From Automation to Collaboration: Human-in-the-Loop Methods for Safe and Trustworthy NLP

Md. Rakibul Islam; MD. Tanvir Ahmed Seum; Most. Sharmin Sultana Samu

From Automation to Collaboration: Human-in-the-Loop Methods for Safe and Trustworthy NLP

T0 review · 0 major / 2 minor · reviewed 2026-06-30 · grok-4.3

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

Pith's one-line read Human-in-the-loop methods shift NLP from full automation to collaboration to reduce bias, hallucination and adversarial risks.

desk verdict This is a straightforward survey that organizes existing human-in-the-loop work for trustworthy NLP and flags some gaps, without adding new methods or results. read the letter →

arxiv 2605.25226 v1 pith:L2NSGSP7 submitted 2026-05-24 cs.CL

Most. Sharmin Sultana Samu , MD. Tanvir Ahmed Seum , Md. Rakibul Islam This is my paper · ORCID

classification cs.CL

keywords human-in-the-loopNLPsafetytrustworthyAIlargelanguagemodelsadversarialrobustnessmodelauditingcollaborationbiasmitigation

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 survey reviews recent techniques in which human experts audit model probes, verify robustness through adversarial examples, construct domain-specific data and steer large language models away from unsafe outputs. It shows that while automation enables scale, high-stakes tasks still require human oversight to catch inconsistencies, especially in low-resource languages and private databases. The authors map current practices across auditing, evaluation, data work and steering, then flag gaps in scalable probing and sustainable benchmarks. They close by sketching practical directions for adaptive auditing and accountable deployment.

What carries the argument

Human-in-the-loop methods that integrate expert input into auditing, robustness evaluation, data construction and model steering.

What would settle it

A controlled comparison showing that fully automated pipelines achieve equal or lower rates of bias, hallucination and adversarial failures than human-in-the-loop versions on the same high-stakes tasks would undermine the central premise.

Watch

Extended reading notes

Core claim

Human supervision remains essential for probe validation, adversarial verification and domain-specific annotation; recent human-in-the-loop methods therefore move NLP systems from pure automation toward sustained collaboration that supports safety and trustworthiness.

Load-bearing premise

Human supervision is essential for probe validation, adversarial verification and domain-specific annotation, but it is costly and hard to scale.

Editorial extensions

If this is right

Probe-based auditing becomes more reliable when humans validate detected inconsistencies.
Adversarial text generation uncovers robustness gaps more effectively when humans verify outputs, particularly in lower-resourced languages.
Enterprise text-to-SQL systems gain validation capacity over private databases through human oversight.
Gaps remain in scalable probing methods and sustainable robustness benchmarks.
Future work should target adaptive auditing, collaborative evaluation frameworks and governance for private systems.

Reading between the lines

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

Cost-reduction techniques for human feedback could expand safe deployment beyond current high-resource settings.
Standardized protocols for human-AI handoff might allow consistent governance across different NLP applications.
Low-resource language benchmarks that incorporate human verification loops could close existing robustness disparities.
Accountability mechanisms tied to human steering decisions may influence regulatory requirements for deployed models.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The main thing here is a literature review of human-in-the-loop techniques aimed at auditing, robustness testing, data labeling, and steering NLP models toward safer behavior. It covers probe validation, adversarial examples, low-resource language issues, and enterprise cases like text-to-SQL over private databases. The paper does a reasonable job laying out how human input helps with these tasks and then points to gaps in scalable probing, sustainable benchmarks, low-resource settings, and governance for private systems.

What it does well is collect scattered threads from recent work into one place and turn them into a set of practical research directions like adaptive auditing and collaborative evaluation. That synthesis can save time for someone entering the area.

The soft spots are the usual ones for a survey: no new technique, no fresh data, and no formal analysis. The motivation that human supervision is essential yet hard to scale is presented as background rather than something the paper tests or quantifies. Without seeing the full reference list or how the authors selected papers, it's difficult to judge whether the coverage is complete or if important threads were missed. The claims rest entirely on the reviewed literature, so any gaps in that literature carry through.

This paper is mainly for readers who want a quick map of current human-in-the-loop efforts in safety and trustworthiness rather than a source of new mechanisms. It could be useful in a reading group if the group is focused on trustworthy AI, but it is not the kind of work that changes how people think about the problem.

