---
title: "CHAIRO — paper by Lu et al. (2026) — summary and ideas"
description: "LLM-based content moderation often fails when harm is implicit, such as discrimination expressed indirectly or through… 38 references."
authors: ["Haotian Lu", "Yuchen Mou", "Bingzhe Wu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1692/"
doi: "10.18653/v1/2026.acl-long.1692"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lu-2026-chairo-contextual-hierarchical-analogical-induction/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lu-2026-chairo-contextual-hierarchical-analogical-induction/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/) / CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs

# CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs

**Authors:** Haotian Lu, Yuchen Mou and [Bingzhe Wu](https://md.research.elamur.ai/free/en/authors/bingzhe-wu/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1692/) · [PDF](https://aclanthology.org/2026.acl-long.1692.pdf) · [DOI 10.18653/v1/2026.acl-long.1692](https://doi.org/10.18653/v1/2026.acl-long.1692) · [OpenAlex](https://openalex.org/W7166858113)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
LLM-based content moderation often fails when harm is implicit, such as discrimination expressed indirectly or through metaphor, or when moderation criteria are vague. Direct prompting and standard fine-tuning generalize poorly to such cases and offer little transparency. CHAIRO addresses this by having the model derive case-specific rules from similar, already-labeled examples. Training proceeds in three stages. First, neighbours for each training item are found with BGE-M3 embeddings and cosine similarity. The base LLM writes analogy-based reasoning chains, and a first SFT round teaches the model to produce relevant analogies on its own. Second, QwQ-32B acts as an auxiliary model that induces an explicit rule from each target item and its model-generated analogies. Outputs are filtered for label consistency and spot-checked by hand. Third, the rule, analogy and reasoning are combined into a tagged hierarchical chain and used for a second SFT round. The authors report clear gains over rule-injected SFT and static multi-stage RAG baselines on both in-distribution and out-of-distribution data. The approach matters because each verdict can be traced to an explicit rule and supporting cases.

## Key points
- The output format uses special tokens: `<RULE>`, `<ANALOGY>` and `<REASONING>` segments, followed by a Safe/Unsafe answer.
- The pipeline relies on two SFT rounds:
  - the first yields an analogy-generating model (ϕ_CoA);
  - the second produces the final CHAIRO model (ϕ*).
- Components: BGE-M3 is used for retrieval, and QwQ-32B serves as the auxiliary rule-induction model.
- In human evaluation, annotators preferred CHAIRO's induced rules over single-instance alternatives in 85% of cases. Rules were also tested for transfer to external models.
- Limitations: the method covers text only and is trained with supervised fine-tuning only (reinforcement learning is left open). If moderation norms change substantially over time or differ sharply between cultures, the model may need to be retrained.
- The supplied excerpt omits the experiments section, so dataset names, the base model and exact accuracy figures should be added from the full paper.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 333 works · 87,019 citations
- [National University of Singapore](https://md.research.elamur.ai/free/en/universities/national-university-of-singapore/) — University, Singapore, Singapore · 180 works · 34,304 citations
- [Shenzhen University](https://md.research.elamur.ai/free/en/universities/shenzhen-university/) — University, Shenzhen, China · 16 works · 95 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

🧭 Topics (2): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works · [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works

## Cite

Cite as: Haotian Lu, Yuchen Mou and Bingzhe Wu. 2026. CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{lu2026chairo,
  title = {{CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs}},
  author = {Haotian Lu, Yuchen Mou and Bingzhe Wu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1692},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1692}
}
```

- [← previous CentaurTA: A Self-Improving Human-Agents Collaboration Framework for Thematic Analysis](https://md.research.elamur.ai/free/en/papers/wang-2026-centaurta-self-improving-human-agents/)
- [next → Challenges in Japanese Euphemism Classification: An Analysis of Pretrained Japanese and Multilingual Models](https://md.research.elamur.ai/free/en/papers/takahashi-2026-challenges-japanese-euphemism-classification-analysis/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/)

## References

38 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and 1 others. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in neural information processing systems, 33:1877–1901.
