Challenge: Currently, intelligent assistants require explicit user requests to perform tasks or services, leading to lengthy and complex conversations.
Approach: They propose a framework that automatically infers implicit intents from user utterances and prompts a large pre-trained language model to suggest suitable task-oriented bots.
Outcome: The proposed framework reduces interaction complexity and integrates domains and tasks.

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Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models (2025.coling-main)

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Challenge: Existing approaches to commonsense-augmented dialogue rely on implicit reasoning to integrate commonsensense inferences during response generation.
Approach: They propose to separate commonsense reasoning into explicit steps for generating, selecting, and integrating commonsensense into dialogue responses.
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Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective (2022.emnlp-main)

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Challenge: Existing approaches to zero-shot learning are format-agnostic and can address new learning tasks without additional training.
Approach: They propose a new paradigm for zero-shot learning that is format agnostic and compatible with any format and applicable to a list of language tasks.
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Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts (2023.acl-long)

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Challenge: Recent studies have demonstrated that natural-language prompts can help to leverage the knowledge learned by pre-trained language models for the binary sentence-level sentiment classification task.
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Towards Zero-Shot Persona Dialogue Generation with In-Context Learning (2023.findings-acl)

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Challenge: Existing methods to improve persona consistency on high-quality human-labeled persona datasets face high cost and poor scalability.
Approach: They propose a method to improve zero-shot persona consistency via in-context learning by pre-training a persona-augmented dialogue generation model and then using in-constant prompting mechanism to realize zero- shot persona customization.
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Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models (2021.emnlp-main)

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Challenge: Existing language models can be refined for zero-shot commonsense reasoning . however, commons sense reasoning is still an unsolved problem .
Approach: They propose a self-supervised learning approach that refines a pre-trained language model to boost conceptualization.
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Global Constraints with Prompting for Zero-Shot Event Argument Classification (2023.findings-eacl)

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Challenge: Existing zero-shot trigger extraction models require annotations, which is not practical for open-domain applications.
Approach: They propose to use global constraints with prompting to tackle event argument classification without annotation and task-specific training.
Outcome: The proposed model outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1 without given argument spas.
Prompt Consistency for Zero-Shot Task Generalization (2022.findings-emnlp)

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Challenge: Recent work has shown that pre-trained language models can perform zero-shot generalization to new tasks without annotated examples.
Approach: They propose to regularize prompt consistency to encourage consistent predictions over a diverse set of prompts.
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Zero-shot Commonsense Reasoning over Machine Imagination (2024.findings-emnlp)

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Challenge: Recent approaches to zero-shot commonsense reasoning have suffered from human reporting bias inherent in textual commonsence knowledge, leading to discrepancies in understanding between PLMs and humans.
Approach: They propose a zero-shot commonsense reasoning framework that integrates machine-generated images into the reasoning process.
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Rethinking Task-Oriented Dialogue Systems: From Complex Modularity to Zero-Shot Autonomous Agent (2024.acl-long)

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Challenge: Task-oriented dialogue systems are designed to be composed of several functional modules, but lacks a general-purpose instruction-following language model.
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Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)

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Challenge: Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations.
Approach: They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch.
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