Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks122 from public data
- CodeBERT: A Pre-Trained Model for Programming and Natural Languages4,421
This work develops CodeBERT with Transformer-based neural architecture, and trains it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators.
- A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions3,965
A thorough overview of hallucination detection methods and benchmarks is presented and the promising research directions on LLM hallucinations are highlighted, including hallucination in large vision-language models and understanding of knowledge boundaries in LLM hallucinations.
- Effective LSTMs for Target-Dependent Sentiment Classification1,010
Two target dependent long short-term memory models, where target information is automatically taken into account, are developed, which achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.
- A language-independent neural network for event detection282
A language-independent neural network is developed to capture both sequence and chunk information from specific contexts and use them to train an event detector for multiple languages without any manually encoded features.
- 141
- A Survey on Dialogue Summarization: Recent Advances and New Frontiers127
A thorough review of dialogue summarization according to the characteristics of each domain, covering meeting, chat, email thread, customer service and medical dialogue, as well as an overview of publicly available research datasets.
- Effective Deep Memory Networks for Distant Supervised Relation Extraction122
This paper introduces a novel neural approach for distant supervised RE with special focus on attention mechanisms, which includes two major attention-based memory components, which are capable of explicitly capturing the importance of each context word for modeling the representation of the entity pair.
- Target-Dependent Sentiment Classification with Long Short Term Memory.122
Two target dependent long short-term memory models, where target information is automatically taken into account, are developed, which achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.
- RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness115
RLAIF-V maximally explores open-source MLLMs from two perspectives, including high-quality feedback data generation for preference learning and self-feedback guidance for inference-time scaling, and reveals the self-alignment potential of open-source MLLMs, where the model can learn from feedback of itself to achieve super GPT-4V trustworthiness.
- Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization114
DialoGPT, a pre-trained model for conversational response generation, can be developed as an unsupervised dialogue annotator, which takes advantage of dialogue background knowledge encoded in DialoG PT.
- TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching110
This work proposes TableGPT, a model that outperforms existing systems on most few-shot settings for table-to-text generation and exploits multi-task learning with two auxiliary tasks that preserve table’s structural information by reconstructing the structure from GPT-2's representation and improving the text's fidelity with content matching task.
- Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration94
A training-free ensemble framework DeePEn, fusing the informative probability distributions yielded by different LLMs at each decoding step, which achieves consistent improvements across six benchmarks covering subject examination, reasoning, and knowledge.
- Semantic Parsing with Syntax- and Table-Aware SQL Generation83
The quality of the generated SQL query is significantly improved through learning to replicate content from column names, cells or SQL keywords; and improving the generation of WHERE clause by leveraging the column-cell relation.
- Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization81
A Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations and a relational graph encoder, where the utterances and discourse relations are modeled in a graph interaction manner.
- Topic-to-Essay Generation with Neural Networks72
A multi-topic aware long short-term memory network that learns the weight of each topic and is sequentially updated during the decoding process and has the ability to generate essays that are not only coherent but also closely related to the input topics.
- Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)65
This work develops a table cell fusion gate to combine representations from row, column and time dimension into one dense vector according to the saliency of each dimension’s representation.
- Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer65
A lexicon extension strategy is designed to address out-of lexicon problem by automatically learning semantic projections and regard word-level entity type distribution features as an external language-independent knowledge and incorporate them into the neural architecture.
- 61
- Retrieval-Generation Synergy Augmented Large Language Models58
This work proposes an iterative retrieval-generation collaborative framework that is not only able to leverage both parametric and non-parametric knowledge, but also helps to find the correct reasoning path through retrieval- generation interactions, which is very important for tasks that require multi-step reasoning.
- Adaptive Multi-pass Decoder for Neural Machine Translation57
A novel architecture called adaptive multi-pass decoder is proposed, which introduces a flexible multi- pass polishing mechanism to extend the capacity of NMT via reinforcement learning and adopts an extra policy network to automatically choose a suitable and effective number of decoding passes.
- Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks56
A novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue un-derstanding and summary generation and a Dialogue Heterogeneous Graph Network for modeling both information.
- Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications55
A review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications is proposed, with the intention of inspiring future research endeavors.
- Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding46
A survey of extrapolatable PEs that have dominated this research field, covering position interpolation and randomized position methods, aims to enable the reader to gain a deep understanding of existing methods and provide stimuli for future research.
- 42
- Aligning Semantic in Brain and Language: A Curriculum Contrastive Method for Electroencephalography-to-Text Generation37
A Curriculum Semantic-aware Contrastive Learning strategy (C- SCL), which effectively recalibrates the subject-dependent EEG representation to the semantic- dependent EEG representation, thereby reducing the discrepancy.
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 2026-10-14. 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.