| 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. |
| Outcome: | The proposed approach outperforms conventional methods on 4 out of 5 benchmark datasets and ranks first among 14 compared methods according to the Friedman test. |
Similar Papers
Exploring the Potential of Large Language Models for Heterophilic Graphs (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches for heterophilic graphs overlook rich textual data associated with nodes, which could unlock deeper insights into their heterophilistic contexts. |
| Approach: | They propose a two-stage framework to enhance node classification on heterophilic graphs by leveraging open-world knowledge encoded by large language models. |
| Outcome: | The proposed framework can be used to better characterize heterophilic graphs, where neighboring nodes often exhibit different labels. |
Multilingual Encoder Knows more than You Realize: Shared Weights Pretraining for Extremely Low-Resource Languages (2025.acl-long)
Copied to clipboard
| Challenge: | XLM-R and mBART have advanced multilingualism in NLP, but low-resource languages such as Tibetan, Uyghur, Kazakh, and Mongolian are underserved. |
| Approach: | They propose a framework for adapting multilingual encoders to text generation in extremely low-resource languages by reusing the weights between the encoder and the decoder. |
| Outcome: | The proposed framework performs better on various downstream tasks even when compared with much larger models. |
Leveraging Training Dynamics and Self-Training for Text Classification (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Semi-supervised learning (SSL) is a promising technique for improving deep learning models when training data is scarce. |
| Approach: | They propose a semi-supervised learning approach that leverages training dynamics of unlabeled data. |
| Outcome: | The proposed method achieves an average increase in F1 score of 3.5% over baselines in low resource settings. |
LLMaAA: Making Large Language Models as Active Annotators (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing supervised learning methods in natural language processing require large amounts of data. |
| Approach: | They propose an active learning loop that takes LLMs as annotators and puts them into an active loop to determine what to annotate efficiently. |
| Outcome: | The proposed model outperforms existing models with few-shot performance in two NLP tasks. |
Self-training Large Language Models through Knowledge Detection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) often require extensive labeled datasets and training compute to achieve impressive performance across downstream tasks. |
| Approach: | They propose a self-training paradigm where the LLM curates its own labels and selectively trains on unknown data samples identified through a reference-free consistency method. |
| Outcome: | The proposed model reduces the dependency on large labeled datasets and mitigates catastrophic forgetting in out-of-distribution benchmarks. |
Co-Evolving LLMs and Embedding Models via Density-Guided Preference Optimization for Text Clustering (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for text clustering use static pseudo-oracles, i.e., unidirectionally querying them for similarity assessment or data augmentation. |
| Approach: | They propose a training framework that enables bidirectional refinement between LLMs and embedding models by using task-aware prompts to guide the LLM in generating interpretations for the input texts. |
| Outcome: | Experiments on 14 benchmark datasets across 5 tasks demonstrate the effectiveness of the proposed training framework. |
How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
Copied to clipboard
Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
| Challenge: | Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs). |
| Approach: | They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications. |
| Outcome: | The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication. |
RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Linguistic Classifiers (2025.coling-industry)
Copied to clipboard
| Challenge: | Large language models have improved our ability to rapidly analyze and classify unstructured natural language data. |
| Approach: | They propose a system approach to employing LLMs as imperfect data annotators for downstream supervised learning tasks. |
| Outcome: | The proposed method outperforms LLM-generated labels in six of eight tests and base classifiers in all tests. |
Aligning Large Language Models for Controllable Recommendations (2024.acl-long)
Copied to clipboard
| Challenge: | Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template. |
| Approach: | They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions. |
| Outcome: | The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy. |
Harnessing Large Language Models as Post-hoc Correctors (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their effectiveness in a wide range of tasks, including machine translation and commonsense reasoning. |
| Approach: | They propose a training-free framework that can work as a post-hoc corrector to propose corrections for ML models. |
| Outcome: | The proposed framework improves the performance of a number of models by up to 39% on text analysis and the challenging molecular predictions. |