To overcome scarce labeled data, Matryoshka Representation Learning truncation to 256 dimensions preserves 99.9% of retrieval quality while reducing dense-index storage and exact similarity arithmetic by a factor of three.
Abstract
Natural language code retrieval is a rapidly evolving task in computer science. However, the 1C:Enterprise ecosystem combines Russian syntax with highly domain-specific terminology, for which open datasets and specialized models have been virtually non-existent. We present a comprehensive pipeline for 1C code retrieval: an open benchmark of 3,413 real-world, PII-scrubbed query-code pairs, a reproducible evaluation harness, and a specialized bi-encoder. To overcome scarce labeled data, we fine-tune on 784,057 synthetic triplets generated by google/gemma-4-26B-A4B-it from public code repositories, using Matryoshka Representation Learning (MRL) and a privacy-aware tokenizer. Because the benchmark subsets differ in size, we report balanced-subset macro, query-weighted micro, and forum-only results. Our model reaches 0.5992 balanced macro nDCG@10, 0.5044 micro, and 0.4617 on forum, versus 0.4932 macro for the baseline architecture and 0.5404 for google/embeddinggemma-300m. Removing every benchmark example flagged by the conservative exact/13-gram overlap audit leaves 0.6011 balanced macro (0.5010 micro), indicating that detected train-benchmark overlap does not explain the headline result. MRL truncation to 256 dimensions preserves 99.9% of retrieval quality while reducing dense-index storage and exact similarity arithmetic by a factor of three.
Querying LLMs as digital libraries is feasible, but its effectiveness depends on model strength, deployment conditions, dataset structure, and execution strategy, and Galois remains valuable when relational discipline and controlled query execution are required.
Code search in large-scale ecosystems is often hindered by the lexical gap between user queries and implementation details, alongside the trade-off between the low latency of traditional Information Retrieval (IR) and the precision of Deep Learning (DL). We present MediaWiki Code2Code Search, a neural retrieval system for semantic code-to-code discovery. By indexing 1.29 million structural entities (functions, types, and templates) across 2,500+ MediaWiki repositories, our system enables retrieval based on computational intent rather than surface tokens. We employ a split-build architecture, decoupling GPU-intensive offline indexing from a CPU-only serving layer; our FAISS IVF-PQ index occupies 168.6 MB: a 96.6\% reduction compared to a flat float32 baseline, and achieves a median query latency of 1.85 seconds on commodity hardware, satisfying the 6 GiB RAM constraint of Wikimedia Toolforge. Our evaluation across a 27-query benchmark demonstrates superior performance over the BM25 baseline, achieving a P@10 of 0.87 compared to 0.64 (0.52 versus 0.34 for strict matching). Gains are most pronounced in name-obfuscated tasks where lexical methods fail. The system is available at https://code2codesearch.toolforge.org under the Apache 2.0 licence and provides an open RESTful API.
The meteoric rise of large language models (LLMs) has reshaped natural language processing, inspiring new approaches to data integration tasks such as entity matching (EM). While proprietary models like GPT-4 deliver strong performance, open-source alternatives (e.g., Mistral, DeciLM, Gemma-3) require supervised fine-tuning (SFT) to excel in specialized domains. However, naïvely training billion-parameter scale LLMs on uncurated corpora is computationally prohibitive and environmentally costly, often taking days while introducing inefficiencies that limit rapid application deployment. We introduce AssistEM, a framework for efficient LLM adaptation to EM via principled data selection. AssistEM integrates NER and TF-IDF signals into a composite score, filters entity pairs with domain-level thresholds, constructs denoised semantic embeddings, and applies graph-based clustering with class-aware sampling to obtain a compact yet representative set of ∼\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}8000 high-quality training instances. This yields rapid specialization: AssistEM-trained LLMs converge in under 4.5 hours–versus 12+ hours for DeciLM-7B and 24+ hours for Yi-34B–while surpassing GPT-4-0613 by 4.3 F1 points and Ditto by 11 F1 points across diverse EM benchmarks. By aligning data quality with model specialization, AssistEM demonstrates that selective fine-tuning not only accelerates adaptation but also improves training efficiency (requiring fewer GPU hours), enabling open-source LLMs to rival–and in some cases outperform–closed-source models. These results highlight data selection as a critical lever for sustainable, domain-specific LLM deployment.
