Isolating Language-Coding from Problem-Solving: Benchmarking LLMs with PseudoEval

• URL: http://arxiv.org/abs/2502.19149v1
• Date: Wed, 26 Feb 2025 14:08:17 GMT
• Title: Isolating Language-Coding from Problem-Solving: Benchmarking LLMs with PseudoEval
• Authors: Jiarong Wu, Songqiang Chen, Jialun Cao, Hau Ching Lo, Shing-Chi Cheung,
• Abstract summary: Existing code generation benchmarks are designed to study Large Language Models' end-to-end performance.<n>We construct PseudoEval, a multilingual code generation benchmark that provides a solution written in pseudocode as input.<n>Our study indicates that problem-solving capability may transfer across programming languages, while language-coding needs more language-specific effort.
• Score: 7.33924106492889
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Existing code generation benchmarks for Large Language Models (LLMs) such as HumanEval and MBPP are designed to study LLMs' end-to-end performance, where the benchmarks feed a problem description in natural language as input and examine the generated code in specific programming languages. However, the evaluation scores revealed in this way provide a little hint as to the bottleneck of the code generation -- whether LLMs are struggling with their problem-solving capability or language-coding capability. To answer this question, we construct PseudoEval, a multilingual code generation benchmark that provides a solution written in pseudocode as input. By doing so, the bottleneck of code generation in various programming languages could be isolated and identified. Our study yields several interesting findings. For example, we identify that the bottleneck of LLMs in Python programming is problem-solving, while Rust is struggling relatively more in language-coding. Also, our study indicates that problem-solving capability may transfer across programming languages, while language-coding needs more language-specific effort, especially for undertrained programming languages. Finally, we release the pipeline of constructing PseudoEval to facilitate the extension to existing benchmarks. PseudoEval is available at: https://anonymous.4open.science/r/PseudocodeACL25-7B74.

Related papers

• Evaluating Programming Language Confusion [6.462594894731934]
  Large Language Models for code (Code LLMs) have gained significant traction in software engineering. These models have demonstrated remarkable capabilities in understanding programming concepts, implementing algorithms, and even bridging different programming languages. Despite these advances, Code LLMs often struggle with programming language confusion--producing code in unintended languages.
  arXiv  Detail & Related papers  (2025-03-17T18:14:15Z)
• Benchmarking LLM Code Generation for Audio Programming with Visual Dataflow Languages [1.559169421643164]
  Node-based programming languages are increasingly popular in media arts coding domains. Using LLM-based code generation to further lower the barrier to creative output is an exciting opportunity. Best strategy for code generation for visual node-based programming languages is still an open question.
  arXiv  Detail & Related papers  (2024-09-01T22:11:23Z)
• CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution [50.7413285637879]
  The CRUXEVAL-X code reasoning benchmark contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. Even a model trained solely on Python can achieve at most 34.4% Pass@1 in other languages.
  arXiv  Detail & Related papers  (2024-08-23T11:43:00Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts. We find that Llama Instruct and Mistral models exhibit high degrees of language confusion. We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs [57.27982780697922]
  Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequential nature of their decoding process. This paper introduces Lexical Unit Decoding, a novel decoding methodology implemented in a data-driven manner.
  arXiv  Detail & Related papers  (2024-05-24T04:35:13Z)
• CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
  We propose CodeGRAG, a Graphical Retrieval Augmented Code Generation framework to enhance the performance of LLMs. CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to fill the gap between programming languages and natural language. Various experiments and ablations are done on four datasets including both the C++ and python languages to validate the hard meta-graph prompt, the soft prompting technique, and the effectiveness of the objectives for pretrained GNN expert.
  arXiv  Detail & Related papers  (2024-05-03T02:48:55Z)
• 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)
• AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection [69.79627042058048]
  AdaCCD is a novel cross-lingual adaptation method that can detect cloned codes in a new language without annotations in that language. We evaluate the cross-lingual adaptation results of AdaCCD by constructing a multilingual code clone detection benchmark consisting of 5 programming languages.
  arXiv  Detail & Related papers  (2023-11-13T12:20:48Z)
• LEVER: Learning to Verify Language-to-Code Generation with Execution [64.36459105535]
  We propose LEVER, a simple approach to improve language-to-code generation by learning to verify the generated programs with their execution results. Specifically, we train verifiers to determine whether a program sampled from the LLMs is correct or not based on the natural language input, the program itself and its execution results. LEVER consistently improves over the base code LLMs(4.6% to 10.9% with code-davinci) and achieves new state-of-the-art results on all of them.
  arXiv  Detail & Related papers  (2023-02-16T18:23:22Z)
• X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models [103.75890012041366]
  Language models (LMs) have proven surprisingly successful at capturing factual knowledge. However, studies on LMs' factual representation ability have almost invariably been performed on English. We create a benchmark of cloze-style probes for 23 typologically diverse languages.
  arXiv  Detail & Related papers  (2020-10-13T05:29:56Z)

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.