Search Results for author:
Found 25 papers, 10 papers with code
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution
Large Language Models (LLMs) require precise alignment with complex instructions to optimize their performance in real-world applications.
LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features
Accurate segmentation of rectal lymph nodes is crucial for the staging and treatment planning of rectal cancer.
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants
The effective alignment of Large Language Models (LLMs) with precise instructions is essential for their application in diverse real-world scenarios.
Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation
The CoSAM utilizes sequence-based detection to guide the segmentation of metastatic lymph nodes in rectal cancer, contributing to improved localization performance for the segmentation model.
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback
Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF training, we observe an increase in human evaluators' preference win ratio for model responses, but a decrease in evaluators' satisfaction rate.
SIFiD: Reassess Summary Factual Inconsistency Detection with LLM
Ensuring factual consistency between the summary and the original document is paramount in summarization tasks.
Generalizable Entity Grounding via Assistance of Large Language Model
In this work, we propose a novel approach to densely ground visual entities from a long caption.
Instruction Fusion: Advancing Prompt Evolution through Hybridization
The fine-tuning of Large Language Models (LLMs) specialized in code generation has seen notable advancements through the use of open-domain coding queries.
UniGS: Unified Representation for Image Generation and Segmentation
On the other hand, the progressive dichotomy module can efficiently decode the synthesized colormap to high-quality entity-level masks in a depth-first binary search without knowing the cluster numbers.
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation
The generalization of the model is further verified on the WORD dataset.
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis leverages multimodal signals to detect the sentiment of a speaker.
AIMS: All-Inclusive Multi-Level Segmentation
Despite the progress of image segmentation for accurate visual entity segmentation, completing the diverse requirements of image editing applications for different-level region-of-interest selections remains unsolved.
TCR: Short Video Title Generation and Cover Selection with Attention Refinement
In this paper, we first collect and present a real-world dataset named Short Video Title Generation (SVTG) that contains videos with appealing titles and covers.
GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation
Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i. e., long and informative commentary about the domain-specific scenes with appropriate reasoning) is still far from being solved, which however has great applications such as automatic sports narrative.
High Quality Entity Segmentation
Given the high-quality and -resolution nature of the dataset, we propose CropFormer which is designed to tackle the intractability of instance-level segmentation on high-resolution images.
Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes
Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition.
High-Quality Entity Segmentation
It improves mask prediction by fusing high-res image crops that provide more fine-grained image details and the full image.
Contrastive Learning enhanced Author-Style Headline Generation
In this paper, we propose a novel Seq2Seq model called CLH3G (Contrastive Learning enhanced Historical Headlines based Headline Generation) which can use the historical headlines of the articles that the author wrote in the past to improve the headline generation of current articles.
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation
In this paper, we propose the Tencent-MVSE dataset, which is the first benchmark dataset for the multi-modal video similarity evaluation task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation
To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either task-unrelated or task-specific training signals from unlabeled data.
TAG: Toward Accurate Social Media Content Tagging with a Concept Graph
Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept phrases to characterize the main idea of a text snippet on the fast-growing social media.
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization
Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performance gains on a diverse range of Natural Language Understanding (NLU) tasks.
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications
In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49, 000+ data samples for the task of Chinese query-based document summarization.
GIANT: Scalable Creation of a Web-scale Ontology
These services will benefit from a highly structured and web-scale ontology of entities, concepts, events, topics and categories.
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
We further present our techniques to tag documents with user-centered concepts and to construct a topic-concept-instance taxonomy, which has helped to improve search as well as news feeds recommendation in Tencent QQ Browser.
