Search Results for author:
Found 8 papers, 5 papers with code
AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization
To address this, we propose a novel two-stage framework for adaptive and efficient reasoning.
Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation
Mainstream defenses aim to vaccinate the model such that the later harmful fine-tuning attack is less effective.
O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning
Experiments on various mathematical reasoning benchmarks show that O1-Pruner not only significantly reduces inference overhead but also achieves higher accuracy, providing a novel and promising solution to this challenge.
Latent Distance Guided Alignment Training for Large Language Models
Consequently, we utilize the distance between sample pairs in the latent space to guide DPO-based alignment training.
LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
(2) We develop LLM Reasoners, a library for standardized modular implementation of existing and new reasoning algorithms, under a unified formulation of the search, reward, and world model components.
Vector-Quantized Prompt Learning for Paraphrase Generation
Therefore, we present vector-quantized prompts as the cues to control the generation of pre-trained models.
ProSG: Using Prompt Synthetic Gradients to Alleviate Prompt Forgetting of RNN-like Language Models
RNN-like language models are getting renewed attention from NLP researchers in recent years and several models have made significant progress, which demonstrates performance comparable to traditional transformers.
PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large language models (LLMs) and the intricacies of the target task.
