Papers with data construction strategy

2 papers
CodeM: Less Data Yields More Versatility via Ability Matrix (2024.findings-acl)

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Challenge: Recent efforts to train code large language models have been booming recently . however, this will incur significant costs in constructing data and training model considering the countless downstream scenarios.
Approach: They propose a data construction strategy which decouples code LLMs’ abilities into two dimensions and constructs a lightweight training corpus that only covers a subset of target scenarios.
Outcome: The proposed model can train a multilingual multitasking model using less data and training data.
Sentence Ordering with a Coherence Verifier (2023.findings-acl)

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Challenge: Recent sentence ordering studies can be classified into 2 categories: pair-wise ranking-based and sequence generation-based methods.
Approach: They propose a sentence ordering method by plugging a coherence verifier into ranking-based and sequence generation-based methods.
Outcome: The proposed method improves on topological sorting-based and pointer network-based methods with topological and point-based models.

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