Exploiting Code Symmetries for Learning Program Semantics

• URL: http://arxiv.org/abs/2308.03312v9
• Date: Sun, 8 Sep 2024 17:23:16 GMT
• Title: Exploiting Code Symmetries for Learning Program Semantics
• Authors: Kexin Pei, Weichen Li, Qirui Jin, Shuyang Liu, Scott Geng, Lorenzo Cavallaro, Junfeng Yang, Suman Jana,
• Abstract summary: We introduce a group-theoretic framework that defines code symmetries as semantics-preserving transformations. We develop a novel variant of self-attention that is provably equivariant to code symmetries from the permutation group defined over the program dependence graph. Our results suggest that code LLMs that encode the code structural prior via the code symmetry group generalize better and faster.
• Score: 21.145969030761716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper tackles the challenge of teaching code semantics to Large Language Models (LLMs) for program analysis by incorporating code symmetries into the model architecture. We introduce a group-theoretic framework that defines code symmetries as semantics-preserving transformations, where forming a code symmetry group enables precise and efficient reasoning of code semantics. Our solution, SymC, develops a novel variant of self-attention that is provably equivariant to code symmetries from the permutation group defined over the program dependence graph. SymC obtains superior performance on five program analysis tasks, outperforming state-of-the-art code models without any pre-training. Our results suggest that code LLMs that encode the code structural prior via the code symmetry group generalize better and faster.

Related papers

• SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoning [18.40402135952776]
  This paper presents SymRTLO, a novel neuron-symbolic RTL optimization framework. A symbolic module is proposed for analyzing and optimizing finite state machine (FSM) logic. Experiments on the RTL-Rewriter benchmark with Synopsys Design Compiler and Yosys show that SymRTLO improves power, performance, and area (PPA) by up to 43.9%, 62.5%, and 51.1%, respectively.
  arXiv  Detail & Related papers  (2025-04-14T16:15:55Z)
• Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs [53.00384299879513]
  In large language models (LLMs), code and reasoning reinforce each other. Code provides verifiable execution paths, enforces logical decomposition, and enables runtime validation. We identify key challenges and propose future research directions to strengthen this synergy.
  arXiv  Detail & Related papers  (2025-02-26T18:55:42Z)
• SGC-VQGAN: Towards Complex Scene Representation via Semantic Guided Clustering Codebook [9.993066868670283]
  We introduce SGC-VQGAN through Semantic Online Clustering method to enhance token semantics through Consistent Semantic Learning. Our approach constructs a temporospatially consistent semantic codebook, addressing issues of codebook collapse and imbalanced token semantics.
  arXiv  Detail & Related papers  (2024-09-09T23:12:43Z)
• Can Large Language Models Understand Symbolic Graphics Programs? [136.5639211254501]
  Symbolic graphics programs are popular in computer graphics. We create a benchmark for the semantic visual understanding of symbolic graphics programs. We find that LLMs considered stronger at reasoning generally perform better.
  arXiv  Detail & Related papers  (2024-08-15T17:59:57Z)
• Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models [54.51932175059004]
  We introduce a scalable method for generating synthetic instructions to enhance the code generation capability of Large Language Models. The proposed algorithm, Genetic-Instruct, mimics evolutionary processes, utilizing self-instruction to create numerous synthetic samples from a limited number of seeds.
  arXiv  Detail & Related papers  (2024-07-29T20:42:59Z)
• Case2Code: Scalable Synthetic Data for Code Generation [105.89741089673575]
  Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs. We propose a textbfCase2Code task by exploiting the expressiveness and correctness of programs.
  arXiv  Detail & Related papers  (2024-07-17T11:35:00Z)
• An Empirical Study on Capability of Large Language Models in Understanding Code Semantics [4.638578225024275]
  Large Language Models for Code (code LLMs) have demonstrated remarkable performance across various software engineering (SE) tasks. This paper introduces EMPICA, a framework designed to evaluate the capabilities of code LLMs in understanding code semantics.
  arXiv  Detail & Related papers  (2024-07-04T03:40:58Z)
• Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting [78.48355455324688]
  We propose a novel zero-shot synthetic code detector based on the similarity between the original code and its LLM-rewritten variants. Our results demonstrate a significant improvement over existing SOTA synthetic content detectors.
  arXiv  Detail & Related papers  (2024-05-25T08:57:28Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• Reflections on Inductive Thematic Saturation as a potential metric for measuring the validity of an inductive Thematic Analysis with LLMs [0.0]
  The paper suggests that initial thematic saturation (ITS) could be used as a metric to assess part of the transactional validity of Thematic Analysis (TA) with Large Language Models (LLMs) The paper presents the initial coding of two datasets of different sizes, and it reflects on how the LLM reaches some form of analytical saturation during the coding.
  arXiv  Detail & Related papers  (2024-01-06T15:34:38Z)
• CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules [51.82044734879657]
  We propose CodeChain, a novel framework for inference that elicits modularized code generation through a chain of self-revisions. We find that CodeChain can significantly boost both modularity as well as correctness of the generated solutions, achieving relative pass@1 improvements of 35% on APPS and 76% on CodeContests.
  arXiv  Detail & Related papers  (2023-10-13T10:17:48Z)
• ${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning [7.712824077083934]
  We focus on exploiting Euclidean symmetries inherent in certain cooperative multi-agent reinforcement learning problems. We design neural network architectures with symmetric constraints embedded as an inductive bias for multi-agent actor-critic methods.
  arXiv  Detail & Related papers  (2023-08-23T00:18:17Z)
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation [72.1638273937025]
  Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. CodeT5+ is a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning.
  arXiv  Detail & Related papers  (2023-05-13T14:23:07Z)
• FAENet: Frame Averaging Equivariant GNN for Materials Modeling [123.19473575281357]
  We introduce a flexible framework relying on frameaveraging (SFA) to make any model E(3)-equivariant or invariant through data transformations. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling.
  arXiv  Detail & Related papers  (2023-04-28T21:48:31Z)
• Execution-based Code Generation using Deep Reinforcement Learning [8.085533911328577]
  PPOCoder is a new framework for code generation that combines pre-trained PL models with Proximal Policy Optimization. PPOCoder seamlessly integrates external code-specific knowledge into the model optimization process. It's important to note that PPOCoder is a task-agnostic and model-agnostic framework that can be used across different code generation tasks and PLs.
  arXiv  Detail & Related papers  (2023-01-31T18:02:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.