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LLMs show greater robustness in noisy Bangla text event detection than encoder models

By PulseAugur Editorial · [2 sources] ·

A new research paper evaluates the robustness of different AI model architectures for event detection in noisy Bangla text. The study found that while encoder-only models like BanglaBERT and XLM-R perform better on clean data, decoder-only models such as Llama 3 and Gemma 3 demonstrate superior resilience to noise, particularly when event triggers are corrupted. The research also highlights that model scaling and combined training on clean and noisy data can significantly improve robustness, especially for decoder-only LLMs. AI

IMPACT Decoder-only LLMs show promise for real-world applications where text quality is variable, potentially improving event detection in low-resource languages.

RANK_REASON The cluster contains a research paper published on arXiv detailing model evaluations and findings.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs show greater robustness in noisy Bangla text event detection than encoder models

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Tanvir Ahmed Sijan, S. M Golam Rifat, Nayeemul Islam, Md. Musfique Anwar ·

    Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text

    arXiv:2606.30914v1 Announce Type: new Abstract: Event detection (ED) systems are typically evaluated on clean, curated text, leaving their robustness to real-world noise largely unexplored, particularly for low-resource languages such as Bangla. We introduce a generalized Bangla …

  2. arXiv cs.CL TIER_1 English(EN) · Md. Musfique Anwar ·

    Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text

    Event detection (ED) systems are typically evaluated on clean, curated text, leaving their robustness to real-world noise largely unexplored, particularly for low-resource languages such as Bangla. We introduce a generalized Bangla news event ontology and a benchmark comprising 9…

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