@inproceedings{pinninti-etal-2026-corrective,
title = "Corrective Feedback-Driven Humor Generation for Tiny Language Models",
author = {Pinninti, Ramakrishna and
Bandyopadhyay, Dibyanayan and
Routhu, Lalit Chandra and
Dias, Ga{\"e}l and
Lechervy, Alexis and
Pantin, Jeremie and
Hasanuzzaman, Mohammed and
Ekbal, Asif},
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.132/",
pages = "2898--2916",
ISBN = "979-8-89176-439-2",
abstract = "Humor generation remains a challenging problem for language models due to its subjective, creative, and culturally sensitive nature. While large language models (LLMs) demonstrate strong generative capabilities, their deployment is often constrained by computational cost. We propose a lightweight, corrective feedback-driven framework in which a compact generator (GPT-2, 140M parameters) produces punchlines under the guidance of an LLM acting as a critic. Unlike traditional knowledge distillation, our method leverages the LLM as a feedback provider rather than a teacher to be mimicked, enabling the student model to iteratively refine its outputs through corrective supervision. We systematically evaluate multiple LLMs (LLaMA3-8B, Qwen3-8B and Mistral-7B) as guides under different strategies, including frozen and learnable configurations, to study their impact on humor quality and diversity. To support responsible humor generation, we curate a high-quality setup{--}punchline dataset from Reddit and explicitly filter out harmful content. Experimental results show that corrective feedback-guided training substantially improves humor quality, fluency, and diversity over standard fine-tuning and knowledge distillation baselines, while preserving the efficiency of compact models. We observe that, among the evaluated guides, Mistral-7B consistently provides particularly strong guidance on the curated joke dataset. Our code and resources are available at: https://github.com/Ramakrishna176420/Corrective-Feedback-Driven-Humor-Generation-for-Tiny-Language-Models."
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<abstract>Humor generation remains a challenging problem for language models due to its subjective, creative, and culturally sensitive nature. While large language models (LLMs) demonstrate strong generative capabilities, their deployment is often constrained by computational cost. We propose a lightweight, corrective feedback-driven framework in which a compact generator (GPT-2, 140M parameters) produces punchlines under the guidance of an LLM acting as a critic. Unlike traditional knowledge distillation, our method leverages the LLM as a feedback provider rather than a teacher to be mimicked, enabling the student model to iteratively refine its outputs through corrective supervision. We systematically evaluate multiple LLMs (LLaMA3-8B, Qwen3-8B and Mistral-7B) as guides under different strategies, including frozen and learnable configurations, to study their impact on humor quality and diversity. To support responsible humor generation, we curate a high-quality setup–punchline dataset from Reddit and explicitly filter out harmful content. Experimental results show that corrective feedback-guided training substantially improves humor quality, fluency, and diversity over standard fine-tuning and knowledge distillation baselines, while preserving the efficiency of compact models. We observe that, among the evaluated guides, Mistral-7B consistently provides particularly strong guidance on the curated joke dataset. Our code and resources are available at: https://github.com/Ramakrishna176420/Corrective-Feedback-Driven-Humor-Generation-for-Tiny-Language-Models.</abstract>
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%0 Conference Proceedings
%T Corrective Feedback-Driven Humor Generation for Tiny Language Models
%A Pinninti, Ramakrishna
%A Bandyopadhyay, Dibyanayan
%A Routhu, Lalit Chandra
%A Dias, Gaël
%A Lechervy, Alexis
%A Pantin, Jeremie
%A Hasanuzzaman, Mohammed
%A Ekbal, Asif
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F pinninti-etal-2026-corrective
%X Humor generation remains a challenging problem for language models due to its subjective, creative, and culturally sensitive nature. While large language models (LLMs) demonstrate strong generative capabilities, their deployment is often constrained by computational cost. We propose a lightweight, corrective feedback-driven framework in which a compact generator (GPT-2, 140M parameters) produces punchlines under the guidance of an LLM acting as a critic. Unlike traditional knowledge distillation, our method leverages the LLM as a feedback provider rather than a teacher to be mimicked, enabling the student model to iteratively refine its outputs through corrective supervision. We systematically evaluate multiple LLMs (LLaMA3-8B, Qwen3-8B and Mistral-7B) as guides under different strategies, including frozen and learnable configurations, to study their impact on humor quality and diversity. To support responsible humor generation, we curate a high-quality setup–punchline dataset from Reddit and explicitly filter out harmful content. Experimental results show that corrective feedback-guided training substantially improves humor quality, fluency, and diversity over standard fine-tuning and knowledge distillation baselines, while preserving the efficiency of compact models. We observe that, among the evaluated guides, Mistral-7B consistently provides particularly strong guidance on the curated joke dataset. Our code and resources are available at: https://github.com/Ramakrishna176420/Corrective-Feedback-Driven-Humor-Generation-for-Tiny-Language-Models.
%U https://aclanthology.org/2026.aacl-long.132/
%P 2898-2916
Markdown (Informal)
[Corrective Feedback-Driven Humor Generation for Tiny Language Models](https://aclanthology.org/2026.aacl-long.132/) (Pinninti et al., AACL-IJCNLP 2026)
ACL
- Ramakrishna Pinninti, Dibyanayan Bandyopadhyay, Lalit Chandra Routhu, Gaël Dias, Alexis Lechervy, Jeremie Pantin, Mohammed Hasanuzzaman, and Asif Ekbal. 2026. Corrective Feedback-Driven Humor Generation for Tiny Language Models. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2898–2916, Hengqin, China. Association for Computational Linguistics.