I would send it to peer review if the full manuscript shows thorough coverage and clear organization; a solid survey on an active topic can still be worth referee time even if it is not novel.

Referee Report

0 major / 2 minor

Summary. The manuscript is a survey examining recent human-in-the-loop methods in NLP that shift from full automation to collaboration in order to address safety and trustworthiness concerns such as bias, hallucination, adversarial vulnerability, and unreliable generalization. It reviews the role of human expertise in probe-based auditing, adversarial verification, domain-specific annotation, robustness evaluation, data construction, and model steering; identifies gaps in scalable probing, sustainable benchmarks (especially for low-resource languages), and governance of private systems; and outlines practical research directions for adaptive auditing, collaborative evaluation, and accountable deployment.

Significance. As a descriptive review that organizes existing work on human-AI collaboration for trustworthy NLP and flags concrete open problems in low-resource settings and private-system governance, the paper could provide a useful reference point for the field if the coverage of the literature is representative.

minor comments (2)

[Abstract] Abstract: the phrase 'our findings highlight gaps' is slightly misleading for a survey; consider rephrasing to 'the review identifies gaps' to clarify that no new empirical findings are presented.
The manuscript would benefit from an explicit statement of the search strategy, time window, or inclusion criteria used to select the reviewed papers, as is conventional for survey articles.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the constructive summary of our survey and for recommending minor revision. No specific major comments were provided in the report.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; descriptive survey with no derivations

full rationale

This is a survey paper that reviews existing human-in-the-loop methods for NLP auditing, robustness, data construction, and steering. It advances no new equations, predictions, fitted parameters, theorems, or quantitative claims. The statement that human supervision is essential yet costly is presented as background motivation drawn from prior literature, not as a tested proposition derived within the paper. No self-citation chains, ansatzes, or renamings reduce any central claim to its own inputs. The paper is self-contained as a descriptive review against external benchmarks.

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

The survey rests on domain assumptions about model risks and the necessity of human oversight; no free parameters, invented entities, or ad-hoc axioms are introduced.

assumptions (2)

domain assumption Large language models are widely deployed in high-stakes NLP tasks, yet risks such as bias, hallucination, adversarial vulnerability and unreliable generalization remain.
Stated directly in the opening of the abstract as the motivation.
domain assumption Human supervision is essential for probe validation, adversarial verification and domain-specific annotation.
Explicit premise in the abstract that underpins the need for the reviewed methods.

how reviews work

0 comments

Cite this review

Pith. "Pith review of From Automation to Collaboration: Human-in-the-Loop Methods for Safe and Trustworthy NLP." pith.science (2026). https://pith.science/paper/L2NSGSP7

@misc{pith2026260525226,
  author       = {Pith},
  title        = {Pith review of: From Automation to Collaboration: Human-in-the-Loop Methods for Safe and Trustworthy NLP},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L2NSGSP7}},
  note         = {Machine review of arXiv:2605.25226}
}

read the original abstract

Large language models are widely deployed in high-stakes NLP tasks, yet risks such as bias, hallucination, adversarial vulnerability and unreliable generalization remain. Probe-based auditing reveals inconsistencies in model behavior. Adversarial text generation uncovers robustness gaps, especially in lower-resourced languages with limited benchmarks. Enterprise text-to-SQL settings expose the difficulty of validating outputs over private and large-scale databases. Human supervision is essential for probe validation, adversarial verification and domain-specific annotation, but it is costly and hard to scale. This survey examines recent human-in-the-loop methods that shift NLP from automation toward collaboration for safety and trustworthiness. We review how human expertise supports auditing, robustness evaluation, data construction and model steering. Our findings highlight gaps in scalable probing, sustainable robustness benchmarks, low-resource settings and governance of private systems. We outline practical research directions for adaptive auditing, collaborative evaluation and accountable deployment.

