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Works86 from public data
- Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing2,830
It is shown that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
- Language Models for Image Captioning: The Quirks and What Works293
By combining key aspects of the ME and RNN methods, this paper achieves a new record performance over previously published results on the benchmark COCO dataset, however, the gains the authors see in BLEU do not translate to human judgments.
- Augmenting Language Models with Long-Term Memory263
Experiments show that the proposed framework, Language Models Augmented with Long-Term Memory (LongMem), outperforms strong long-context models on ChapterBreak, a challenging long- context modeling benchmark, and achieves remarkable improvements on memory-augmented in-context learning over LLMs.
- Fine-tuning large neural language models for biomedical natural language processing203
Overall, domain-specific vocabulary and pretraining facilitate robust models for fine-tuning and establish a new state of the art on a wide range of biomedical NLP applications.
- Dialogue State Tracking with a Language Model using Schema-Driven Prompting151
A new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots is introduced.
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- Self-Verification Improves Few-Shot Clinical Information Extraction85
This work explores a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs and consistently improves accuracy for various LLMs in standard clinical information extraction tasks.
- Constrained locally weighted clustering75
The theoretical analysis shows that the probability of points being assigned to the correct clusters is much higher by the new algorithm, compared to the conventional methods, indicating that the design produces clusters which are closer to the ground truth than clusters created by the current state of theart algorithms.
- ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning67
ExACT, an approach to combine test-time search and self-learning to build o1-like models for agentic applications, is presented, suggesting a promising research direction to enhance VLMs' capabilities for agentic applications via test-time search and self-learning.
- UnitedQA: A Hybrid Approach for Open Domain Question Answering67
It is demonstrated that a simple hybrid approach by combining answers from both readers can efficiently take advantages of extractive and generative answer inference strategies and outperforms single models as well as homogeneous ensembles.
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- The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding63
MT-DNN is presented, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models, designed to facilitate rapid customization for a broad spectrum of NLU tasks.
- Sounding Board: A User-Centric and Content-Driven Social Chatbot61
The system architecture consists of several components including spoken language processing, dialogue management, language generation, and content management, with emphasis on user-centric and content-driven design.
- Disentangled Feature Representation for Few-Shot Image Classification57
This work proposes a novel disentangled feature representation (DFR) framework, dubbed DFR, for few-shot learning applications, which can adaptively decouple the discriminative features that are modeled by the classification branch, from the class-irrelevant component of the variation branch.
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- Open-Domain Name Error Detection using a Multi-Task RNN48
A multi-task recurrent neural network language model for sentence-level name detection is proposed for use in combination with out-of-vocabulary word detection, which shows a 26% improvement in name-error detection F-score over a system using n-gram lexical features.
- Bi-directional Attention with Agreement for Dependency Parsing47
A novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions, which achieves state-of-the-art unlabeled attachment scores on 6 languages.
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- A non-linear dimensionality-reduction technique for fast similarity search in large databases36
A non-linear transformation scheme that extracts two important parameters specifying the data is presented, and it is proved that these parameters correspond to a bounded volume around the search sphere, irrespective of dimensionality.
- Graph Neural Networks With Triple Attention for Few-Shot Learning33
The proposed Attentive GNN model outperforms the state-of-the-art few-shot learning methods using both GNN and non-GNN approaches and is consistent over the mini-Image net, tiered-ImageNet, CUB-200-2011, and Flowers-102 benchmarks.
- Posterior Differential Regularization with f-divergence for Improving Model Robustness33
It is shown that regularizing the posterior difference with f-divergence can result in well-improved model robustness, indicating the great potential of the proposed framework for enhancing NLPmodel robustness.
- Reassessing Layer Pruning in LLMs: New Insights and Methods30
The results demonstrate that a simple approach, i.e., pruning the final 25\% of layers followed by fine-tuning the remaining last three layer, yields remarkably strong performance.
- Scalable and Sound Low-Rank Tensor Learning28
This work proposes directly optimizing the tensor trace norm by approximating its dual spectral norm, and shows that the approximation bounds can be efficiently converted to the original problem via the generalized conditional gradient algorithm.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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