HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs

by Tin Nguyen, Logan Bolton, Mohammad R. Taesiri, Trung Bui, and Anh Nguyen.

An Achilles heel of Large Language Models (LLMs) is their tendency to hallucinate non-factual statements. A response mixed of factual and non-factual statements poses a challenge for humans to verify and accurately base their decisions on. To combat this problem, we propose Highlighted Chain-of-Thought Prompting (HoT), a technique for prompting LLMs to generate responses with XML-tags that ground facts to those provided in the query. That is, given an input question, LLMs would first re-format the question to add XML tags highlighting key facts, and then, generate a response with highlights over the facts referenced from the input. Interestingly, in few-shot settings, HoT **outperforms** the vanilla chain of thoughts (CoT) on a wide range of 17 tasks from arithmetic, reading comprehension to logical reasoning.

1. Requirements

python=3.10.15
google-generativeai==0.8.3
openai==1.58.1

2. How to Run

Run the following command to execute the script:

python main.py --save_answer --llm_model "$llm_model" --dataset "$dataset" --answer_mode "$run_mode" --data_mode "$data_mode"

Parameters:

• --llm_model: Defines the LLM model to use. Choices include:
  • gemini-1.5-pro-002, gemini-1.5-flash-002,
  • gpt-4o-2024-08-06
  • llama_8b, llama_70b, llama_sambanova_405b
  • qwen25_coder_32b, qwq_32b, deepseek_r1
• --dataset: Specifies the dataset to evaluate, such as:
  • GSM8K, AQUA, DROP
• --answer_mode": Determines the answering strategy:
  • cot: Chain-of-Thought prompting
  • hot: Highlight Chain-of-Thought prompting
• --data_mode:
  • random: Runs the model on 200 randomly selected samples.
  • longest: Runs the model on 200 longest samples.
  • shortest: Runs the model on 200 shortest samples.
  • full: Runs the model on the whole dataset.

Example Usage

python main.py --save_answer --llm_model "gpt-4o-2024-08-06" --dataset "GSM8K" --answer_mode "cot" --data_mode random

3. How to evaluate the result

Run the following command to evaluate the results:

python evaluate.py --llm_model "$llm_model" --dataset "$dataset" --answer_mode "$answer_mode" --data_mode "$data_mode"

Example Usage

python evaluate.py --llm_model "gpt-4o-2024-08-06" --dataset "GSM8K" --answer_mode "cot" --data_mode longest

4. How to visualize the result

Run the following command to render the result on html pages:

python visualize.py --llm_model "$llm_model" --dataset "$dataset" --answer_mode "$answer_mode" --save_html

Example Usage

python visualize.py --llm_model "gpt-4o-2024-08-06" --dataset "GSM8K" --answer_mode "cot" --data_mode --save_html

5. License

MIT

Citation

If you use this for your research, please cite:

@article{nguyen2025hot,
  title={HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs},
  author={Nguyen, Tin and Bolton, Logan and Taesiri, Mohammad Reza and Nguyen, Anh Totti},
  journal={arXiv preprint arXiv:2503.02003},
  year={2025}
}