)

    Davies, B.: Education for sexism: A theoretical analysis of the sex/gender bias in education. Educational Philosophy and Theory21(1), 1–19 (1989)

    1989

  6. [6]

    RSS: Data Science and Artificial Intelligence1(1), udaf002 (2025)

    Delacroix, S., Robinson, D., Bhatt, U., Domenicucci, J., Montgomery, J., Varo- quaux, G., Ek, C.H., Fortuin, V., He, Y., Diethe, T., et al.: Beyond quantification: Navigating uncertainty in professional ai systems. RSS: Data Science and Artificial Intelligence1(1), udaf002 (2025)

    2025

  7. [7]

    International journal of artificial intelligence in educa- tion35(2), 774–790 (2025)

    Estévez-Ayres, I., Callejo, P., Hombrados-Herrera, M.Á., Alario-Hoyos, C., Del- gado Kloos, C.: Evaluation of llm tools for feedback generation in a course on concurrent programming. International journal of artificial intelligence in educa- tion35(2), 774–790 (2025)

    2025

  8. [8]

    Nature630(8017), 625–630 (2024)

    Farquhar, S., Kossen, J., Kuhn, L., Gal, Y.: Detecting hallucinations in large lan- guage models using semantic entropy. Nature630(8017), 625–630 (2024)

    2024

Show all 48 references
  1. [9]

    In: Proceedings of the International CALL Research Conference

    Fincham, N.X., Alvarez, A.A.: Using large language models (llms) to facilitate l2 proficiency development through personalized feedback and scaffolding: An em- pirical study. In: Proceedings of the International CALL Research Conference. vol. 2024, pp. 59–64 (2024)

    2024

  2. [10]

    In: Social Cognition, pp

    Fiske, S.T.: Controlling other people: The impact of power on stereotyping. In: Social Cognition, pp. 101–115. Routledge, New York (2018) Accepted for AIED 2026 13

    2018

  3. [11]

    Computational Linguistics50(3), 1097–1179 (2024)

    Gallegos, I.O., Rossi, R.A., Barrow, J., Tanjim, M.M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., Ahmed, N.K.: Bias and fairness in large language models: A survey. Computational Linguistics50(3), 1097–1179 (2024)

    2024

  4. [12]

    AI magazine22(4), 39–39 (2001)

    Graesser, A.C., VanLehn, K., Rosé, C.P., Jordan, P.W., Harter, D.: Intelligent tutoring systems with conversational dialogue. AI magazine22(4), 39–39 (2001)

    2001

  5. [13]

    Nature633(8028), 147–154 (2024)

    Hofmann, V., Kalluri, P.R., Jurafsky, D., King, S.: AI generates covertly racist decisions about people based on their dialect. Nature633(8028), 147–154 (2024)

    2024

  6. [14]

    Science Insights Education Frontiers16(2), 2577–2587 (2023)

    Huang, L.: Ethics of artificial intelligence in education: Student privacy and data protection. Science Insights Education Frontiers16(2), 2577–2587 (2023)

    2023

  7. [15]

    ACM Transactions on Information Systems43(2), 1–55 (2025)

    Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language mod- els: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems43(2), 1–55 (2025)

    2025

  8. [16]

    Machine learning110(3), 457– 506 (2021)

    Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine learning110(3), 457– 506 (2021)

    2021

  9. [17]

    Learning and individual differences103, 102274 (2023)

    Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al.: Chatgpt for good? on opportunities and challenges of large language models for education. Learning and individual differences103, 102...

    2023

  10. [18]

    In: The Eleventh Interna- tional Conference on Learning Representations (2023)

    Kuhn, L., Gal, Y., Farquhar, S.: Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. In: The Eleventh Interna- tional Conference on Learning Representations (2023)

    2023

  11. [19]

    International journal of Educational Technology in Higher education20(1), 56 (2023)

    Labadze, L., Grigolia, M., Machaidze, L.: Role of AI chatbots in education: system- atic literature review. International journal of Educational Technology in Higher education20(1), 56 (2023)

    2023

  12. [20]

    British Journal of Educational Technology55(5), 1982–2002 (2024)

