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LLM-assisted query expansion shows mixed results for Khmer semantic search

By PulseAugur Editorial · [1 sources] ·

Researchers have developed KSE-Web, a system designed to improve semantic search for the Khmer language, which faces challenges due to limited data and mixed language usage. The study evaluated various retrieval methods, including BM25, dense retrieval, and hybrid approaches, alongside LLM-assisted query expansion using Qwen2.5 models. Results indicated that traditional BM25 performed best, with hybrid methods showing comparable effectiveness. While LLM expansion did not universally improve results, larger Qwen2.5 models showed promise, though direct expansion also introduced issues like topic drift and noise. AI

IMPACT Highlights potential and limitations of LLM-assisted retrieval for low-resource languages, informing future model development.

RANK_REASON Academic paper detailing a new system and experimental analysis for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

LLM-assisted query expansion shows mixed results for Khmer semantic search

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Academic paper detailing a new system and experimental analysis for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nimol Thuon ·

    KSE-Web: An Analysis of Hybrid Retrieval and LLM-Assisted Query Expansion for Low-Resource Khmer Semantic Search

    arXiv:2608.21365v1 Announce Type: cross Abstract: As a low-resource language, Khmer presents several retrieval challenges, including limited annotated data, ambiguous word boundaries, weak support in multilingual embedding models, and frequent mixed Khmer-English usage. This pape…

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