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Found 39 papers, 19 papers with code
Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data
In this paper, we introduce the image-centric multi-annotation X-ray dataset (IMAX), the first attempt to enhance the multi-task learning capabilities of medical multi-modal large language models (MLLMs) from the data construction level.
MedM-VL: What Makes a Good Medical LVLM?
To support reproducibility and future research, we release a modular codebase, MedM-VL, and two pre-trained models: MedM-VL-2D for 2D medical image analysis and MedM-VL-CT-Chest for 3D CT-based applications.
Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety
Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit weaknesses in traditional safety alignment, which often relies on rigid refusal heuristics or representation engineering to block harmful outputs.
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training
Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks.
Connector-S: A Survey of Connectors in Multi-modal Large Language Models
With the rapid advancements in multi-modal large language models (MLLMs), connectors play a pivotal role in bridging diverse modalities and enhancing model performance.
Aspect-Aware Decomposition for Opinion Summarization
Opinion summarization plays a key role in deriving meaningful insights from large-scale online reviews.
Grade Inflation in Generative Models
We show that the correlation score, Jaccard score, earth-mover's score, and Kullback-Leibler (relative-entropy) score all suffer grade inflation.
Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation
Language-conditioned robotic learning has significantly enhanced robot adaptability by enabling a single model to execute diverse tasks in response to verbal commands.
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model
To the best of our knowledge, Med-2E3 is the first MLLM to integrate both 3D and 2D features for 3D medical image analysis.
Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
To mitigate the tug-of-war problem of multi-modal multi-task optimization in MLLMs, recent advances primarily focus on improving the LLM components, while neglecting the connector that bridges the gap between modalities.
Enhancing elusive clues in knowledge learning by contrasting attention of language models
Causal language models acquire vast amount of knowledge from general text corpus during pretraining, but the efficiency of knowledge learning is known to be unsatisfactory, especially when learning from knowledge-dense and small-sized corpora.
Co-occurrence is not Factual Association in Language Models
Pretrained language models can encode a large amount of knowledge and utilize it for various reasoning tasks, yet they can still struggle to learn novel factual knowledge effectively from finetuning on limited textual demonstrations.
The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results
In this paper, we briefly summarize the first competition on resource-limited infrared small target detection (namely, LimitIRSTD).
Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines
Based on the proposed RGBT-Tiny dataset and SAFit measure, extensive evaluations have been conducted, including 23 recent state-of-the-art algorithms that cover four different types (i. e., visible generic detection, visible SOD, thermal SOD and RGBT object detection).
Conditional Language Learning with Context
In this fashion, conditional finetuning achieves selective learning from a corpus, learning knowledge useful for downstream tasks while avoiding learning useless corpus statistics like topic biases.
TinyLLaVA Factory: A Modularized Codebase for Small-scale Large Multimodal Models
We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results.
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism
We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
A Sentiment Consolidation Framework for Meta-Review Generation
Modern natural language generation systems with Large Language Models (LLMs) exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain if they truly possess the capability of information consolidation to generate summaries, especially on documents with opinionated information.
Neural Network-Based Histologic Remission Prediction In Ulcerative Colitis
A total of 5105 images of 154 intestinal segments from 87 patients undergoing EC treatment at a center in China between March 2022 and March 2023 are scored according to the Geboes score.
Learning Autonomous Ultrasound via Latent Task Representation and Robotic Skills Adaptation
As medical ultrasound is becoming a prevailing examination approach nowadays, robotic ultrasound systems can facilitate the scanning process and prevent professional sonographers from repetitive and tedious work.
Privacy Risks in Reinforcement Learning for Household Robots
The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, owing to the remarkable advances in computer vision and large language models.
THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval
The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification.
Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation
We present PeerSum, a novel dataset for generating meta-reviews of scientific papers.
DeltaScore: Fine-Grained Story Evaluation with Perturbations
Numerous evaluation metrics have been developed for natural language generation tasks, but their effectiveness in evaluating stories is limited as they are not specifically tailored to assess intricate aspects of storytelling, such as fluency and interestingness.
Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization
We propose HGSUM, an MDS model that extends an encoder-decoder architecture, to incorporate a heterogeneous graph to represent different semantic units (e. g., words and sentences) of the documents.
Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects
This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system.
PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization
We present PeerSum, a new MDS dataset using peer reviews of scientific publications.
Learning Ultrasound Scanning Skills from Human Demonstrations
Finally, the robustness of the proposed framework is validated with the experiments on real data from sonographers.
Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations
Medical ultrasound has become a routine examination approach nowadays and is widely adopted for different medical applications, so it is desired to have a robotic ultrasound system to perform the ultrasound scanning autonomously.
Dense Nested Attention Network for Infrared Small Target Detection
With the repeated interaction in DNIM, infrared small targets in deep layers can be maintained.
Domain-Specific Suppression for Adaptive Object Detection
The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one.
A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning
In the last few years, the interest in knowledge bases has grown exponentially in both the research community and the industry due to their essential role in AI applications.
Blind Inverse Gamma Correction with Maximized Differential Entropy
Unwanted nonlinear gamma distortion frequently occurs in a great diversity of images during the procedures of image acquisition, processing, and/or display.
The SPPD System for Schema Guided Dialogue State Tracking Challenge
This paper introduces one of our group's work on the Dialog System Technology Challenges 8 (DSTC8), the SPPD system for Schema Guided dialogue state tracking challenge.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features
This model captures structural features by a sequential variational autoencoder component and leverages a topic modeling component based on Gaussian distribution to enhance the recognition of text semantics.
A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution
A Song-On-Demand task, with a total of 38117 songs and 12 attributes corresponding to each song, is used to test the performance of the proposed approach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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