    Lee, J., Hicke, Y., Yu, R., Brooks, C., Kizilcec, R.F.: The life cycle of large lan- guage models in education: A framework for understanding sources of bias. British Journal of Educational Technology55(5), 1982–2002 (2024)

    1982

  13. [21]

    In: Proceedings of the 2025 CHI Conference on Human Factors in Com- puting Systems

    Li, J., Yang, Y., Liao, Q.V., Zhang, J., Lee, Y.C.: As confidence aligns: Under- standing the effect of ai confidence on human self-confidence in human-ai decision making. In: Proceedings of the 2025 CHI Conference on Human Factors in Com- puting Systems. pp. 1–16 (2025)

    2025

  14. [22]

    In: Proceedings of the 28th International Conference on Computational Linguistics

    Liu, H., Dacon, J., Fan, W., Liu, H., Liu, Z., Tang, J.: Does gender matter? towards fairness in dialogue systems. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 4403–4416 (2020)

    2020

  15. [23]

    In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

    Liu, S., Li, Z., Liu, X., Zhan, R., Wong, D.F., Chao, L.S., Zhang, M.: Can llms learn uncertainty on their own? expressing uncertainty effectively in a self-training manner. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. pp. 21635–2...

    2024

  16. [24]

    Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al.: Self-refine: Iterative refinement with self-feedback.AdvancesinNeuralInformationProcessingSystems36,46534–46594 (2023)

    2023

  17. [25]

    Maurya, K.K., Srivatsa, K.A., Petukhova, K., Kochmar, E.: Unifying AI tutor eval- uation: An evaluation taxonomy for pedagogical ability assessment of llm-powered AI tutors. In: Proceedings of the 2025 Conference of the Nations of the Ameri- cas Chapter of the Association for ...

    2025

  18. [26]

    In: Zong, C., Xia, F., Li, W., Navigli, R

    Nadeem, M., Bethke, A., Reddy, S.: StereoSet: Measuring stereotypical bias in pretrained language models. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Pro- ceedings of the 59th Annual Meeting of the Association for Computational Lin- guistics and the 11th International Jo...

    2021 doi

  19. [27]

    Software Impacts19, 100619 (2024)

    Nazir, A., Chakravarthy, T.K., Cecchini, D.A., Khajuria, R., Sharma, P., Mirik, A.T., Kocaman, V., Talby, D.: Langtest: A comprehensive evaluation library for custom llm and nlp models. Software Impacts19, 100619 (2024)

    2024

  20. [28]

    Advances in Neural Information Processing Systems37, 8901–8929 (2024)

    Nikitin, A., Kossen, J., Gal, Y., Marttinen, P.: Kernel language entropy: Fine- grained uncertainty quantification for llms from semantic similarities. Advances in Neural Information Processing Systems37, 8901–8929 (2024)

    2024

  21. [29]

    In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

    Park, M., Kim, S., Lee, S., Kwon, S., Kim, K.: Empowering personalized learning through a conversation-based tutoring system with student modeling. In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. pp. 1–10 (2024)

    2024

  22. [30]

    Harvard Data Science Review7(1) (2025)

    Pawitan, Y., Holmes, C.: Confidence in the reasoning of large language models. Harvard Data Science Review7(1) (2025)

    2025

  23. [31]

    Advances in Neural Information Processing Systems37, 21763–21813 (2024)

    Reuel, A., Hardy, A., Smith, C., Lamparth, M., Hardy, M., Kochenderfer, M.J.: Betterbench: Assessing AI benchmarks, uncovering issues, and establishing best practices. Advances in Neural Information Processing Systems37, 21763–21813 (2024)

    2024

  24. [32]

    AI magazine34(3), 42–54 (2013)

    Rus, V., D’Mello, S., Hu, X., Graesser, A.: Recent advances in conversational intelligent tutoring systems. AI magazine34(3), 42–54 (2013)

    2013

  25. [33]

    In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

    Sap,M.,Gabriel,S.,Qin,L.,Jurafsky,D.,Smith,N.A.,Choi,Y.:Socialbiasframes: Reasoning about social and power implications of language. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 5477– 5490 (July 2020)

