Search Results for author:
Found 14 papers, 11 papers with code
Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs
Large Language Models (LLMs) can correct their self-generated responses, but a decline in accuracy after self-correction is also witnessed.
PyBench: Evaluating LLM Agent on various real-world coding tasks
The LLM Agent, equipped with a code interpreter, is capable of automatically solving real-world coding tasks, such as data analysis and image editing.
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
Open-source Large Language Models (LLMs) and their specialized variants, particularly Code LLMs, have recently delivered impressive performance.
Exploring Activation Patterns of Parameters in Language Models
Our work sheds light on the behavior of parameter activation in LLMs, and we hope these findings will have the potential to inspire more practical applications.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Code Needs Comments: Enhancing Code LLMs with Comment Augmentation
The programming skill is one crucial ability for Large Language Models (LLMs), necessitating a deep understanding of programming languages (PLs) and their correlation with natural languages (NLs).
InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
We further explore how to use LEAN to solve math problems and study its performance under the setting of multi-task learning which shows the possibility of using LEAN as a unified platform for solving and proving in math.
Is Underwater Image Enhancement All Object Detectors Need?
Coupled with 7 object detection models retrained using raw underwater images, we employ these 133 models to comprehensively analyze the effect of underwater image enhancement on underwater object detection.
MultiModal-GPT: A Vision and Language Model for Dialogue with Humans
To further enhance the ability to chat with humans of the MultiModal-GPT, we utilize language-only instruction-following data to train the MultiModal-GPT jointly.
A Challenging Benchmark for Low-Resource Learning
Experiments on a wide range of models show that neural networks, even pre-trained language models, have sharp performance drops on our benchmark, demonstrating the effectiveness on evaluating the weaknesses of neural networks.
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection.
Ranked #1 on
Object Detection In Aerial Images
on DOTA 1.0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism
However, with heterogeneous syndromes and distinct phenotypes, DL models trained with CTs from one data center fail to generalize on images from another center.
UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space
However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation.
