Retrieval-Augmented Few-shot Learning: Improving Few-shot Learning for Implicit Hate Speech Detection
Authors: Jaehoon Kim, Seungwan Jin, Someen Park, Sohyun Park, Kyungsik Han


Reviewer paper summaries: The authors introduced a new prompting strategy called RAF - Retrieval Augmented Few Shot Learning for Hate Speech Detection. The approach is novel and has some great potential. First, they maintain a vectorized dataset of relevant data for context. Then during prompting they provide some context-relevant examples from both classes to the model. This has shown to be more effective and scalable in practice.
Summary of strengths: 01. The new prompting + retrieval strategy called RAF seemed to work very well during hate speech detection. However, it has the potential to be robust and implemented for other downstream tasks as well.

02. The method is scalable for few-shot prompting which has been a bottle neck for several other few-shot prompting methods.
Summary of weaknesses: 01. The authors have only experimented with GPT-3.5-Turbo and GPT-4o. More experimentations with other models would have solidified their claim even more.

02. What vector database was chosen to store the vectors? This is critical for any kind of RAG-based methods. However, no insights were provided.
Comments, suggestions and typos: 01. Figure 3 is very difficult to make any sense of.
02. Appendix sections are misaligned. It's hard to understand which content belongs to which section.
Overall assessment scores: 3