    2020

  26. [34]

    In: International Conference on Artificial Intelligence in Education

    Schmucker, R., Xia, M., Azaria, A., Mitchell, T.: Ruffle&riley: Insights from design- ing and evaluating a large language model-based conversational tutoring system. In: International Conference on Artificial Intelligence in Education. pp. 75–90. Springer (2024)

    2024

  27. [35]

    In: Proceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing

    Taubenfeld, A., Dover, Y., Reichart, R., Goldstein, A.: Systematic biases in LLM simulations of debates. In: Proceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing. pp. 251–267 (November 2024)

    2024

  28. [36]

    Social Psychology of Education4(3), 235–258 (2001)

    Van Laar, C., Sidanius, J.: Social status and the academic achievement gap: A social dominance perspective. Social Psychology of Education4(3), 235–258 (2001)

    2001

  29. [37]

    Computers and Composition73, 102871 (2024)

    Van Poucke, M.: Chatgpt, the perfect virtual teaching assistant? ideological bias in learner-chatbot interactions. Computers and Composition73, 102871 (2024)

    2024

  30. [38]

    In: The Eleventh International Conference on Learning Representations (2023)

    Wang, X., Wei, J., Schuurmans, D., Le, Q.V., Chi, E.H., Narang, S., Chowdhery, A., Zhou, D.: Self-consistency improves chain of thought reasoning in language models. In: The Eleventh International Conference on Learning Representations (2023)

    2023

  31. [39]

    Educational Researcher45(9), 508–514 (2016)

    Warikoo, N., Sinclair, S., Fei, J., Jacoby-Senghor, D.: Examining racial bias in education: A new approach. Educational Researcher45(9), 508–514 (2016)

    2016

  32. [40]

    Journal of research on technology in education pp

    Warr, M., Oster, N.J., Isaac, R.: Implicit bias in large language models: Experi- mental proof and implications for education. Journal of research on technology in education pp. 1–24 (2024) Accepted for AIED 2026 15

    2024

  33. [41]

    In: Pro- ceedings of the International Conference on Information Systems (ICIS) (2021)

    Weber, F., Wambsganss, T., Rüttimann, D., Söllner, M.: Pedagogical agents for interactive learning: A taxonomy of conversational agents in education. In: Pro- ceedings of the International Conference on Information Systems (ICIS) (2021)

    2021

  34. [42]

    In: Findings of the Association for Computa- tional Linguistics: NAACL 2025

    Weissburg, I., Anand, S., Levy, S., Jeong, H.: Llms are biased teachers: Evaluating llm bias in personalized education. In: Findings of the Association for Computa- tional Linguistics: NAACL 2025. pp. 5650–5698 (2025)

    2025

  35. [43]

    In: The Twelfth International Conference on Learning Representations (2024)

    Xiong, M., Hu, Z., Lu, X., LI, Y., Fu, J., He, J., Hooi, B.: Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms. In: The Twelfth International Conference on Learning Representations (2024)

    2024

  36. [44]

    In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

    Xu, T., Wu, S., Diao, S., Liu, X., Wang, X., Chen, Y., Gao, J.: Sayself: Teaching llms to express confidence with self-reflective rationales. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. pp. 5985–5998 (2024)

    2024

  37. [45]

    In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

    Yang, Z., Zhang, Y., Wang, Y., Xu, Z., Lin, J., Sui, Z.: Confidence vs critique: A decomposition of self-correction capability for llms. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 3998–4014 (2025)

    2025

  38. [46]

    Zhou, J., Deng, J., Mi, F., Li, Y., Wang, Y., Huang, M., Jiang, X., Liu, Q., Meng, H.: Towards identifying social bias in dialog systems: Framework, dataset, and benchmark.In:FindingsoftheAssociationforComputationalLinguistics:EMNLP

  39. [47]

    3576–3591 (2022)

    pp. 3576–3591 (2022)

    2022

  40. [48]

    Journal of Instructional Psychology4(3), 2 (1977)

    Zucker, S.H., Prieto, A.G.: Ethnicity and teacher bias in educational decisions. Journal of Instructional Psychology4(3), 2 (1977)

    1977

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