Enhancing Human-Like Responses in Large Language Models

📢 The paper associated with this model has been accepted to the AAAI-26 Workshop on Personalization in the Era of Large Foundation Models (PerFM).

🚀 Human-Like-Llama3-8B-Instruct

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct, specifically optimized to generate more human-like and conversational responses.

The fine-tuning process employed both Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO) to enhance natural language understanding, conversational coherence, and emotional intelligence in interactions.

The proccess of creating this models is detailed in the research paper “Enhancing Human-Like Responses in Large Language Models”.

🛠️ Training Configuration

Base Model: Llama3-8B-Instruct
Framework: Axolotl v0.4.1
Hardware: 2x NVIDIA A100 (80 GB) GPUs
Training Time: ~2 hours 20 minutes
Dataset: Synthetic dataset with ≈11,000 samples across 256 diverse topics

💬 Prompt Template

You can use Llama3 prompt template while using the model:

Llama3

<|start_header_id|>system<|end_header_id|>
{system}<|eot_id|>

<|start_header_id|>user<|end_header_id|>
{user}<|eot_id|>

<|start_header_id|>assistant<|end_header_id|>
{assistant}<|eot_id|>

This prompt template is available as a chat template, which means you can format messages using the tokenizer.apply_chat_template() method:

messages = [
 {"role": "system", "content": "You are helpful AI asistant."},
 {"role": "user", "content": "Hello!"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)

🤖 Models

Model	Download
Human-Like-Llama-3-8B-Instruct	🤗 HuggingFace
Human-Like-Qwen-2.5-7B-Instruct	🤗 HuggingFace
Human-Like-Mistral-Nemo-Instruct	🤗 HuggingFace

🔄 Quantizationed versions

GGUF @bartowski

🎯 Benchmark Results

Group	Model	Average	IFEval	BBH	MATH Lvl 5	GPQA	MuSR	MMLU-PRO
Llama Models	Human-Like-Llama-3-8B-Instruct	22.37	64.97	28.01	8.45	0.78	2.00	30.01
Llama-3-8B-Instruct	23.57	74.08	28.24	8.68	1.23	1.60	29.60
Difference (Human-Like)	-1.20	-9.11	-0.23	-0.23	-0.45	+0.40	+0.41
Qwen Models	Human-Like-Qwen-2.5-7B-Instruct	26.66	72.84	34.48	0.00	6.49	8.42	37.76
Qwen-2.5-7B-Instruct	26.86	75.85	34.89	0.00	5.48	8.45	36.52
Difference (Human-Like)	-0.20	-3.01	-0.41	0.00	+1.01	-0.03	+1.24
Mistral Models	Human-Like-Mistral-Nemo-Instruct	22.88	54.51	32.70	7.62	5.03	9.39	28.00
Mistral-Nemo-Instruct	23.53	63.80	29.68	5.89	5.37	8.48	27.97
Difference (Human-Like)	-0.65	-9.29	+3.02	+1.73	-0.34	+0.91	+0.03

📊 Dataset

The dataset used for fine-tuning was generated using LLaMA 3 models. The dataset includes 10,884 samples across 256 distinct topics such as technology, daily life, science, history, and arts. Each sample consists of:

Human-like responses: Natural, conversational answers mimicking human dialogue.
Formal responses: Structured and precise answers with a more formal tone.

The dataset has been open-sourced and is available at:

👉 Human-Like-DPO-Dataset

More details on the dataset creation process can be found in the accompanying research paper.

📝 Citation

@misc{çalık2025enhancinghumanlikeresponseslarge,
 title={Enhancing Human-Like Responses in Large Language Models}, 
 author={Ethem Yağız Çalık and Talha Rüzgar Akkuş},
 year={2025},
 eprint={2501.05032},
 archivePrefix={arXiv},
 primaryClass={cs.CL},
 url={https://arxiv.org/abs/2501.05032}, 
}