Figures

Figures reproduced from arXiv: 2605.25226 by the authors.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

47 extracted references · 47 canonical work pages

[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...

work page
[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...

work page
[3]

Anum Afzal, Alexander Kowsik, Rajna Fani, and Florian Matthes. 2024. Towards optimizing and evaluating a retrieval augmented qa chatbot using llms with human-in-the-loop. In Proceedings of the fifth workshop on data science with human-in-the-loop (DaSH 2024), pages 4--16

work page 2024
[4]

Maryam Amirizaniani, Adrian Lavergne, Elizabeth Snell Okada, Aman Chadha, Tanya Roosta, and Chirag Shah. 2025. Developing a framework for auditing large language models using human-in-the-loop. In Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, pages 64--74

work page 2025
[5]

Maryam Amirizaniani, Jihan Yao, Adrian Lavergne, Elizabeth Snell Okada, Aman Chadha, Tanya Roosta, and Chirag Shah. 2024. Llmauditor: A framework for auditing large language models using human-in-the-loop. arXiv preprint arXiv:2402.09346

work page arXiv 2024
[6]

Sultan Asiri, Yang Xiao, and Saleh Alzahrani. 2024. Towards improving phishing detection system using human in the loop deep learning model. In Proceedings of the 2024 ACM southeast conference, pages 77--85

work page 2024
[7]

Oleg Bakhteev, Luis Salamanca, Laurence Brandenberger, and Sophia Schlosser. 2025. embed2discover: The nlp tool for human-in-the-loop, dictionary-based content analysis. In Proceedings of the 10th edition of the Swiss Text Analytics Conference, pages 120--132

work page 2025
[8]

Filippos Bellos, Yayuan Li, Cary Shu, Ruey Day, Jeffrey Siskind, and Jason Corso. 2025. Towards effective human-in-the-loop assistive ai agents. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2513--2522

work page 2025

Show all 47 references

[9]

Alba Bonet-Jover, Robiert Sep \'u lveda-Torres, Estela Saquete, and Patricio Mart \' nez-Barco. 2023 a . A semi-automatic annotation methodology that combines summarization and human-in-the-loop to create disinformation detection resources. Knowledge-Based Systems, 275:110723

2023
[10]

Alba Bonet-Jover, Robiert Sep \'u lveda-Torres, Estela Saquete, Patricio Mart \' nez-Barco, Alejandro Piad-Morffis, and Suilan Estevez-Velarde. 2023 b . Applying human-in-the-loop to construct a dataset for determining content reliability to combat fake news. Engineering Appli...

2023
[11]

Xi Cao, Yuan Sun, Jiajun Li, Quzong Gesang, Nuo Qun, and Nyima Tashi. 2025. Human-in-the-loop generation of adversarial texts: A case study on tibetan script. In Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the...

2025
[12]

Benjamin J Carvell, Marc Thomas, Andrew Pace, Christopher Dorney, George De Ath, Richard Everson, Nick Pepper, Adam Keane, Samuel Tomlinson, and Richard Cannon. 2026. Human-in-the-loop testing of ai agents for air traffic control with a regulated assessment framework. In AIAA ...

2026
[13]

Kuang-Ming Chen, Jenq-Neng Hwang, and Hung-yi Lee. 2025. Instructioncp: A simple yet effective approach for transferring large language models to target languages. In Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pages 1--6

2025
[14]

Tek Raj Chhetri, Yibei Chen, Puja Trivedi, Dorota Jarecka, Saif Haobsh, Patrick Ray, Lydia Ng, and Satrajit S Ghosh. 2025. Structsense: A task-agnostic agentic framework for structured information extraction with human-in-the-loop evaluation and benchmarking. arXiv preprint ar...

2025 arXiv
[15]

Yucheng Chu, Hang Li, Kaiqi Yang, Yasemin Copur-Gencturk, and Jiliang Tang. 2025. Llm-based automated grading with human-in-the-loop. In 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pages 1--8. IEEE

2025
[16]

Gabriel Chua, Leanne Tan, Ziyu Ge, and Roy Ka-Wei Lee. 2025. Lost in localization: Building rabakbench with human-in-the-loop validation to expose multilingual safety gaps. In Second Workshop on Language Models for Underserved Communities (LM4UC)

2025
[17]

Clayton Cohn, Caitlin Snyder, Justin Montenegro, and Gautam Biswas. 2024. Towards a human-in-the-loop llm approach to collaborative discourse analysis. In International Conference on Artificial Intelligence in Education, pages 11--19. Springer

2024
[18]

Marco De Santis and Christian Esposito. 2024. Human-in-the-loop for trustworthiness of federated learning. In Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pages 91--98

