A Tool for In-depth Analysis of Code Execution Reasoning of Large Language Models
- URL: http://arxiv.org/abs/2501.18482v1
- Date: Thu, 30 Jan 2025 16:56:08 GMT
- Title: A Tool for In-depth Analysis of Code Execution Reasoning of Large Language Models
- Authors: Changshu Liu, Reyhaneh Jabbarvand,
- Abstract summary: This paper introduces ExeRScope, a series of tools to analyze the result of code execution reasoning frameworks.<n>Analysis can be generalized to code with similar properties without the urgent need to design more benchmarks.
- Score: 1.644043499620662
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Code Executing Reasoning is becoming a new non-functional metric that assesses the ability of large language models (LLMs) in programming tasks. State-of-the-art frameworks (CodeMind or REval) and benchmarks (CruxEval) usually focus on LLM's prediction of a given code's input/output or intermediate variable states/values on limited programs. However, there is no tool for more in-depth analysis of the results. Without such a tool, the observations about LLM's code execution reasoning cannot be generalized to more datasets, preventing the research community and practitioners from devising the next generation of LLMs with better code execution reasoning abilities. This paper introduces ExeRScope, a series of tools and heuristics to analyze the result of code execution reasoning frameworks to understand better the impact of code properties in the studied benchmarks on the code execution reasoning. With such tooling, analysis can be generalized to code with similar properties without the urgent need to design more benchmarks, which is a cumbersome effort.
Related papers
- On LLM-Assisted Generation of Smart Contracts from Business Processes [0.08192907805418582]
Large language models (LLMs) have changed the reality of how software is produced.<n>We present an exploratory study to investigate the use of LLMs for generating smart contract code from business process descriptions.<n>Our results show that LLM performance falls short of the perfect reliability required for smart contract development.
arXiv Detail & Related papers (2025-07-30T20:39:45Z) - MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks [56.34018316319873]
We propose MERA Code, a benchmark for evaluating code for the latest code generation LLMs in Russian.<n>This benchmark includes 11 evaluation tasks that span 8 programming languages.<n>We evaluate open LLMs and frontier API models, analyzing their limitations in terms of practical coding tasks in non-English languages.
arXiv Detail & Related papers (2025-07-16T14:31:33Z) - Is Compression Really Linear with Code Intelligence? [60.123628177110206]
textitFormat Annealing is a lightweight, transparent training methodology designed to assess the intrinsic capabilities of pre-trained models equitably.<n>Our empirical results reveal a fundamental logarithmic relationship between measured code intelligence and bits-per-character (BPC)<n>Our work provides a more nuanced understanding of compression's role in developing code intelligence and contributes a robust evaluation framework in the code domain.
arXiv Detail & Related papers (2025-05-16T16:59:14Z) - BinMetric: A Comprehensive Binary Analysis Benchmark for Large Language Models [50.17907898478795]
We introduce BinMetric, a benchmark designed to evaluate the performance of large language models on binary analysis tasks.<n>BinMetric comprises 1,000 questions derived from 20 real-world open-source projects across 6 practical binary analysis tasks.<n>Our empirical study on this benchmark investigates the binary analysis capabilities of various state-of-the-art LLMs, revealing their strengths and limitations in this field.
arXiv Detail & Related papers (2025-05-12T08:54:07Z) - On Explaining (Large) Language Models For Code Using Global Code-Based Explanations [45.126233498200534]
Language Models for Code (LLM4Code) have significantly changed the landscape of software engineering (SE)
We introduce code rationales (Code$Q$), a technique with rigorous mathematical underpinning, to identify subsets of tokens that can explain individual code predictions.
Our evaluation demonstrates that Code$Q$ is a powerful interpretability method to explain how (less) meaningful input concepts (i.e., natural language particle at') highly impact output generation.
arXiv Detail & Related papers (2025-03-21T01:00:45Z) - 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) - ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models [81.12673534903979]
Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools.<n>We propose ToolCoder, a novel framework that reformulates tool learning as a code generation task.
arXiv Detail & Related papers (2025-02-17T03:42:28Z) - SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors [5.247363735860479]
Large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks.
Given LLMs' ability to understand and process diverse programs, they present a promising direction for building general-purpose surrogate models.
We introduce SURGE, a benchmark with $1160$ problems covering $8$ key aspects.
Through empirical analysis of $21$ open-source and proprietary LLMs, we examine scaling laws, data efficiency, and predictive accuracy.
arXiv Detail & Related papers (2025-02-16T15:38:19Z) - What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions.
We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types.
We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
arXiv Detail & Related papers (2024-07-08T17:27:17Z) - Perplexed: Understanding When Large Language Models are Confused [3.4208414448496027]
This paper introduces perplexed, a library for exploring where a language model is perplexed.
We conducted a case study focused on Large Language Models (LLMs) for code generation using an additional tool we built to help with the analysis of code models called codetokenizer.
We found that our studied code LLMs had their worst performance on coding structures where the code was not syntactically correct.
arXiv Detail & Related papers (2024-04-09T22:03:39Z) - Reasoning Runtime Behavior of a Program with LLM: How Far Are We? [25.451857140926943]
Large language models for code (i.e., code LLMs) have shown strong code understanding and generation capabilities.
Code reasoning is one of the most essential abilities of code LLMs.
We propose a framework, namely REval, for evaluating code reasoning abilities and consistency of code LLMs with program execution.
arXiv Detail & Related papers (2024-03-25T05:37:16Z) - CodeMind: A Framework to Challenge Large Language Models for Code Reasoning [1.4027589547318842]
We introduce CodeMind, a framework designed to gauge the code reasoning abilities of Large Language Models (LLMs)
CodeMind supports three code reasoning tasks: Independent Execution Reasoning (IER), Dependent Execution Reasoning (DER), and Specification Reasoning (SR)
arXiv Detail & Related papers (2024-02-15T02:24:46Z) - Efficient Tool Use with Chain-of-Abstraction Reasoning [63.08202389132155]
Large language models (LLMs) need to ground their reasoning to real-world knowledge.<n>There remains challenges for fine-tuning LLM agents to invoke tools in multi-step reasoning problems.<n>We propose a new method for LLMs to better leverage tools in multi-step reasoning.
arXiv Detail & Related papers (2024-01-30T21:53:30Z) - CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark.
In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship.
We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
arXiv Detail & Related papers (2023-11-29T08:29:54Z) - Large Language Models for Code Analysis: Do LLMs Really Do Their Job? [13.48555476110316]
Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks.
This paper offers a comprehensive evaluation of LLMs' capabilities in performing code analysis tasks.
arXiv Detail & Related papers (2023-10-18T22:02:43Z) - CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models [74.22729793816451]
Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability.
We propose CREATOR, a novel framework that enables LLMs to create their own tools using documentation and code realization.
We evaluate CREATOR on MATH and TabMWP benchmarks, respectively consisting of challenging math competition problems.
arXiv Detail & Related papers (2023-05-23T17:51:52Z)
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.