2. Eshwar Chandrasekharan, Chaitrali Gandhi, Matthew Wortley Mustelier, and Eric Gilbert. 2019. Crossmod: A cross-community learning-based system to assist reddit moderators. Proceedings of the ACM on human-computer interaction, 3(CSCW):1–30. [find](https://scholar.google.com/scholar?q=Eshwar+Chandrasekharan%2C+Chaitrali+Gandhi%2C+Matthew+Wortley+Mustelier%2C+and+Eric+Gilbert%2E+2019%2E+Crossmod%3A+A+cross%2Dcommunity+learning%2Dbased+system+to+assist+reddit+moderators%2E+Proceedings+of+the+ACM+on+hu)
3. Jianfa Chen, Emily Shen, Trupti Bavalatti, Xiaowen Lin, Yongkai Wang, Shuming Hu, Harihar Subramanyam, Ksheeraj Sai Vepuri, Ming Jiang, Ji Qi, Li Chen, Nan Jiang, and Ankit Jain. 2024a. Class-rag: Real-time content moderation with retrieval augmented generation. Preprint, arXiv:2410.14881. — [arXiv:2410.14881](https://arxiv.org/abs/2410.14881) [find](https://scholar.google.com/scholar?q=Jianfa+Chen%2C+Emily+Shen%2C+Trupti+Bavalatti%2C+Xiaowen+Lin%2C+Yongkai+Wang%2C+Shuming+Hu%2C+Harihar+Subramanyam%2C+Ksheeraj+Sai+Vepuri%2C+Ming+Jiang%2C+Ji+Qi%2C+Li+Chen%2C+Nan+Jiang%2C+and+Ankit+Jain%2E+2024a%2E+Class%2Drag%3A+Rea)
4. Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024b. [Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation](https://md.research.elamur.ai/free/en/papers/chen-2024-m3-embedding-multi-linguality/). Preprint, arXiv:2402.03216.
5. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [Bert: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies,…
6. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. [Hierarchical neural story generation](https://md.research.elamur.ai/free/en/papers/fan-2018-hierarchical-neural-story-generation/). In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 889–898, Melbourne, Australia. Association for Computational Linguistics.
7. Mirko Franco, Ombretta Gaggi, and Claudio E Palazzi. 2023. Analyzing the use of large language models for content moderation with chatgpt examples. In Proceedings of the 3rd International Workshop on Open Challenges in Online Social Networks, pages 1–8. [find](https://scholar.google.com/scholar?q=Mirko+Franco%2C+Ombretta+Gaggi%2C+and+Claudio+E+Palazzi%2E+2023%2E+Analyzing+the+use+of+large+language+models+for+content+moderation+with+chatgpt+examples%2E+In+Proceedings+of+the+3rd+International+Workshop+on+)
8. Kristina Gligoric, Myra Cheng, Lucia Zheng, Esin Durmus, and Dan Jurafsky. 2024. NLP systems that can’t tell use from mention censor counterspeech, but teaching the distinction helps. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics… [find](https://scholar.google.com/scholar?q=Kristina+Gligoric%2C+Myra+Cheng%2C+Lucia+Zheng%2C+Esin+Durmus%2C+and+Dan+Jurafsky%2E+2024%2E+NLP+systems+that+can%E2%80%99t+tell+use+from+mention+censor+counterspeech%2C+but+teaching+the+distinction+helps%2E+In+Proceedings+o)
9. Zihao He, Jonathan May, and Kristina Lerman. 2024. Cpl-novid: Context-aware prompt-based learning for norm violation detection in online communities. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 569–582. [find](https://scholar.google.com/scholar?q=Zihao+He%2C+Jonathan+May%2C+and+Kristina+Lerman%2E+2024%2E+Cpl%2Dnovid%3A+Context%2Daware+prompt%2Dbased+learning+for+norm+violation+detection+in+online+communities%2E+In+Proceedings+of+the+International+AAAI+Conferenc)
10. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. [The curious case of neural text degeneration](https://md.research.elamur.ai/free/en/papers/holtzman-2019-curious-case-neural-text-degeneration/). Preprint, arXiv:1904.09751.
