Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learning
Large language models (LLMs) have exhibited remarkable few-shot learning capabilities and unified the paradigm of NLP tasks through the in-context learning (ICL) technique. Despite the success of ICL, the quality of the exemplar demonstrations can significantly influence the LLM's performance....
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Zusammenfassung: | Large language models (LLMs) have exhibited remarkable few-shot learning
capabilities and unified the paradigm of NLP tasks through the in-context
learning (ICL) technique. Despite the success of ICL, the quality of the
exemplar demonstrations can significantly influence the LLM's performance.
Existing exemplar selection methods mainly focus on the semantic similarity
between queries and candidate exemplars. On the other hand, the logical
connections between reasoning steps can be beneficial to depict the
problem-solving process as well. In this paper, we proposes a novel method
named Reasoning Graph-enhanced Exemplar Retrieval (RGER). RGER first quires LLM
to generate an initial response, then expresses intermediate problem-solving
steps to a graph structure. After that, it employs graph kernel to select
exemplars with semantic and structural similarity. Extensive experiments
demonstrate the structural relationship is helpful to the alignment of queries
and candidate exemplars. The efficacy of RGER on math and logit reasoning tasks
showcases its superiority over state-of-the-art retrieval-based approaches. Our
code is released at https://github.com/Yukang-Lin/RGER. |
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DOI: | 10.48550/arxiv.2409.11147 |