Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks77 from public data
- Large language models for generative information extraction: a survey507
This study surveys the most recent advancements in generative Large Language Models efforts for IE tasks and identifies several insights in technique and promising research directions that deserve further exploration in future studies.
- DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation136
The proposed DISC-MedLLM is a comprehensive solution that leverages Large Language Models (LLMs) to provide accurate and truthful medical response in end-to-end conversational healthcare services, surpassing existing medical LLMs in both single-turn and multi-turn consultation scenarios.
- Model Compression and Efficient Inference for Large Language Models: A Survey110
This paper investigates compression and efficient inference methods for large language models from an algorithmic perspective, and distinguishes large language models into medium models and ``real'' large models.
- 106
- A benchmark for automatic medical consultation system: frameworks, tasks and datasets93
This article creates a new large medical dialogue dataset with multi-level fine-grained annotations and establishes five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy.
- DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning85
This work proposes Multiple Experts Fine-tuning Framework, a methodology that improves general LLMs by endowing them with multi-turn question answering abilities, domain text processing capabilities, mathematical computation skills, and retrieval-enhanced generation capabilities.
- Water-Soluble Degradable Hyperbranched Polyesters: Novel Candidates for Drug Delivery?82
A novel approach to hyperbranched polymers is presented in this work, where addition of diethanolamine or N-methyl-d-glucamine to methyl acrylate (AB) generates dominantly AD(n)-type intermediates.
- CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation81
A multi-factor hierarchical framework, CoMAE, is proposed, which models the above three key factors of empathy expression in a hierarchical way and can generate more empathetic responses than previous methods.
- Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning78
This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs) by conceptualizing this transformation as a domain adaptation process, i.e., transitioning from text understanding to embracing multiple modalities, and demonstrates that selectively tuning only with conversational data can improve efficiency further.
- Towards an Online Empathetic Chatbot with Emotion Causes62
The proposed empathetic chatbot is developed and compared with several SOTA methods using automatic metrics, expert-based human judgements as well as user-based online evaluation to verify the effectiveness of the proposed approach.
- DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation59
A new dialog pre-training framework called DialogVED is proposed, which introduces continuous latent variables into the enhanced encoder-decoder pre- training framework to increase the relevance and diversity of responses.
- Test of the Universal Scaling Law of Diffusion in Colloidal Monolayers51
It is found that the universal scaling law with α=1 holds well for highly charged polystyrene spheres suspended at an air-water interface, where the strong electrostatic interactions play a dominant role.
- Evidence of nematic order and nodal superconducting gap along [110] direction in RbFe2As249
A scanning tunneling microscopy study on RbFe2As2, a heavily hole-doped Fe-based superconductor (FeSC), suggests a novel (π, π) nematic phase emerging, suggesting an intimate connection between nematicity and superconducting pairing in iron-basedsuperconductors.
- Adapting Translation Models for Transcript Disfluency Detection46
This paper proposes a fast non-autoregressive TDD model following the non- autoregressive NMT model emerged recently, and shows that the proposed model significantly outperforms previous state-of-the-art models.
- Enhanced probabilistic analytical target cascading with application to multi-scale design37
The EPATC approach outperforms the original PATC by providing more accurate optimal solutions and is shown to be more effective and efficient than the current PATC approach.
- Hierarchical reinforcement learning for automatic disease diagnosis32
Inspired by the offline consultation process, this work proposes to integrate a hierarchical policy structure of two levels into the dialogue system for policy learning that achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems.
- DxFormer: a decoupled automatic diagnostic system based on decoder–encoder transformer with dense symptom representations25
A new automatic diagnostic framework called DxFormer is proposed, which decouples symptom inquiry and disease diagnosis, so that these two modules can be independently optimized and achieves the state-of-the-art diagnostic accuracy.
- 22
- EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning19
An Entity-Agnostic Representation Learning (EARL) method to introduce knowledge graphs to informative conversation generation and demonstrates that this model can generate more informative, coherent, and natural responses than baseline models.
- 18
- EnsembleGAN18
This paper proposes ensembleGAN, an adversarial learning framework for enhancing a retrieval-generation ensemble model in open-domain conversation scenario that consists of a language-model-like generator, a ranker generator, and one ranker discriminator.
- End-to-End Conversational Search for Online Shopping with Utterance Transfer16
ConvSearch is proposed, an end-to-end conversational search system that deeply combines the dialog system with search, which leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone.
- Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension15
This paper proposes a joint framework that unifies these two tasks, sharing the same encoder to extract the common and task-invariant features with different decoders to learn task-specific features, and augments the Transformer architecture with a memory updater, designed to selectively store and update the history dialog information so as to support downstream tasks.
- Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations14
This paper proposes a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations and shows that the proposed method achieves huge improvement over all evaluation metrics compared with traditional baseline methods.
- Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models13
This study evaluates the instruction-following capabilities of various retrieval models beyond content relevance, including LLM-based dense retrieval and reranking models and develops InfoSearch, a novel retrieval evaluation benchmark spanning six document-level attributes, and introduces novel metrics.
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 2026-10-15. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.