11. Tao Huang. 2025. Content moderation by llm: From accuracy to legitimacy. Artificial Intelligence Review, 58(10):1–32. [find](https://scholar.google.com/scholar?q=Tao+Huang%2E+2025%2E+Content+moderation+by+llm%3A+From+accuracy+to+legitimacy%2E+Artificial+Intelligence+Review%2C+58%2810%29%3A1%E2%80%9332%2E)
12. Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, and Madian Khabsa. 2023. [Llama guard: Llm-based input-output safeguard for human-ai conversations](https://md.research.elamur.ai/free/en/papers/inan-2023-llama-guard-llm-based-input/). CoRR, abs/2312.06674.
13. Prince Jha, Raghav Jain, Konika Mandal, Aman Chadha, Sriparna Saha, and Pushpak Bhattacharyya. 2024. MemeGuard: An LLM and VLM-based framework for advancing content moderation via meme intervention. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1:… [find](https://scholar.google.com/scholar?q=Prince+Jha%2C+Raghav+Jain%2C+Konika+Mandal%2C+Aman+Chadha%2C+Sriparna+Saha%2C+and+Pushpak+Bhattacharyya%2E+2024%2E+MemeGuard%3A+An+LLM+and+VLM%2Dbased+framework+for+advancing+content+moderation+via+meme+intervention%2E+I)
14. Aryan Keluskar, Amrita Bhattacharjee, and Huan Liu. 2024. Do llms understand ambiguity in text? a case study in open-world question answering. In 2024 IEEE International Conference on Big Data (Big-Data), pages 7485–7490. IEEE. [find](https://scholar.google.com/scholar?q=Aryan+Keluskar%2C+Amrita+Bhattacharjee%2C+and+Huan+Liu%2E+2024%2E+Do+llms+understand+ambiguity+in+text%3F+a+case+study+in+open%2Dworld+question+answering%2E+In+2024+IEEE+International+Conference+on+Big+Data+%28Big%2DDa)
15. Mohammed Abdul Khaliq, Paul Yu-Chun Chang, Mingyang Ma, Bernhard Pflugfelder, and Filip Miletić. 2024. RAGAR, your falsehood radar: RAG-augmented reasoning for political fact-checking using multimodal large language models. In Proceedings of the Seventh Fact Extraction and VERification Workshop (FE… [find](https://scholar.google.com/scholar?q=Mohammed+Abdul+Khaliq%2C+Paul+Yu%2DChun+Chang%2C+Mingyang+Ma%2C+Bernhard+Pflugfelder%2C+and+Filip+Miletic%CC%81%2E+2024%2E+RAGAR%2C+your+falsehood+radar%3A+RAG%2Daugmented+reasoning+for+political+fact%2Dchecking+using+multimoda)
16. Mahi Kolla, Siddharth Salunkhe, Eshwar Chandrasekharan, and Koustuv Saha. 2024. Llm-mod: Can large language models assist content moderation? In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pages 1–8. [find](https://scholar.google.com/scholar?q=Mahi+Kolla%2C+Siddharth+Salunkhe%2C+Eshwar+Chandrasekharan%2C+and+Koustuv+Saha%2E+2024%2E+Llm%2Dmod%3A+Can+large+language+models+assist+content+moderation%3F+In+Extended+Abstracts+of+the+CHI+Conference+on+Human+Facto)
