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Found 5 papers, 3 papers with code
What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models
In this work, we examine the behaviour of non-target heads, that is, the output of heads when given input that belongs to a different task than the one they were trained for.
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval
The third, and most challenging, is the move from the recognition setup to a novel retrieval setup, where the query is a zero-shot entity type, and the expected result is all the sentences from a large, pre-indexed corpus that contain entities of these types, and their corresponding spans.
Answering Questions by Meta-Reasoning over Multiple Chains of Thought
Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer.
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Inferring Implicit Relations in Complex Questions with Language Models
A prominent challenge for modern language understanding systems is the ability to answer implicit reasoning questions, where the required reasoning steps for answering the question are not mentioned in the text explicitly.
What's in your Head? Emergent Behaviour in Multi-Task Transformer Models
In this work, we examine the behaviour of non-target heads, that is, the output of heads when given input that belongs to a different task than the one they were trained for.
