Search Results for author:
Found 14 papers, 10 papers with code
PersonaMath: Enhancing Math Reasoning through Persona-Driven Data Augmentation
To bridge this gap, we propose a data augmentation approach and introduce PersonaMathQA, a dataset derived from MATH and GSM8K, on which we train the PersonaMath models.
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models
Specifically, Ruler equips LLMs with the ability to generate responses of a specified length based on length constraints within the instructions.
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct
This framework iteratively improve data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution, generating a more complex and diverse image-text instruction dataset that empowers MLLMs with enhanced capabilities.
Autogenic Language Embedding for Coherent Point Tracking
Point tracking is a challenging task in computer vision, aiming to establish point-wise correspondence across long video sequences.
Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs
We applied the HCP strategy in experiments with six Repo-Code LLMs, and the results demonstrate that our proposed method can significantly enhance completion accuracy while substantially reducing the length of input.
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
Long-context modeling capabilities have garnered widespread attention, leading to the emergence of Large Language Models (LLMs) with ultra-context windows.
Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models
In this study, we propose a data mining framework \textbf{ProLong} that can assign each training sample with a long dependency score, which can be used to rank and filter samples that are more advantageous for enhancing long-context modeling abilities in LLM training.
DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception
The development of large language models (LLMs) has significantly advanced the emergence of large multimodal models (LMMs).
Marathon: A Race Through the Realm of Long Context with Large Language Models
With the advancement of large language models (LLMs) and the expansion of their context windows, existing long-context benchmarks fall short in effectively evaluating the models' comprehension and reasoning abilities in extended texts.
VDialogUE: A Unified Evaluation Benchmark for Visually-grounded Dialogue
Visually-grounded dialog systems, which integrate multiple modes of communication such as text and visual inputs, have become an increasingly popular area of investigation.
DiffusionTrack: Diffusion Model For Multi-Object Tracking
In inference, the model refines a set of paired randomly generated boxes to the detection and tracking results in a flexible one-step or multi-step denoising diffusion process.
Compact Transformer Tracker with Correlative Masked Modeling
Transformer framework has been showing superior performances in visual object tracking for its great strength in information aggregation across the template and search image with the well-known attention mechanism.
VariabilityTrack:Multi-Object Tracking with Variable Speed Object Movement
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos.
