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ENTITY Malayalam

Malayalam

PulseAugur coverage of Malayalam — every cluster mentioning Malayalam across labs, papers, and developer communities, ranked by signal.

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SENTIMENT · 30D

3 day(s) with sentiment data

RECENT · PAGE 1/1 · 7 TOTAL
  1. TOOL · CL_193690 ·

    Monolingual models outperform multilingual on Dravidian languages

    Researchers have developed and evaluated five GPT-2 architecture models to assess the performance of multilingual language models on Dravidian languages. Four of these models were trained monolingually for Tamil, Telugu…

  2. TOOL · CL_167534 ·

    New dataset teaches LLMs Indian Knowledge Systems across 7 languages

    Researchers have developed IKS-Instruct, a new multilingual dataset designed to teach large language models about Indian Knowledge Systems (IKS). The dataset contains over 24,000 instruction-response pairs in seven lang…

  3. TOOL · CL_167301 ·

    New AI system GeoMVC tackles misogyny in multimodal memes

    Researchers have developed a new system called GeoMVC for detecting misogyny in internet memes, a task complicated by the interplay between visual and textual elements and cultural context. The system employs a Geometri…

  4. RESEARCH · CL_167424 ·

    Indian languages face 8x "tokenizer tax" in LLMs due to English-centric training

    A new research paper highlights a significant disadvantage faced by Indian languages when processed by large language models due to subword tokenization. These tokenizers, primarily trained on English data, result in an…

  5. TOOL · CL_117769 ·

    New benchmark and fine-tuning technique improve Indic language ASR

    Researchers have developed Vividh-ASR, a new benchmark designed to evaluate automatic speech recognition (ASR) models on Indic languages, specifically Hindi and Malayalam. This benchmark categorizes audio into four tier…

  6. RESEARCH · CL_41788 ·

    SCRIBE framework improves ASR for Indic languages with new error analysis

    Researchers have introduced SCRIBE, a new diagnostic framework designed to improve automatic speech recognition (ASR) for Indic languages. Unlike traditional metrics like Word Error Rate (WER), SCRIBE categorizes errors…

  7. RESEARCH · CL_30789 ·

    New benchmark tackles ASR bias in Indic languages

    Researchers have developed Vividh-ASR, a new benchmark designed to evaluate automatic speech recognition (ASR) models for Indic languages, specifically Hindi and Malayalam. This benchmark categorizes audio into four tie…