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Works155 from public data
- 🧜Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models1,188
This paper presents taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyzes existing approaches aiming at mitigating LLm hallucination, and discusses potential directions for future research.
- Document-Level Machine Translation with Large Language Models219
An in-depth evaluation of LLMs' ability on discourse modeling using document-level machine translation (MT) as a testbed finds that LLMs have demonstrated superior performance and show potential to become a new paradigm for document- level translation.
- MAGE: Machine-generated Text Detection in the Wild218
A comprehensive testbed is built by gathering texts from diverse human writings and texts generated by different LLMs, showing challenges in distinguishing machine-generated texts from human-authored ones across various scenarios, especially out-of-distribution.
- Exploiting Cross-Sentence Context for Neural Machine Translation209
This paper proposes a cross-sentence context-aware approach and investigates the influence of historical contextual information on the performance of neural machine translation (NMT).
- Uni-MoE: Scaling Unified Multimodal LLMs With Mixture of Experts180
This work presents the pioneering attempt to develop a unified MLLM with the MoE architecture, named Uni-MoE that can handle a wide array of modalities and implements a sparse MoE architecture within the LLMs to enable efficient training and inference through modality-level data parallelism and expert-level model parallelism.
- Convolutional Self-Attention Networks137
Novel convolutional self-attention networks are proposed, which offer SANs the abilities to strengthen dependencies among neighboring elements, and model the interaction between features extracted by multiple attention heads.
- DrugAssist: a large language model for molecule optimization107
The proposed DrugAssist is an interactive molecule optimization model which performs optimization through human–machine dialogue by leveraging LLM’s strong interactivity and generalizability, simultaneously showcasing immense potential in transferability and iterative optimization.
- Modeling Recurrence for Transformer87
This work proposes to directly model recurrence for Transformer with an additional recurrence encoder, and introduces a novel attentive recurrent network to leverage the strengths of both attention models and recurrent networks.
- Interactive Story Visualization with Multiple Characters84
A system for generic interactive story visualization, capable of handling multiple novel characters and supporting the editing of layout and local structure is proposed, developed by leveraging the prior knowledge of large language and T2I models, trained on massive corpora.
- Self-Attention with Structural Position Representations78
This work uses dependency tree to represent the grammatical structure of a sentence, and proposes two strategies to encode the positional relationships among words in the dependency tree.
- Interformer: an interaction-aware model for protein-ligand docking and affinity prediction68
Interformer, a generative deep learning model, enhances protein-ligand docking accuracy and generalizability by capturing essential non-covalent interactions and demonstrates its practical value in real-world drug design by reasonably ranking ligand affinity through a contrastive learning strategy.
- Context-aware Self-Attention Networks for Natural Language Processing67
This work casts local context modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention and leverage the internal representations that embed sentence-level information as the global context to contextualize the transformations of query and key layers.
- Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement59
This paper proposes to use routing-by-agreement strategies to aggregate layers dynamically and shows that the proposed approach consistently outperforms the strong baseline model and a representative static aggregation model.
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- Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation55
This work directly exposes the raw data into NAT by leveraging pretraining to rejuvenate more alignments for low-frequency target words, and combines these complementary approaches as a new training strategy for further boosting NAT performance.
- Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models51
This study revisits six core challenges of Neural Machine Translation, offering insights into their ongoing relevance in the context of advanced Large Language Models, and identifies three new challenges in LLM-based translation: inference efficiency, translation of low-resource languages during pretraining, and human-aligned evaluation.
- A Comprehensive Study of GPT-4V’s Multimodal Capabilities in Medical Imaging51
This paper presents a comprehensive evaluation of GPT-4V's capabilities across diverse medical imaging tasks, including Radiology Report Generation, Medical Visual Question Answering (VQA), and Visual Grounding, and finds the limitations of conventional evaluation metrics like the BLEU score.
- Translating Pro-Drop Languages With Reconstruction Models51
Experimental results on both Chinese-English and Japanese-English dialogue translation tasks show that the proposed approach significantly and consistently improves translation performance over a strong NMT baseline, which is directly built on the training data annotated with DPs.
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- Progressive Multi-Granularity Training for Non-Autoregressive Translation47
This study empirically shows that NAT models are prone to learn fine-grained lower-mode knowledge, such as words and phrases, compared with sentences, and proposes progressive multi-granularity training for NAT, which improves phrase translation accuracy and model reordering ability, resulting in better translation quality against strong NAT baselines.
- How Does Selective Mechanism Improve Self-Attention Networks?46
Assessing the strengths of selective SANs (SSANs), which are implemented with a flexible and universal Gumbel-Softmax, shows that SSANs consistently outperform the standard SANs.
- Towards Understanding Neural Machine Translation with Word Importance46
This work measures the word importance by attributing the NMT output to every input word through a gradient-based method, and reveals that words of certain syntactic categories have higher importance while the categories vary across language pairs, which can inspire better design principles of NMT architectures for multi-lingual translation.
- A Novel Approach to Dropped Pronoun Translation44
This work proposes a semisupervised approach to recall possibly missing pronouns in the translation of Dropped Pronouns by both extracting rules from the DP-labelled training data and translating theDP-generated input sentences.
- Context-Aware Cross-Attention for Non-Autoregressive Translation43
Experimental results show that this approach can consistently improve translation quality over strong NAT baselines and extensive analyses demonstrate that the enhanced cross-attention achieves better exploitation of source contexts by leveraging both local and global information.
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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