2024
[19]

Ziquan Deng, Xiwei Xuan, Kwan-Liu Ma, and Zhaodan Kong. 2024. A reliable framework for human-in-the-loop anomaly detection in time series. ACM Transactions on Interactive Intelligent Systems

2024
[20]

Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazare, and Jason Weston. 2019. Learning from dialogue after deployment: Feed yourself, chatbot! In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3667--3684

2019
[21]

Yuto Harada and Yohei Oseki. 2025. Cognitive feedback: Decoding human feedback from cognitive signals. In Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+ NLP), pages 209--219

2025
[22]

Yufei He, Ruoyu Li, Alex Chen, Yue Liu, Yulin Chen, Yuan Sui, Cheng Chen, Yi Zhu, Luca Luo, Frank Yang, and 1 others. 2025. Enabling self-improving agents to learn at test time with human-in-the-loop guidance. In Proceedings of the 2025 Conference on Empirical Methods in Natur...

2025
[23]

Mengze Hong, Wailing Ng, Chen Jason Zhang, Yuanfeng Song, and Di Jiang. 2025. Dial-in llm: Human-aligned llm-in-the-loop intent clustering for customer service dialogues. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 5896--5911

2025
[24]

Lena John, Ahmed Malek Ghanmi, Tim Wittenborg, S \""o ren Auer, and Oliver Karras. 2026. Extractable: Human-in-the-loop transformation of scientific corpora into structured knowledge. In International Conference on Theory and Practice of Digital Libraries, pages 470--487. Springer

2026
[25]

Hong Jin Kang, Muhammad Ali Gulzar, Nanyun Peng, Miryung Kim, and 1 others. 2024. Human-in-the-loop synthetic text data inspection with provenance tracking. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3118--3129

2024
[26]

Julia Kreutzer, Stefan Riezler, and Carolin Lawrence. 2021. Offline reinforcement learning from human feedback in real-world sequence-to-sequence tasks. In Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021), pages 37--43

2021
[27]

Jiahui Li and Roman Klinger. 2025. iprop: Interactive prompt optimization for large language models with a human in the loop. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 276--285

2025
[28]

Jiwei Li, Alexander H Miller, Sumit Chopra, Marc'Aurelio Ranzato, and Jason Weston. 2016. Dialogue learning with human-in-the-loop. arXiv preprint arXiv:1611.09823

2016 arXiv
[29]

Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, and 1 others. 2025. Storm-born: A challenging mathematical derivations dataset curated via a human-in-the-loop multi-agent framework. In Findings of the Associat...

2025
[30]

Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, and Xuan-Jing Huang. 2024. Aligning large language models with human preferences through representation engineering. In Proceedings of the 62nd Annual Meeting...

2024
[31]

Bel \'e n Mart \' n-Urcelay, Yoonsang Lee, Matthieu R Bloch, and Christopher J Rozell. 2026. Beyond labels: Information-efficient human-in-the-loop learning using ranking and selection queries. arXiv preprint arXiv:2602.15738

2026
[32]

Ethan Mendes, Yang Chen, Wei Xu, and Alan Ritter. 2023. Human-in-the-loop evaluation for early misinformation detection: A case study of covid-19 treatments. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pag...

2023
[33]

Nick Pangakis and Sam Wolken. 2025. Keeping humans in the loop: human-centered automated annotation with generative ai. In Proceedings of the International AAAI Conference on Web and Social Media, volume 19, pages 1471--1492

2025
[34]

Larissa Pusch, Alexandre Courtiol, and Tim Conrad. 2026. A human-in-the-loop, llm-centered architecture for knowledge-graph question answering. arXiv preprint arXiv:2602.05512

2026
[35]

Sameer Sadruddin, Jennifer D’Souza, Eleni Poupaki, Alex Watkins, Hamed Babaei Giglou, Anisa Rula, Bora Karasulu, S \""o ren Auer, Adrie Mackus, and Erwin Kessels. 2025. Llms4schemadiscovery: A human-in-the-loop workflow for scientific schema mining with large language models. ...

2025
[36]

Ali Riahi Samani, Tianhao Wang, Kangshuo Li, and Feng Chen. 2025. Large language models with reinforcement learning from human feedback approach for enhancing explainable sexism detection. In Proceedings of the 31st International Conference on Computational Linguistics, pages ...

