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- Alex WaibelSuggested from co-authorship
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Works252 from public data
- Qwen2.5 Technical Report5,408
The open-weight flagship Qwen2.5-72B-Instruct outperforms a number of open and proprietary models and demonstrates competitive performance to the state-of-the-art open-weight model, Llama-3-405B-Instruct, which is around 5 times larger.
- Qwen Technical Report4,443
Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts, and includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques.
- Qwen2 Technical Report2,548
A comprehensive suite of foundational and instruction-tuned language models, encompassing a parameter range from 0.5 to 72 billion, featuring dense models and a Mixture-of-Experts model, which surpasses most prior open-weight models and demonstrates robust multilingual capabilities.
- 747
- Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis531
This paper introduces a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE), and proposes a two-stage framework to address this task, which outperforms a few strong baselines adapted from state-of-the-art related methods.
- KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction518
A Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt) that injects latent knowledge contained in relation labels into prompt construction with learnable virtual type words and answer words.
- A Joint Neural Model for Information Extraction with Global Features481
A joint neural framework that aims to extract the globally optimal IE result as a graph from an input sentence and can be easily applied to new languages or trained in a multilingual manner, as OneIE does not use any language-specific feature.
- Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning288
This work exploits large language models (LLMs) as decomposers for effective table-based reasoning, and proposes a novel ''parsing-execution-filling'' strategy to decompose a complex question into simper step-by-step sub-questions by generating intermediate SQL queries as a bridge.
- A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation269
A knowledge-enhanced pretraining model to utilize commonsense knowledge from external knowledge bases to generate reasonable stories that can generate more reasonable stories than state-of-the-art baselines, particularly in terms of logic and global coherence.
- CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark262
The first Chinese Biomedical Language Understanding Evaluation Evaluation (CBLUE) benchmark is presented: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis.
- Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion260
This paper proposes a hybrid transformer architecture with unified input-output for diverse multimodal knowledge graph completion tasks, and proposes multi-level fusion, which integrates visual and text representation via coarse-grained prefix-guided interaction and fine- grained correlation-aware fusion modules.
- Document-level Relation Extraction as Semantic Segmentation236
A Document U-shaped Network for document-level relation extraction is proposed, which leverages an encoder module to capture the context information of entities and a U- shaped segmentation module over the image-style feature map to capture global interdependency among triples.
- Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning235
A straightforward yet effective fine-tuning technique, Child-Tuning, which updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process is proposed.
- Automated Concatenation of Embeddings for Structured Prediction209
This paper proposes Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search.
- GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-supervised Learning and Explicit Policy Injection187
GALAXY is a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning and has a stronger few-shot ability than existing models under various low-resource settings.
- Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning183
This paper finds empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.
- Hallucination Augmented Contrastive Learning for Multimodal Large Language Model179
This paper introduces contrastive learning into MLLMs and uses text with hallucination as hard negative examples, naturally bringing representations of non-hallucinative text and visual samples closer while pushing way representations of non-hallucinating and hallucinative text.
- StructuralLM: Structural Pre-training for Form Understanding153
This paper proposes a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents, with two new designs to make the most of the interactions of cell and layouts information.
- Contrastive Triple Extraction with Generative Transformer152
This paper introduces a novel model, contrastive triple extraction with a generative transformer module for encoder-decoder-based generation and proposes a novel triplet contrastive training object to generate faithful results.
- Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks145
This work introduces Mobile-Agent-E, a hierarchical multi-agent framework capable of self-evolution through past experience capable of achieving 22% absolute improvement over previous state-of-the-art approaches across three foundation model backbones.
- FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN141
Each track’s goal, data and evaluation metrics are introduced, and the results of the received submissions are reported.
- Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion136
A novel gated and attentive neighbor aggregator is built for accurately integrating the semantics of a few-shot relation's neighborhood, which helps filtering the noise neighbors even if a KG contains extremely sparse neighborhoods.
- Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition135
This work proposes a boundary enhanced neural span classification model that has the ability to generate high-quality candidate spans and greatly reduces the time complexity during inference, and incorporates an additional boundary detection task to predict those words that are boundaries of entities.
- 134
- E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning132
This paper proposes the first end-to-end vision-language pre-trained model for both V+L understanding and generation, namely E2E-VLP, where a unified Transformer framework is built to jointly learn visual representation, and semantic alignments between image and text.
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 2026-10-13. 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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