17. Deepak Kumar, Yousef Anees AbuHashem, and Zakir Durumeric. 2024. Watch your language: Investigating content moderation with large language models. In Proceedings of the International AAAI Conference on Web and Social Media, volume 18, pages 865– 878. [find](https://scholar.google.com/scholar?q=Deepak+Kumar%2C+Yousef+Anees+AbuHashem%2C+and+Zakir+Durumeric%2E+2024%2E+Watch+your+language%3A+Investigating+content+moderation+with+large+language+models%2E+In+Proceedings+of+the+International+AAAI+Conference+o)
18. Yaqiong Li, Peng Zhang, Hansu Gu, Tun Lu, Siyuan Qiao, Yubo Shu, Yiyang Shao, and Ning Gu. 2025. Demod: A holistic tool with explainable detection and personalized modification for toxicity censorship. Proc. ACM Hum. Comput. Interact., 9(2):1–24. [find](https://scholar.google.com/scholar?q=Yaqiong+Li%2C+Peng+Zhang%2C+Hansu+Gu%2C+Tun+Lu%2C+Siyuan+Qiao%2C+Yubo+Shu%2C+Yiyang+Shao%2C+and+Ning+Gu%2E+2025%2E+Demod%3A+A+holistic+tool+with+explainable+detection+and+personalized+modification+for+toxicity+censorship)
19. Yue Liu, Hongcheng Gao, Shengfang Zhai, Jun Xia, Tianyi Wu, Zhiwei Xue, Yulin Chen, Kenji Kawaguchi, Jiaheng Zhang, and Bryan Hooi. 2025. Guardreasoner: Towards reasoning-based llm safeguards. Preprint, arXiv:2501.18492. — [arXiv:2501.18492](https://arxiv.org/abs/2501.18492) [find](https://scholar.google.com/scholar?q=Yue+Liu%2C+Hongcheng+Gao%2C+Shengfang+Zhai%2C+Jun+Xia%2C+Tianyi+Wu%2C+Zhiwei+Xue%2C+Yulin+Chen%2C+Kenji+Kawaguchi%2C+Jiaheng+Zhang%2C+and+Bryan+Hooi%2E+2025%2E+Guardreasoner%3A+Towards+reasoning%2Dbased+llm+safeguards%2E+Preprin)
20. Huan Ma, Changqing Zhang, Huazhu Fu, Peilin Zhao, and Bingzhe Wu. 2024. Adapting large language models for content moderation: Pitfalls in data engineering and supervised fine-tuning. Preprint, arXiv:2310.03400. — [arXiv:2310.03400](https://arxiv.org/abs/2310.03400) [find](https://scholar.google.com/scholar?q=Huan+Ma%2C+Changqing+Zhang%2C+Huazhu+Fu%2C+Peilin+Zhao%2C+and+Bingzhe+Wu%2E+2024%2E+Adapting+large+language+models+for+content+moderation%3A+Pitfalls+in+data+engineering+and+supervised+fine%2Dtuning%2E+Preprint%2C+arXiv%3A)
21. Sarah Masud, Sahajpreet Singh, Viktor Hangya, Alexander Fraser, and Tanmoy Chakraborty. 2024. Hate personified: Investigating the role of llms in content moderation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16,… [find](https://scholar.google.com/scholar?q=Sarah+Masud%2C+Sahajpreet+Singh%2C+Viktor+Hangya%2C+Alexander+Fraser%2C+and+Tanmoy+Chakraborty%2E+2024%2E+Hate+personified%3A+Investigating+the+role+of+llms+in+content+moderation%2E+In+Proceedings+of+the+2024+Confere)
22. Lingrui Mei, Shenghua Liu, Yiwei Wang, Baolong Bi, Ruibin Yuan, and Xueqi Cheng. 2024. [Hiddenguard: Fine-grained safe generation with specialized representation router](https://md.research.elamur.ai/free/en/papers/mei-2026-hiddenguard-fine-grained-safe-generation/). CoRR, abs/2410.02684.
23. Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, and Hao Wu. 2018. Mixed precision training. Preprint, arXiv:1710.03740. [find](https://scholar.google.com/scholar?q=Paulius+Micikevicius%2C+Sharan+Narang%2C+Jonah+Alben%2C+Gregory+Diamos%2C+Erich+Elsen%2C+David+Garcia%2C+Boris+Ginsburg%2C+Michael+Houston%2C+Oleksii+Kuchaiev%2C+Ganesh+Venkatesh%2C+and+Hao+Wu%2E+2018%2E+Mixed+precision+trai)
24. Huy Nghiem and Hal Daumé III. 2024. Hatecot: An explanation-enhanced dataset for generalizable offensive speech detection via large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024, pages 5938–5956. [find](https://scholar.google.com/scholar?q=Huy+Nghiem+and+Hal+Daum%C3%A9+III%2E+2024%2E+Hatecot%3A+An+explanation%2Denhanced+dataset+for+generalizable+offensive+speech+detection+via+large+language+models%2E+In+Findings+of+the+Association+for+Computational+Li)
25. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and 1 others. 2022. [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/). Advances in neural information processing systems, 35:27730–27744.
26. Konstantina Palla, José Luis Redondo García, Claudia Hauff, Francesco Fabbri, Andreas Damianou, Henrik Lindström, Daniel R. Taber, and Mounia Lalmas. 2025. Policy-as-prompt: Rethinking content moderation in the age of large language models. In Proceedings of the 2025 ACM Conference on Fairness, Acco… [find](https://scholar.google.com/scholar?q=Konstantina+Palla%2C+Jos%C3%A9+Luis+Redondo+Garc%C3%ADa%2C+Claudia+Hauff%2C+Francesco+Fabbri%2C+Andreas+Damianou%2C+Henrik+Lindstr%C3%B6m%2C+Daniel+R%2E+Taber%2C+and+Mounia+Lalmas%2E+2025%2E+Policy%2Das%2Dprompt%3A+Rethinking+content+moderat)
27. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, and 1 others. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Jeffrey+Wu%2C+Rewon+Child%2C+David+Luan%2C+Dario+Amodei%2C+Ilya+Sutskever%2C+and+1+others%2E+2019%2E+Language+models+are+unsupervised+multitask+learners%2E+OpenAI+blog%2C+1%288%29%3A9%2E)
28. 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](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/). Advances in Neural Information Processing Systems, 36:53728– 53741.
29. Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020. Zero: Memory optimizations toward training trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1– 16. IEEE. [find](https://scholar.google.com/scholar?q=Samyam+Rajbhandari%2C+Jeff+Rasley%2C+Olatunji+Ruwase%2C+and+Yuxiong+He%2E+2020%2E+Zero%3A+Memory+optimizations+toward+training+trillion+parameter+models%2E+In+SC20%3A+International+Conference+for+High+Performance+Com)
30. Traian Rebedea, Razvan Dinu, Makesh Narsimhan Sreedhar, Christopher Parisien, and Jonathan Cohen. 2023. Nemo guardrails: A toolkit for controllable and safe LLM applications with programmable rails. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023… [find](https://scholar.google.com/scholar?q=Traian+Rebedea%2C+Razvan+Dinu%2C+Makesh+Narsimhan+Sreedhar%2C+Christopher+Parisien%2C+and+Jonathan+Cohen%2E+2023%2E+Nemo+guardrails%3A+A+toolkit+for+controllable+and+safe+LLM+applications+with+programmable+rails%2E+I)
