Paper: Metric Learning for Detection of Large Language Model Generated Texts (ICLR-2024)

Reviewer comment (Novelty):
This approach still relies heavily on the LLM themselves.

Author response:
We utilize the LLMs themselves as a method to reduce computational costs from the detectors. Having references from the generative LLMs, the detectors can focus more on learning a good metric while still maintaining reasonable complexity.

Reviewer comment (Experiments):
The scope of the study is rather small, focusing only on short responses.

Author response:
We are expanding this works to longer text data.

Reviewer comment (Experiments):
Could you elaborate on the different types/variations of prompts?

Author response:
The prompts are mainly questions in the forms what/who/when/where/which/how.

Reviewer comment (Theory):
The proposed approach requires knowing the context that may have been given to an LLM to generate the text. This seems like too big of a restriction, as it is often the case that we don’t know how the LLM was prompted.

Author response:
We are developing another model to reconstruct prompts from arbitrary texts. These models can be as simple as a finetune LLM to pose a question that capture the topic of a text.

Reviewer comment (Experiments):
The only LLM considered is ChatGPT. It would’ve been interesting to include a broader set of LLMs and explore the robustness of the approach to unseen LLM. Suggested Models: Llamav1, Llamav2, OPT, GPT-2, GPT-4, Cohere, Dollyv2, etc.

Author response:
At the time of model training and evaluation of models in this paper, GPT-3.5-TURBO/ChatGPT was the most popular and advance. We will incorporate the newer models into this work.

Reviewer comment (Experiments):
In the “same corpus with paraphraser” experiments the model was trained on the paraphrased LLM detections. This means that all the testing data is still in-domain, and hence it may defeat the purpose of the experiment.

Author response:
While the models are trained on paraphrased texts, the question-answer data is not the same between training and testing. Furthermore, the test data only comprises of paraphrased texts. Therefore, we believe this experiment makes sense as-is. Regardless, we will follow your suggestion.

Reviewer comment (Experiments):
Use a different paraphraser on the testing data.

Author response:
While the models are trained on paraphrased texts, the question-answer data is not the same between training and testing. Furthermore, the test data only comprises of paraphrased texts. Therefore, we believe this experiment makes sense as-is. Regardless, we will follow your suggestion.

Reviewer comment (Experiments):
Do not train on the paraphrased text.

Author response:
While the models are trained on paraphrased texts, the question-answer data is not the same between training and testing. Furthermore, the test data only comprises of paraphrased texts. Therefore, we believe this experiment makes sense as-is. Regardless, we will follow your suggestion.

Reviewer comment (Experiments):
There are more up-to-date metric-learning losses that could’ve been explored. Examples:  
- Supervised Contrastive Loss: https://arxiv.org/abs/2004.11362  
- InfoNCE: https://papers.nips.cc/paper_files/paper/2016/file/6b180037abbebea991d8b1232f8a8ca9-Paper.pdf  
- Tuplet Margin Loss: https://openaccess.thecvf.com/content_ICCV_2019/papers/Yu_Deep_Metric_Learning_With_Tuplet_Margin_Loss_ICCV_2019_paper.pdf

Author response:
Contrastive loss is fairly older than triplet loss. We will consider the other two.

Reviewer comment (Experiments):
Not enough baselines compared against. Some recommendations include:  
- OpenAI Detector: https://huggingface.co/roberta-base-openai-detector  
- LLMDet: https://github.com/TrustedLLM/LLMDet  
- A RoBERTa detector fine-tuned on the context and LLM-generation / human-answer for each dataset.

Author response:
We eliminate a lot of detectors from comparison due to their complexity. We are aiming at small models that can be maintained in consumer computers.

Reviewer comment (Evaluation):
In S5, the training time of various methods is brought up as a reason to exclude certain baselines from consideration. However, this is an offline cost, which is not relevant at inference/test-time, so it's not clear why it's relevant. How do various methods compare at inference time?

Author response:
While computational costs of supervised classifiers are indeed cheaper at inference time, they still require a big enough system to host/maintain. For example, ROBERTA at 1.5 billion parameters is definitely not easy for a regular consumer computer to sustain. As we really want to aim at cheap models, complexity in inference is also of interests.

Reviewer comment (Experiments):
In practice, the robustness of machine-text detectors is important for many applications of machine-text detectors. For example, the cost of false positives is high when making false accusations of plagiarism. As a result, it is important to measure the robustness and calibration of such detectors. How does the proposed method fare when faced with various distribution shifts relative to the training data? (new LLMs, new domains, new decoding methods, etc.) Is it more robust than simple alternatives, such as simple supervised classifiers?

Author response:
Expanding this model to general/new LLMs and domains is one future direction of this project.

Reviewer comment (Experiments):
Lack of convincing baselines. The authors merely use the distance threshold approach on the embedding generated by the standalone pretrained MPNet as the baseline.

Author response:
Dear reviewer,

Thank you very much for you feedback. We want to address your second point regarding the baseline. As we eliminate supervised classifiers due to their complexity, and probabilistic models as they require access to output logits, the choice of baselines is limited. We will take your suggestion and incorporate more literature into the next iteration of this paper.

Thank you again.