ICLER : Intent CL assification with Enhanced Reasoning (2025)

Abstract: In recent years, intent classification technology based on In-Context Learning (ICL) has made significant progress. However, when applied to enterprise vertical domains, existing methods are inadequate in identifying micro-grained intentions. This study identifies two primary causes of errors in data analysis: (1) Retrieving incorrect instances, this is often due to the limitations of embedding models in capturing subtle sentence-level information in business scenarios (such as entity-related or phenomenon-specific details) (2) Insufficient reasoning ability of Large Language Models (LLMs), which tend to rely on surface-level semantics while overlooking deeper semantic associations and business logic, leading to misclassification. To address these issues, we propose ICLER, an intent classification method with enhanced reasoning. This method first optimizes the embedding model by introducing a reasoning mechanism to enhance its ability to fine-grained sentence-level information. Then, this mechanism is incorporated into the ICL framework, maintaining computational efficiency while significantly enhancing intent recognition accuracy. Experimental results demonstrate that ICLER significantly outperforms the original ICL method in intent identification within vertical domains. Moreover, it yields accuracy improvements of 0.04% to 1.14% on general datasets and its fine-tuned embedding model achieves an average performance gain of 5.56% on selected classification tasks in the MTEB benchmark.

## introduction natural language processing (nlp) has witnessed remarkable advancements through large-scale pretrained language models, which demonstrate exceptional performance across various tasks via in-context learning(icl). icl leverages analogical learning principles, enabling models to iden-tify patterns from provided examples and generate predictions for few-shot classification tasks. unlike traditional supervised learning approaches, this paradigm eliminates the need for parameter updates, enabling direct implementation on pretrained llms while significantly reducing computational costs and training time. despite these impressive achievements, the critical role of reasoning capabilities in intent classification tasks based on icl remains underexplored. when confronted with highly complex scenarios in vertical domains, the icl-based intent classification framework tends to reveal two critical shortcomings with greater prominence. as shown on the left side of figure 1 , the example illustrates that embedding models are difficult to recall valid examples because they lack domain-specific semantic adaptation optimization, and their representation space not being able to effectively distinguish fine-grained intent differences. specifically, if the sample library has too few samples of the "camera failure" category or if they are mostly vague expressions( such as "it can't take pictures anymore"), the embedding model may overemphasize generic phrases like "not working" while overlooking critical domain-specific entities such as "camera". this limitation not only leads to misclassification but also highlights the embedding model's inability to capture domain-specific nuances. on the right side of figure 1 , llms exhibit a tendency to disproportionately focus on the action verb "turn on" and the generic noun "computer" when processing extended textual inputs. this observed pattern suggests that it is difficult for llms to achieve the mapping extension from surface semantics to deep semantics, for example, the causal relationship between "pressed the power button but there was no response" and "the computer won't turn on", and thus cannot complete the reasoning of users' actual intent through icl-based intent classification framework, ultimately leading to a significant reduction in the accuracy of intent recognition. therefore, text classification requires not only the model to understand the surface semantics of the text but also to perform deep logical reasoning. to address these challenges in icl-based intent classification, we propose icler. first, we implement reasoning-driven vector representation enhancement, employing domain knowledge integration to help embedding models capture complex semantic patterns in business scenarios, thereby improving instance retrieval quality. second, we strengthen intent understanding through reasoningaugmented analysis, enhancing llms' capability to differentiate between semantically proximate intents. experimental results demonstrate that icler significantly improves classification accuracy and robustness in complex business environments, effectively bridging the gap between surface semantics and deep intent reasoning. our paper contributions are: ## method figure 2 illustrates the processing pipeline of icler. this method processes query by retrieving relevant examples from the instance database d and logical analysis results from the reasoning database r through a dual-channel retrieval mechanism. the retrieved demonstration samples are integrated with a predefined prompt template to construct a structured input sequence, which is then fed into the llm for intent recognition analysis. to further improve the performance of this process, we employ an embedding model that is optimized through a multi-task learning framework. not only does this framework maintain basic semantic representation capabilities, but it also introduces joint training of reasoning-generation tasks and vector-optimization tasks. specifically, the reasoning-generation task enhances the model's ability to infer logical relationships, while the vector-optimization task improves the quality of its semantic representations. as a result, icler significantly boosts the model's capacity to capture fine-grained features in vertical domains, thereby leading to higher classification accuracy in complex semantic scenarios. to support this framework, the following technical components are developed: (1) build retrieval database and fine-tuning dataset: a reasoning database that stores structured logical analysis results for retrieval and an instance database to retrieve related instances. then, build a fine-tuning dataset with the corresponding database; (2) multi-task training pipeline: a multi-task training pipeline that optimizes embedding quality through joint learning of reason-ing and representation tasks; (3) results obtain: an intent classification module that integrates improved embeddings and logical reasoning for accurate intent recognition. ## conclusion in conclusion, icler successfully improves the performance of icl in the intention classification task. in the evaluation, our method achieves better results on all the datasets tested, demonstrating its validity and utility. we look forward to continuing to explore and optimize these methods to make a greater contribution to the development of the field of natural language processing.