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Found 24 papers, 11 papers with code
VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
Multimodal Large Language models (MLLMs) have shown promise in web-related tasks, but evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks.
CoUDA: Coherence Evaluation via Unified Data Augmentation
Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models.
Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
This iterative cycle of exploration and training fosters continued improvement in the agents.
MMBaT: A Multi-task Framework for mmWave-based Human Body Reconstruction and Translation Prediction
Human body reconstruction with Millimeter Wave (mmWave) radar point clouds has gained significant interest due to its ability to work in adverse environments and its capacity to mitigate privacy concerns associated with traditional camera-based solutions.
Rationale-Enhanced Language Models are Better Continual Relation Learners
Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations.
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.
Categorizing Flight Paths using Data Visualization and Clustering Methodologies
This work leverages the U. S. Federal Aviation Administration's Traffic Flow Management System dataset and DV8, a recently developed tool for highly interactive visualization of air traffic data, to develop clustering algorithms for categorizing air traffic by their varying flight paths.
PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training
To decouple train length from target length for efficient context window extension, we propose Positional Skip-wisE (PoSE) training that smartly simulates long inputs using a fixed context window.
Advanced Underwater Image Restoration in Complex Illumination Conditions
To validate the effectiveness of our approach, we conducted extensive experiments on simulated and real-world datasets.
Efficient Large-scale AUV-based Visual Seafloor Mapping
Driven by the increasing number of marine data science applications, there is a growing interest in surveying and exploring the vast, uncharted terrain of the deep sea with robotic platforms.
RestGPT: Connecting Large Language Models with Real-World RESTful APIs
To address the practical challenges of tackling complex instructions, we propose RestGPT, which exploits the power of LLMs and conducts a coarse-to-fine online planning mechanism to enhance the abilities of task decomposition and API selection.
RepCL: Exploring Effective Representation for Continual Text Classification
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks.
DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction.
Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?
We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate Python programming courses at the postsecondary level.
Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process.
Calibrating Factual Knowledge in Pretrained Language Models
However, we find that facts stored in the PLMs are not always correct.
ConFiguRe: Exploring Discourse-level Chinese Figures of Speech
Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type.
Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach
2) Balanced Tuning (BT) finetunes the model on the balanced memory data.
Skill requirements in job advertisements: A comparison of skill-categorization methods based on explanatory power in wage regressions
In this paper, we compare different methods to extract skill requirements from job advertisements.
Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances
For each instance in a batch, we involve other instances in the same batch to interact with it.
Marine Bubble Flow Quantification Using Wide-Baseline Stereo Photogrammetry
Beside reporting the steps to make bubble characterization robust and autonomous, we carefully evaluate the reachable accuracy to be in the range of 1-2\% of the bubble radius and propose a novel auto-calibration procedure that, due to the lack of point correspondences, uses only the silhouettes of bubbles.
Robustly Removing Deep Sea Lighting Effects for Visual Mapping of Abyssal Plains
The majority of Earth's surface lies deep in the oceans, where no surface light reaches.
Refractive Geometry for Underwater Domes
Underwater cameras are typically placed behind glass windows to protect them from the water.
