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Found 15 papers, 6 papers with code
Prompt-free and Efficient Few-shot Learning with Language Models
Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score.
Leeroo Orchestrator: Elevating LLMs Performance Through Model Integration
In this paper, we propose an architecture to harness the collective knowledge of multiple trained LLMs to create a new state-of-the-art.
Ranked #4 on
Multi-task Language Understanding
on MMLU
(using extra training data)
RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question
We demonstrate that RQUGE has a higher correlation with human judgment without relying on the reference question.
Policy Compliance Detection via Expression Tree Inference
In this work, we learn how to infer expression trees automatically from policy texts.
Open Vocabulary Extreme Classification Using Generative Models
To develop systems that simplify this process, we introduce the task of open vocabulary XMC (OXMC): given a piece of content, predict a set of labels, some of which may be outside of the known tag set.
PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models
Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score.
Cross-Policy Compliance Detection via Question Answering
Policy compliance detection is the task of ensuring that a scenario conforms to a policy (e. g. a claim is valid according to government rules or a post in an online platform conforms to community guidelines).
Database Reasoning Over Text
Neural models have shown impressive performance gains in answering queries from natural language text.
Neural Databases
We describe NeuralDB, a database system with no pre-defined schema, in which updates and queries are given in natural language.
KILT: a Benchmark for Knowledge Intensive Language Tasks
We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.
Ranked #3 on
Entity Linking
on KILT: WNED-CWEB
SESA: Supervised Explicit Semantic Analysis
We propose a novel model of representation learning called Supervised Explicit Semantic Analysis (SESA) that is trained in a supervised fashion to embed items to a set of dimensions with explicit semantics.
