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Found 5 papers, 5 papers with code
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
This has made distilled and dense models, due to latency constraints, the go-to choice for deployment in real-world retrieval applications.
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Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task
Recent work has shown that language models scaled to billions of parameters, such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios.
To Tune or Not To Tune? Zero-shot Models for Legal Case Entailment
For that, we participated in the legal case entailment task of COLIEE 2021, in which we use such models with no adaptations to the target domain.
A cost-benefit analysis of cross-lingual transfer methods
An effective method for cross-lingual transfer is to fine-tune a bilingual or multilingual model on a supervised dataset in one language and evaluating it on another language in a zero-shot manner.
Yes, BM25 is a Strong Baseline for Legal Case Retrieval
We describe our single submission to task 1 of COLIEE 2021.
