| Challenge: | Existing methods for intent classification are inadequate in identifying micro-grained intentions . ICLER is based on In-Context Learning, but it is inadequate in enterprise vertical domains . |
| Approach: | They propose an intent classification method with enhanced reasoning that optimizes the embedding model to capture subtle sentence-level information. |
| Outcome: | The proposed method outperforms existing methods in intent identification tasks in vertical domains. |
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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui
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Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models (2026.acl-long)
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Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zhengqi Wen, Chonghua Liao, Ling Yang, Haoran Luo, Zheng Lian, Jianhua Tao
| Challenge: | In-context learning (ICL) struggles with complex reasoning due to superficial, example-level implicit imitation. |
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Scaling Intent Understanding: A Framework for Classification with Clarification using Lightweight LLMs (2026.eacl-industry)
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| Challenge: | Proprietary large-language models (LLMs) assign intents to user utterances without addressing ambiguity. |
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ICLEval: Evaluating In-Context Learning Ability of Large Language Models (2025.coling-main)
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| Challenge: | Existing evaluation frameworks focus on language abilities and knowledge, often overlooking the assessment of ICL ability. |
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Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions (2024.emnlp-main)
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| Challenge: | Generative large language models (LLMs) have brought advances in text generation, but their potential for enhancing classification tasks remains underexplored. |
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Going beyond research datasets: Novel intent discovery in the industry setting (2023.findings-eacl)
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| Challenge: | Novel intent discovery automates grouping of similar messages to identify previously unknown intents. |
| Approach: | They propose to use question-only data to improve the intent discovery pipeline . they propose to utilize conversational structure of real-life datasets for clustering . |
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REIC: RAG-Enhanced Intent Classification at Scale (2025.emnlp-industry)
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Ziji Zhang, Michael Yang, Zhiyu Chen, Yingying Zhuang, Shu-Ting Pi, Qun Liu, Rajashekar Maragoud, Vy Nguyen, Anurag Beniwal
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Intent Discovery with Frame-guided Semantic Regularization and Augmentation (2023.findings-acl)
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| Challenge: | Existing intent discovery methods focus on transferring prior knowledge of known intents to unknown ones. |
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Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance (2024.findings-emnlp)
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| Challenge: | In-context learning (ICL) is a dominant paradigm in natural language processing. |
| Approach: | They propose a prompting method for classification tasks using exemplar answers in a *comparative format' they also propose introducing a test instance before the exemplars to improve performance . |
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From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning (2024.findings-acl)
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| Challenge: | Existing studies fine-tune discriminative models on specific defined intent classes, preventing them from being directly adopted to new intent domains. |
| Approach: | They propose to use a pre-trained generative intent model to detect new intents from different domains with no parameter updates. |
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