Papers with PLUG

2 papers
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)

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Challenge: Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data.
Approach: They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks.
Outcome: The proposed language model generalizes well across knowledge-grounded dialogue tasks.
PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning (2024.acl-long)

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Challenge: Instruction tuning has advanced large language models (LLMs) but its application in lower-resource languages faces challenges due to the imbalanced foundational abilities of LLMs across different languages.
Approach: They propose a pivot language guided generation approach that utilizes a high-resource language as the pivot to enhance instruction tuning in lower-resourced languages.
Outcome: The proposed approach improves instruction-following abilities of LLMs by 29% on average compared to directly responding in the target language alone.

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