Instruction-tuned Language Models are Better Knowledge Learners
In order for large language model (LLM)-based assistants to effectively adapt
to evolving information needs, it must be possible to update their factual
knowledge through continued training on new data. The standard recipe for doing
so involves continued pre-training on new documents followed by
instruction-tuning on question-answer (QA) pairs. However, we find that LLMs
trained with this recipe struggle to answer questions, even though the
perplexity of documents is minimized. We found that QA pairs are generally
straightforward, while documents are more complex, weaving many factual
statements together in an intricate manner. Therefore, we hypothesize that it
is beneficial to expose LLMs to QA pairs before continued pre-training on
documents so that the process of encoding knowledge from complex documents
takes into account how this knowledge is accessed through questions. Based on
this, we propose pre-instruction-tuning (PIT), a method that instruction-tunes
on questions prior to training on documents. This contrasts with standard
instruction-tuning, which learns how to extract knowledge after training on
documents. Extensive experiments and ablation studies demonstrate that PIT
significantly enhances the ability of LLMs to absorb knowledge from new
documents, outperforming standard instruction-tuning by 17.8%.
65d58152b8c5655c03b4267f
Zhengbao Jiang
admin_assigned
2024-02-21T10:12:23.174Z
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Zhengbao Jiang
user
zhengbao
65d58152b8c5655c03b42680
Zhiqing Sun
admin_assigned
2024-02-21T10:12:48.636Z
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Zhiqing Sun
user
zhiqings
65d58152b8c5655c03b42681
Weijia Shi
admin_assigned
2024-02-21T10:13:20.519Z
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Weijia Shi
user
swj0419
65d58152b8c5655c03b42682
Pedro Rodriguez
65d58152b8c5655c03b42683
Chunting Zhou
admin_assigned
2024-02-21T10:14:09.906Z
64f8d07dd493d8b0d2c04ecd
/avatars/7112232dcadcd2a7c12ec635d781b3ea.svg
Zhou
user
Chunting
65d58152b8c5655c03b42684
Graham Neubig
admin_assigned
2024-02-21T10:14:16.435Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1625166923504-noauth.png
Graham Neubig
user
gneubig
65d58152b8c5655c03b42685
Xi Victoria Lin
admin_assigned
2024-02-21T10:14:47.089Z
61df349e34b589fbaf2e93c4
/avatars/9dd7776d3b610d78e40c2e178ab2f112.svg
Victoria Lin
user
VictoriaLinML
65d58152b8c5655c03b42686
Wen-tau Yih
65d58152b8c5655c03b42687
Srinivasan Iyer
Instruction-tuned Language Models are Better Knowledge Learners
In order for large language model (LLM)-based assistants to effectively adapt
to evolving information needs, it must be possible to update their factual
knowledge through continued training on new data. The standard recipe for doing
so involves continued pre-training on new documents followed by
instruction-tuning on question-answer (QA) pairs. However, we find that LLMs
trained with this recipe struggle to answer questions, even though the
perplexity of documents is minimized. We found that QA pairs are generally
straightforward, while documents are more complex, weaving many factual
statements together in an intricate manner. Therefore, we hypothesize that it
is beneficial to expose LLMs to QA pairs before continued pre-training on
documents so that the process of encoding knowledge from complex documents
takes into account how this knowledge is accessed through questions. Based on
this, we propose pre-instruction-tuning (PIT), a method that instruction-tunes
on questions prior to training on documents. This contrasts with standard
instruction-tuning, which learns how to extract knowledge after training on
documents. Extensive experiments and ablation studies demonstrate that PIT
significantly enhances the ability of LLMs to absorb knowledge from new
documents, outperforming standard instruction-tuning by 17.8%.
Pre-instruction-tuning enhances large language models' ability to learn from documents by first exposing them to question-answer pairs.
large language model LLM continued training pre-training instruction-tuning question-answer pairs perplexity factual knowledge pre-instruction-tuning PIT ablation studies