Challenge: Existing approaches to sequential sentence classification are constrained by model size, sequence length, and single-label setting.
Approach: They propose a large language model-based framework for both single- and multi-label SSC tasks that generate SSC labels through designed prompts.
Outcome: The proposed framework enhances task understanding by incorporating demonstrations and a query to describe the prediction target.

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Challenge: Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification, but the vast number of labels can exceed LLMs’ input limits.
Approach: They propose a method that integrates large language models with dense retrieval techniques to overcome these challenges.
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Topic Modeling for Short Texts with Large Language Models (2024.acl-srw)

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Challenge: Large Language Models (LLMs) can be used to solve topic modeling challenges for short texts by contextually learning the meanings of words.
Approach: They propose two approaches to using Large Language Models (LLMs) for topic modeling: parallel prompting and sequential prompting.
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Efficient Sequential Decision Making with Large Language Models (2024.emnlp-main)

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Challenge: Existing approaches to retrain or finetune large language models (LLMs) for decision making suffer from computational burden of gradient updates.
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Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning (2024.naacl-long)

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Challenge: Existing methods for training contrastive learning based sentence embedding models are largely influenced by the quality of sentence pairs.
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LLM-Guided Co-Training for Text Classification (2025.emnlp-main)

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Challenge: Empirical results show that it achieves state-of-the-art performance on 4 out of 5 benchmark datasets and ranks first among 14 compared methods according to the Friedman test.
Approach: They propose a weighted co-training approach that is guided by Large Language Models (LLMs) they use LLM labels on unlabeled data as target labels and co-train two encoder-only based networks that train each other over multiple iterations.
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A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches (2025.findings-naacl)

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Challenge: Existing approaches for low-resource text summarization use large language models (LLMs) but such models suffer from inconsistent outputs and are difficult to adapt to domain-specific data.
Approach: They propose two methods to effectively utilize large language models for low-resource text summarization.
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Scalable and Cost Effective High-Cardinality Classification with LLMs via Multi-View Label Representations and Retrieval Augmentation (2025.emnlp-industry)

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Challenge: Existing methods for labeling contact center interactions show significant inconsistencies and sensitivity to label ordering.
Approach: They propose a two-step retrieval-augmented classification framework enhanced with a multi-view representation of labels.
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PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation (2024.emnlp-main)

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Challenge: Large language models (LLMs) are useful for low-resource scenarios and time-restricted applications.
Approach: They propose a large-scale evaluation tool for large language models that uses prompts . they evaluate 720 prompt templates for open-source LLM-based metrics on MT and summarization datasets a 6.6M evaluations.
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Mixture of Soft Prompts for Controllable Data Generation (2023.findings-emnlp)

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Challenge: Large language models (LLMs) generate fluent text when the target output follows natural language patterns.
Approach: They propose a method that uses large language models to generate fluent text from a limited ontology rather than direct prediction by using soft prompts.
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Fast Adaptation via Prompted Data: An Efficient Cross-Domain Fine-tuning Method for Large Language Models (2024.lrec-main)

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Challenge: Large language models (LLMs) have been successful in a variety of natural language understanding tasks, but domain discrepancies between the downstream task and the pre-training corpora may have hindered LLMs to excel further in the vertical applications.
Approach: They propose a Fast Adaptation method for LLMs via Prompted Data that integrates downstream text corpora, gold labels and external knowledge sources into a highly controllable prompt.
Outcome: The proposed method bridges the gap between the downstream task and the pre-training corpora and integrates downstream text corpors, gold labels and external knowledge sources into a highly controllable prompt.

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