John Bosco Mugeni, S. Lynden, Toshiyuki Amagasa et al.· International Journal of Dat...· 0 citations
Large language models (LLMs) now generate fluent natural language and source code, creating challenges for authorship attribution, academic integrity, and software supply-chain security. Most existing detectors for AI-generated content are evaluated separately on natural language or source code, often under matched train–test conditions that can overestimate real-world reliability. We present a paired-prompt benchmark for human-versus-machine detection across English text, Python code, and mixed text–code documents. The benchmark includes 22,141 instances from HC3, CodeSearchNet, MBPP, and HumanEval across training, validation, and test partitions, plus Mix-Eval, a mixed-content set of 997 Jupyter-notebook-style samples. We evaluate RoBERTa-large for text, GraphCodeBERT and CodeBERT-base for code, a unified RoBERTa-base detector trained on both modalities, and zero-shot baselines. Fine-tuned detectors achieve near-perfect in-distribution performance, with AUROC 1.0000±0.0000 and accuracy above 99.5%. Across five instruction-tuned generator families of varying size (3.8B–7B) and architecture, with the human and problem distributions held fixed, cross-generator transfer causes negligible degradation (AUROC spread 0.0002; drops of at most 0.0003). In contrast, domain shift is the main failure mode: on MBPP+HumanEval, GraphCodeBERT drops to 0.85±0.02 AUROC and CodeBERT-base to 0.67±0.02. On Mix-Eval, the unified detector outperforms a routed text–code pipeline by 21 AUROC points (0.96 vs. 0.75), largely because of router failures on mixed inputs. Training-time augmentation improves low-false-positive performance, while legacy supervised detectors show systematic class inversion on modern LLM outputs. These results show that reliable deployment requires cross-domain evaluation, mixed-content testing, and calibration beyond in-distribution accuracy.
With recent advancements, Small language models (SLMs) are increasingly used as preprocessors to handle query classification, routing, and candidate selection in retrieval pipelines, but they are nearly always prompted in English, even when users search in Hindi, Bengali, or code-mixed forms. We test whether prompting the same (frozen) SLM in three typologically diverse languages and aggregating the outputs can improve classification without retraining or translation. Nine decoder-only models (1B--9B parameters) evaluated on four public benchmarks show that confidence-weighted fusion of English, Hindi, and Bengali predictions raises macro-F1 by 3--5 points over English-only baselines, with the strongest gains on binary and coarse intent tasks. Parallel execution keeps latency within 1.2--1.4× of the single-language baseline. A paraphrase-only ensemble under identical conditions reaches only +1.4~F1 on average, suggesting that cross-lingual diversity rather than surface-level input variation drives the gain. Because no additional data, training, or translation services are required, our method may be useful when scaling to larger models is out of reach.
We introduce SciRet, a compute-aware empirical study of retrieval-augmented generation for scientific question answering over CORD-19. Rather than proposing a new model, we evaluate a fixed scientific RAG pipeline across three corpus scales: 1,034 chunks (1K papers), 5,160 chunks (5K papers), and 15,480 chunks (15K papers). The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation. Across these settings, hybrid retrieval is more robust than either sparse-only or dense-only retrieval in our setting, reaching Recall@10 of 1.000 at 1K and 15K. In contrast, an MS MARCO-trained cross-encoder reranker reduces precision on the scientific corpus, suggesting that domain mismatch can outweigh the benefits of stronger query-passage interaction. Generation faithfulness measured with RAGAS increases with corpus scale in our setup. Retrieval evaluation uses pseudo-relevance labels derived from the hybrid system, so we treat the results as controlled comparative evidence rather than a benchmark claim. We release code, indexes, and evaluation outputs to support replication and follow-up studies.
Kaysarul Anas Apurba, Mahade Hasan, Rofiqul Alam Shehab et al.· 0 citations