Property	Value
Stage 1	Multilingual pretraining — SemEval MWAHAHA
Stage 2	CLEF-JOKER Task 4 — English
Backbone	Qwen3-14B (QLoRA 4-bit)
LoRA r / alpha	16 / 16
Task	Dual-sense pun-brief generation

Usage

This is a PEFT LoRA adapter. Load the base model and apply the adapter:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, "Jayi2424/HumorGen_JOKER_EN_14B")

pun_word = "bark"
sense_1  = "the sound a dog makes"
sense_2  = "the outer covering of a tree"

prompt = (
    "<|im_start|>system\n"
    "You are an expert at writing puns. Given a pun word and two meanings, write a "
    "sentence that uses both senses naturally.\n<|im_end|>\n"
    "<|im_start|>user\n"
    f"Pun word: {pun_word}\nSense 1: {sense_1}\nSense 2: {sense_2}\n<|im_end|>\n"
    "<|im_start|>assistant\n"
)
inputs  = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=80, temperature=0.8, top_p=0.95)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation

bibtex

@misc{ajayi2026humorgen,
  title         = {HumorGen: Cognitive Synergy for Humor Generation in Large Language
                   Models via Persona-Based Distillation},
  author        = {Ajayi, Edward and others},
  year          = {2026},
  eprint        = {2604.09629},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2604.09629}
}

@inproceedings{ajayi2026joker,
  title     = {HumorGen at CLEF 2026 JOKER Task 4: Cross-Lingual Constrained
               Pun Generation via the Cognitive Synergy Framework},
  author    = {Ajayi, Edward and others},
  booktitle = {Working Notes of CLEF 2026},
  year      = {2026},
  url       = {https://edwardajayi.github.io/assets/papers/HumorGen-JOKER.pdf}
}