31. Paul Röttger, Bertie Vidgen, Dirk Hovy, and Janet Pierrehumbert. 2022. [Two contrasting data annotation paradigms for subjective NLP tasks](https://md.research.elamur.ai/free/en/papers/rottger-2022-two-contrasting-data-annotation-paradigms/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 175–190,…
32. Nishant Vishwamitra, Keyan Guo, Farhan Tajwar Romit, Isabelle Ondracek, Long Cheng, Ziming Zhao, and Hongxin Hu. 2024. [Moderating new waves of online hate with chain-of-thought reasoning in large language models](https://md.research.elamur.ai/free/en/papers/vishwamitra-2023-moderating-new-waves-online-hate/). In IEEE Symposium on Security and Privacy, SP 2024, San Francisco, CA, USA, May 19-23,…
33. Peiran Wang, Qiyu Li, Longxuan Yu, Ziyao Wang, Ang Li, and Haojian Jin. 2024. Moderator: Moderating text-to-image diffusion models through fine-grained context-based policies. In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, CCS 2024, Salt Lake City, UT, U… [find](https://scholar.google.com/scholar?q=Peiran+Wang%2C+Qiyu+Li%2C+Longxuan+Yu%2C+Ziyao+Wang%2C+Ang+Li%2C+and+Haojian+Jin%2E+2024%2E+Moderator%3A+Moderating+text%2Dto%2Dimage+diffusion+models+through+fine%2Dgrained+context%2Dbased+policies%2E+In+Proceedings+of+the+20)
34. Jialin Wu, Jiangyi Deng, Shengyuan Pang, Yanjiao Chen, Jiayang Xu, Xinfeng Li, and Wenyuan Xu. 2024. [Legilimens: Practical and unified content moderation for large language model services](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, CCS 2024, Salt Lak…
35. CoRR, Zhuowen Yuan, Zidi Xiong, Yi Zeng, Ning Yu, Ruoxi Jia, Dawn Song, and Bo Li. 2024. Rigorllm: resilient guardrails for large language models against undesired content. In Proceedings of the 41st International Conference on Machine Learning, pages 57953–57965. [find](https://scholar.google.com/scholar?q=CoRR%2C+Zhuowen+Yuan%2C+Zidi+Xiong%2C+Yi+Zeng%2C+Ning+Yu%2C+Ruoxi+Jia%2C+Dawn+Song%2C+and+Bo+Li%2E+2024%2E+Rigorllm%3A+resilient+guardrails+for+large+language+models+against+undesired+content%2E+In+Proceedings+of+the+41st+)
36. Wenjun Zeng, Yuchi Liu, Ryan Mullins, Ludovic Peran, Joe Fernandez, Hamza Harkous, Karthik Narasimhan, Drew Proud, Piyush Kumar, Bhaktipriya Radharapu, Olivia Sturman, and Oscar Wahltinez. 2024. Shieldgemma: Generative ai content moderation based on gemma. Preprint, arXiv:2407.21772. — [arXiv:2407.21772](https://arxiv.org/abs/2407.21772) [find](https://scholar.google.com/scholar?q=Wenjun+Zeng%2C+Yuchi+Liu%2C+Ryan+Mullins%2C+Ludovic+Peran%2C+Joe+Fernandez%2C+Hamza+Harkous%2C+Karthik+Narasimhan%2C+Drew+Proud%2C+Piyush+Kumar%2C+Bhaktipriya+Radharapu%2C+Olivia+Sturman%2C+and+Oscar+Wahltinez%2E+2024%2E+Shiel)
37. Xianyang Zhan, Agam Goyal, Yilun Chen, Eshwar Chandrasekharan, and Koustuv Saha. 2025. Slm-mod: Small language models surpass llms at content moderation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Tech… [find](https://scholar.google.com/scholar?q=Xianyang+Zhan%2C+Agam+Goyal%2C+Yilun+Chen%2C+Eshwar+Chandrasekharan%2C+and+Koustuv+Saha%2E+2025%2E+Slm%2Dmod%3A+Small+language+models+surpass+llms+at+content+moderation%2E+In+Proceedings+of+the+2025+Conference+of+the+N)
38. Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, and Zheyan Luo. 2024. [LlamaFactory: Unified efficient fine-tuning of 100+ language models](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 400–410, Bangkok, Thail…

*Updated 1 November 2026*