Kati8/ICLR_2023_2025 huggingface dataset:
authors: Ching-Yun Ko
authors: Payel Das
authors: Youssef Mroueh
authors: Soham Dan
authors: Georgios Kollias
authors: Subhajit Chaudhury
authors: Tejaswini Pedapati
authors: Luca Daniel
reviews: 
Comments: [Edit] I have updated my rating after reading the authors' response.

This paper proposes "SASA", a decoding algorithm that can be used with language models to detoxify their outputs. SASA uses the embedding space of the LM along with a classifier model to steer the LM to generate less toxic statements. The authors demonstrate the efficacy of their method with numerous experiments on multiple datasets as well as varied language models; further they report standard metrics such as toxicity rate that are commonly used in this field.
Strengths: * Strong range of experiments that show the much reduced toxicity in their model's generations. 
* Figures that explain their method clearly, leading to ease of understanding.
Weaknesses: * I suggest that the authors revert the pdf to the original font and style, to maintain the standard format of ICLR. 
* What is the classification model fv(c,x) used in the experiments? Further, what is the classification accuracy of this model in practice? 
* Related to above: Line 465 states "That said, with an aligned base model, the internal sentence embedding space can
be more informative of risk attributes such as toxicity" - do you have empirical proof of the same via the classification model's accuracy?
* Table 1 shows a huge drop in fluency/perplexity in SASA - what is the significance of the same?
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper aims to tackle the challenge of minimizing the generation of harmful and toxic outputs. While existing approaches involve retraining large language models (LLMs) or utilizing an external model to guide LLMs in reducing harmful generation, this paper proposes a non-parametric method that constructs toxic and non-toxic subspaces to steer language generation.
Strengths: 1. The primary strength of this approach is its non-parametric nature and its training-free implementation within the context of LLMs. Consequently, this method does not require extensive computational resources.

2. The proposed method demonstrates effectiveness in specific tasks, as it learns subspaces from existing datasets.
Weaknesses: 1. The generalization capability of the proposed method is limited. The subspaces are derived from a relatively small dataset containing approximately 2 million samples, which may result in a lack of generalization to broader scenarios.

2. An increase in perplexity (PPL) is observed as the value of beta increases, which could potentially degrade text quality.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3