Search Results for author:
Found 9 papers, 5 papers with code
LeanProgress: Guiding Search for Neural Theorem Proving via Proof Progress Prediction
Our experiments show that LeanProgress achieves an overall prediction accuracy of 75. 1\% in predicting the amount of progress and, hence, the remaining number of steps.
LeanAgent: Lifelong Learning for Formal Theorem Proving
We present LeanAgent, a novel lifelong learning framework for formal theorem proving that continuously generalizes to and improves on ever-expanding mathematical knowledge without forgetting previously learned knowledge.
Creative and Context-Aware Translation of East Asian Idioms with GPT-4
As a type of figurative language, an East Asian idiom condenses rich cultural background into only a few characters.
In-Context Learning May Not Elicit Trustworthy Reasoning: A-Not-B Errors in Pretrained Language Models
Recent advancements in artificial intelligence have led to the creation of highly capable large language models (LLMs) that can perform tasks in a human-like manner.
Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean
Neural theorem proving combines large language models (LLMs) with proof assistants such as Lean, where the correctness of formal proofs can be rigorously verified, leaving no room for hallucination.
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
Using this data, we develop ReProver (Retrieval-Augmented Prover): an LLM-based prover augmented with retrieval for selecting premises from a vast math library.
User Pairing and Power Allocation for FTN-based SC-NOMA and MIMO-NOMA Systems Considering User Fairness
As far as we know, this paper is the first solution to the issue of user pairing and power allocation in FTN-based NOMA, which proves the great advantage of the combination of these two state-of-the-art technologies.
For Intelligent and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-than-Nyquist and Deep Learning
With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable.
Receiver Design for Faster-than-Nyquist Signaling: Deep-learning-based Architectures
Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers.