2025
[37]

Hope Schroeder, Deb Roy, and Jad Kabbara. 2025 a . Just put a human in the loop? investigating llm-assisted annotation for subjective tasks. In Findings of the Association for Computational Linguistics: ACL 2025, pages 25771--25795

2025
[38]

Noah L Schroeder, Chris Davis Jaldi, and Shan Zhang. 2025 b . Large language models with human-in-the-loop validation for systematic review data extraction. arXiv preprint arXiv:2501.11840

2025
[39]

Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020. Learning to summarize with human feedback. Advances in neural information processing systems, 33:3008--3021

2020
[40]

Anton F Thielmann, Christoph Weisser, and Benjamin S \""a fken. 2024. Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Langu...

2024
[41]

Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. 2019. Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering. Transactions of the Association for Computational Linguistics, 7:387--401

2019
[42]

Jiongxiao Wang, Junlin Wu, Muhao Chen, Yevgeniy Vorobeychik, and Chaowei Xiao. 2024. Rlhfpoison: Reward poisoning attack for reinforcement learning with human feedback in large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Ling...

2024
[43]

Verena Weber, Enrico Piovano, and Melanie Bradford. 2021. It is better to verify: Semi-supervised learning with a human in the loop for large-scale nlu models. In Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances, pages 8--15

2021
[44]

Fabian Wenz, Omar Bouattour, Devin Yang, Justin Choi, Cecil Gregg, Nesime Tatbul, and C a g atay Demiralp. 2025. Benchpress: A human-in-the-loop annotation system for rapid text-to-sql benchmark curation. arXiv preprint arXiv:2510.13853

2025
[45]

Jing Yang, Didier Vega-Oliveros, Tais Seibt, and Anderson Rocha. 2021. Scalable fact-checking with human-in-the-loop. In 2021 IEEE International Workshop on Information Forensics and Security (WIFS), pages 1--6. IEEE

2021
[46]

Xianlong Zeng, Yijing Gao, Fanghao Song, and Ang Liu. 2024. Similar data points identification with llm: A human-in-the-loop strategy using summarization and hidden state insights. arXiv preprint arXiv:2404.04281

2024
[47]

Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Leon Zhou, Hainiu Xu, Li Zhang, Lara J Martin, Rotem Dror, Sha Li, and 1 others. 2023. Human-in-the-loop schema induction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: S...

2023

Pith tools

Reviewed June 30, 2026 · model on record in the stance chip above.

[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...

work page

[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...

work page

[3] [3]

Anum Afzal, Alexander Kowsik, Rajna Fani, and Florian Matthes. 2024. Towards optimizing and evaluating a retrieval augmented qa chatbot using llms with human-in-the-loop. In Proceedings of the fifth workshop on data science with human-in-the-loop (DaSH 2024), pages 4--16

work page 2024

[4] [4]

Maryam Amirizaniani, Adrian Lavergne, Elizabeth Snell Okada, Aman Chadha, Tanya Roosta, and Chirag Shah. 2025. Developing a framework for auditing large language models using human-in-the-loop. In Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, pages 64--74

work page 2025

[5] [5]

Maryam Amirizaniani, Jihan Yao, Adrian Lavergne, Elizabeth Snell Okada, Aman Chadha, Tanya Roosta, and Chirag Shah. 2024. Llmauditor: A framework for auditing large language models using human-in-the-loop. arXiv preprint arXiv:2402.09346

work page arXiv 2024

[6] [6]

Sultan Asiri, Yang Xiao, and Saleh Alzahrani. 2024. Towards improving phishing detection system using human in the loop deep learning model. In Proceedings of the 2024 ACM southeast conference, pages 77--85

work page 2024

[7] [7]

Oleg Bakhteev, Luis Salamanca, Laurence Brandenberger, and Sophia Schlosser. 2025. embed2discover: The nlp tool for human-in-the-loop, dictionary-based content analysis. In Proceedings of the 10th edition of the Swiss Text Analytics Conference, pages 120--132

work page 2025

[8] [8]

Filippos Bellos, Yayuan Li, Cary Shu, Ruey Day, Jeffrey Siskind, and Jason Corso. 2025. Towards effective human-in-the-loop assistive ai agents. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2513--2522

work page 2025