Search Results for author:
Found 13 papers, 12 papers with code
Mask-DPO: Generalizable Fine-grained Factuality Alignment of LLMs
We further study the generalization property of Mask-DPO using different training sample scaling strategies and find that scaling the number of topics in the dataset is more effective than the number of questions.
Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning
To alleviate the long-existing difficulties brought by sparse rewards in RL, which are even exacerbated by the partial correctness of the long chain of thought for reasoning tasks, we further apply a token-level reward model to sample important tokens in reasoning trajectories for learning.
Training Language Models to Critique With Multi-agent Feedback
To overcome these challenges, this paper proposes a novel data generation pipeline, named MultiCritique, that improves the critique ability of LLMs by utilizing multi-agent feedback in both the SFT and reinforcement learning (RL) stages.
ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models
Such an annotator can not only evaluate the hallucination levels of various LLMs on the large-scale dataset but also help to mitigate the hallucination of LLMs generations, with the Natural Language Inference (NLI) metric increasing from 25% to 37% on HaluEval.
ANAH: Analytical Annotation of Hallucinations in Large Language Models
Reducing the `$\textit{hallucination}$' problem of Large Language Models (LLMs) is crucial for their wide applications.
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.
Fake Alignment: Are LLMs Really Aligned Well?
The growing awareness of safety concerns in large language models (LLMs) has sparked considerable interest in the evaluation of safety.
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.
Dense Distinct Query for End-to-End Object Detection
Concretely, we first lay dense queries like traditional detectors and then select distinct ones for one-to-one assignments.
Ranked #4 on
Object Detection
on CrowdHuman (full body)
RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose
Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency.
Ranked #1 on
2D Human Pose Estimation
on Human-Art
(Validation AP metric)
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
Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection
In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD).
MMRotate: A Rotated Object Detection Benchmark using PyTorch
We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning.
