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LLMs struggle with semantic recall in long code contexts, new paper finds

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv explores the limitations of large language models (LLMs) in understanding long code contexts. Researchers found that while LLMs excel at lexical recall (verbatim code retrieval), their semantic recall (understanding operational semantics) significantly degrades when code is positioned in the middle of long inputs. The study introduces a metric called semantic recall sensitivity and proposes a new task, SemTrace, to better evaluate this capability. Findings suggest current benchmarks may overestimate LLMs' code understanding abilities. AI

IMPACT Highlights potential overestimation of LLM code understanding capabilities, suggesting a need for more robust evaluation methods.

RANK_REASON Academic paper detailing a new evaluation method and findings for LLM code understanding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with semantic recall in long code contexts, new paper finds

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Academic paper detailing a new evaluation method and findings for LLM code understanding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Adam \v{S}torek, Mukur Gupta, Samira Hajizadeh, Prashast Srivastava, Suman Jana ·

    Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Understanding

    arXiv:2505.13353v5 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed for understanding large codebases, but whether they understand operational semantics of long code context or rely on pattern matching shortcuts remains unclear. We distingui…

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