Few-shot relation extraction involves identifying the type of relationship
between two specific entities within a text, using a limited number of
annotated samples. A variety of solutions to this problem have emerged by
applying meta-learning and neural graph techniques which typically necessitate
a training process for adaptation. Recently, the strategy of in-context
learning has been demonstrating notable results without the need of training.
Few studies have already utilized in-context learning for zero-shot information
extraction. Unfortunately, the evidence for inference is either not considered
or implicitly modeled during the construction of chain-of-thought prompts. In
this paper, we propose a novel approach for few-shot relation extraction using
large language models, named CoT-ER, chain-of-thought with explicit evidence
reasoning. In particular, CoT-ER first induces large language models to
generate evidences using task-specific and concept-level knowledge. Then these
evidences are explicitly incorporated into chain-of-thought prompting for
relation extraction. Experimental results demonstrate that our CoT-ER approach
(with 0% training data) achieves competitive performance compared to the
fully-supervised (with 100% training data) state-of-the-art approach on the
FewRel1.0 and FewRel2.0 datasets.

使用大型语言模型的 CoT-ER 方法为少样本关系提取提出了一种新的方法，该方法利用具体证明推理将证据明确地纳入思维链式提示，实验结果表明，CoT-ER 方法（无训练数据）在 FewRel1.0 和 FewRel2.0 数据集上的表现与全监督（有 100% 训练数据）最新方法相当。

基于显式证据推理的少样本关系抽取思路链

Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation  Extraction

Fact verification is a challenging task that requires simultaneously
reasoning and aggregating over multiple retrieved pieces of evidence to
evaluate the truthfulness of a claim. Existing approaches typically (i) explore
the semantic interaction between the claim and evidence at different
granularity levels but fail to capture their topical consistency during the
reasoning process, which we believe is crucial for verification; (ii) aggregate
multiple pieces of evidence equally without considering their implicit stances
to the claim, thereby introducing spurious information. To alleviate the above
issues, we propose a novel topic-aware evidence reasoning and stance-aware
aggregation model for more accurate fact verification, with the following four
key properties: 1) checking topical consistency between the claim and evidence;
2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring
semantic similarity between the global topic information and the semantic
representation of evidence; 4) aggregating evidence based on their implicit
stances to the claim. Extensive experiments conducted on the two benchmark
datasets demonstrate the superiority of the proposed model over several
state-of-the-art approaches for fact verification. The source code can be
obtained from this https URL

该研究提出了一种主题感知证据推理和立场感知集成模型来更准确地验证事实，重点在于检查索赔和证据之间的主题一致性，维护多个证据之间的主题连贯性，确保全局主题信息和证据的语义表示之间的语义相似性，并基于证据对索赔的隐含立场聚合证据。