| Challenge: | Existing approaches to zero/few-shot slot filling focus on slot descriptions and examples . AISFG model is based on domain-specific labels, which is not capable of transferring to new domains with little or no data. |
| Approach: | They propose a model with a query template that incorporates domain descriptions, slot descriptions, and examples with context. |
| Outcome: | Experimental results show that the proposed model outperforms state-of-the-art approaches in zero/few-shot slot filling task. |
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Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting (2023.findings-acl)
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| Challenge: | Existing approaches to slot filling only learn surface mapping of slot types between D S and D T and get poor generalization capability or robustness. |
| Approach: | They propose a generative zero-shot prompt learning framework for cross-domain slot filling which improves generalization and robustness than previous work. |
| Outcome: | The proposed framework improves generalization and robustness on unseen slots and an efficient prompt tuning strategy boosts performance. |
Robust Zero-Shot Cross-Domain Slot Filling with Example Values (P19-1)
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| Challenge: | Task-oriented dialog systems rely on deep learning-based slot filling models . little to no training data for target domains may be available or schemas may not be aligned . |
| Approach: | They propose to use slot descriptions and examples of slot values to learn slot semantic representations that are transferable across domains and robust to misaligned schemas. |
| Outcome: | The proposed model outperforms state-of-the-art models on two multi-domain datasets on low-data setting. |
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining (2021.acl-short)
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| Challenge: | Existing methods to predict slots and their values do not encode enough semantic information, limiting the models’ zero-shot capability. |
| Approach: | They propose a QA-driven slot filling model which extracts slot-filler spans from utterances with a span-based QA model. |
| Outcome: | The proposed model outperforms baselines by over 5% on the SNIPS benchmark. |
Robust Retrieval Augmented Generation for Zero-shot Slot Filling (2021.emnlp-main)
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| Challenge: | Automating high quality knowledge graphs from a given collection of documents remains a challenging problem in AI. |
| Approach: | They propose a novel approach to slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. |
| Outcome: | The proposed model improves on both T-REx and zsRE slot filling datasets and ranks at the top-1 position in the KILT leaderboard. |
Synergistic Augmentation: Enhancing Cross-Domain Zero-Shot Slot Filling with Small Model-Assisted Large Language Models (2025.findings-acl)
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| Challenge: | Existing approaches to slot filling are limited due to data scarcity and timeconsuming efforts. |
| Approach: | They propose a framework that harnesses the power of a small model to augment inferential capabilities of LLMs without additional training. |
| Outcome: | The proposed framework improves slot filling performance on a spoken language dataset and a NER dataset. |
SpeechLLMs for Large-scale Contextualized Zero-shot Slot Filling (2025.emnlp-industry)
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| Challenge: | Slot filling is a key subtask in spoken language understanding (SLU) . recent advent of speech-based large language models has opened new avenues for speech understanding . |
| Approach: | They propose to improve slot-filling task by creating an empirical upper bound for the task . they propose to use a speech-based large language model to integrate speech and text modalities . |
| Outcome: | The proposed model improves slot filling performance while reducing generalization gaps. |
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling (2021.emnlp-main)
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| Challenge: | Existing methods for zero-shot cross-domain slot filling do not achieve effective knowledge transfer to the target domain. |
| Approach: | They propose a novel approach based on prototypical contrastive learning and a dynamic label confusion strategy for zero-shot slot filling. |
| Outcome: | The proposed model improves on unseen slots while setting new state-of-the-arts on slot filling task. |
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System (2021.acl-long)
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| Challenge: | Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. |
| Approach: | They introduce a task, Novel Slot Detection, in the task-oriented dialogue system. |
| Outcome: | The proposed task is based on two public NSD datasets and proposes strong baselines . it aims to identify a sequence of tokens and extract semantic constituents from user queries . |
Zero-shot Slot Filling in the Age of LLMs for Dialogue Systems (2025.coling-industry)
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| Challenge: | Existing methods for zero-shot slot filling focus on text data, overlooking conversational data. |
| Approach: | They propose a method for automatic data annotation with slot induction and black-box knowledge distillation from a teacher LLM to a smaller model. |
| Outcome: | The proposed method outperforms existing models on internal datasets by 26% relative increase in F1 score. |
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking (2021.naacl-main)
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Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba
| Challenge: | Existing models for zero-shot cross-domain dialogue state tracking require in-domain data to model a new domain. |
| Approach: | They propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST by encoding a dialogue context and a slots with a pre-trained encoder and generating slot value in auto-regressive manner. |
| Outcome: | The proposed model significantly improves state-of-the-art results in zero-shot cross-domain setting. |