Search Results for author:
Found 7 papers, 3 papers with code
Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing
Apart from previous codebook-based methods that rely on one-shot decoding, our method utilizes high-quality codes obtained in the previous iteration to guide the prediction of the Code-Predictor in the subsequent iteration, improving code prediction accuracy and ensuring stable dehazing performance.
Reward Shaping to Mitigate Reward Hacking in RLHF
Reinforcement Learning from Human Feedback (RLHF) is essential for aligning large language models (LLMs) with human values.
FaceMe: Robust Blind Face Restoration with Personal Identification
In this paper, we propose a personalized face restoration method, FaceMe, based on a diffusion model.
SocialGaze: Improving the Integration of Human Social Norms in Large Language Models
We find that LLMs' understanding of social acceptance is often misaligned with human consensus.
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick
Large language models (LLMs) excellently generate human-like text, but also raise concerns about misuse in fake news and academic dishonesty.
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios
Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality.
KwaiYiiMath: Technical Report
Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning.
Ranked #97 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
