  Large Language Models (LLMs) have demonstrated exceptional performance in
biochemical tasks, especially the molecule caption translation task, which aims
to bridge the gap between molecules and natural language texts. However,
previous methods in adapting LLMs to the molecule-caption translation task
required extra domain-specific pre-training stages, suffered weak alignment
between molecular and textual spaces, or imposed stringent demands on the scale
of LLMs. To resolve the challenges, we propose In-Context Molecule Adaptation
(ICMA), as a new paradigm allowing LLMs to learn the molecule-text alignment
from context examples via In-Context Molecule Tuning. Specifically, ICMA
incorporates the following three stages: Hybrid Context Retrieval,
Post-retrieval Re-ranking, and In-context Molecule Tuning. Initially, Hybrid
Context Retrieval utilizes BM25 Caption Retrieval and Molecule Graph Retrieval
to retrieve informative context examples. Additionally, we also propose
Post-retrieval Re-ranking with Sequence Reversal and Random Walk to further
improve the quality of retrieval results. Finally, In-Context Molecule Tuning
unlocks the in-context molecule learning capability of LLMs with retrieved
examples and adapts the parameters of LLMs for the molecule-caption translation
task. Experimental results demonstrate that ICMT can empower LLMs to achieve
state-of-the-art or comparable performance without extra training corpora and
intricate structures, showing that LLMs are inherently in-context molecule
learners.
