REVIEW 2 cited by
ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We introduce ZeroSCROLLS, a zero-shot benchmark for natural language understanding over long texts, which contains only test and small validation sets, without training data. We adapt six tasks from the SCROLLS benchmark, and add four new datasets, including two novel information fusing tasks, such as aggregating the percentage of positive reviews. Using ZeroSCROLLS, we conduct a comprehensive evaluation of both open-source and closed large language models, finding that Claude outperforms ChatGPT, and that GPT-4 achieves the highest average score. However, there is still room for improvement on multiple open challenges in ZeroSCROLLS, such as aggregation tasks, where models struggle to pass the naive baseline. As the state of the art is a moving target, we invite researchers to evaluate their ideas on the live ZeroSCROLLS leaderboard.
Forward citations
Cited by 2 Pith papers
-
ARC-Encoder: learning compressed text representations for large language models
ARC-Encoder pools queries in an encoder's last attention layer to produce compressed continuous representations that a frozen decoder consumes as token embeddings.
-
MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models
MiniLongBench, a 237-sample compression of LongBench, is claimed to reproduce model rankings with a 0.97 Spearman correlation at 4.5% of the evaluation cost.
Discussion (0). Continue with ORCID to comment.