Ehoro A.G.

🧪 SlopFinder is here!!

We're building a dataset to study what humans actually consider AI slop.

SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.

Every vote helps build the dataset. 🧩

How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.

This is an early MVP, so the dataset is small and the system is still evolving.

Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)

Dataset:
bench-labs/slop-classification

@benchlabs

17 replies

reacted to Yuki131's post with 🔥 3 days ago

Post

2140

Test-Time Scaling for Rerankers?

Can rerankers scale at test time—not by generating longer reasoning traces, but by selectively using richer document representations?

KaLM-Reranker-V1 supports Matryoshka compression from 1× to 32×, which suggests a progressive multi-fidelity pipeline:

- Embedding retrieval → Top-100
- KaLM-Reranker @ 32× compression → Top-20
- The same reranker @ 2× compression → final ranking

The intuition is simple: cheaply screen many candidates, then allocate higher-fidelity cross-attention only to the most promising ones.

For 100@32× → 20@2×, the passage-token interaction budget is roughly 31.8% of directly running 100@2×, before fixed model overheads. The key question is whether it can retain nearly the same ranking quality.

We’re considering evaluating nDCG–latency Pareto curves.

Would you consider this a useful form of test-time scaling for retrieval?

KaLM-Embedding/KaLM-Reranker-V1-Nano

KaLM-Embedding/KaLM-Reranker-V1-Small

KaLM-Embedding/KaLM-Reranker-V1-Large

KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking (2606.22807)

https://huggingface.co/collections/KaLM-Embedding/lychee-kalm-reranker