| Герои Меча и Магии 5 - Heroes Of Might And Magic V Новости, обсуждение, аналитическая и статическая информация по Heroes of Might and magic 5 |
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| Герои Меча и Магии 5 - Heroes Of Might And Magic V Новости, обсуждение, аналитическая и статическая информация по Heroes of Might and magic 5 |
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In academic circles, "243" often refers to a paper's identifier in a specific conference track. Depending on your interest, you might also be looking for:
This 2023 paper by Wan et al. investigates how large language models (LLMs) may perpetuate social biases when writing recommendation letters. It is highly regarded for its systematic approach to examining language style and lexical content.
: "Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition" – A 2022 paper introducing a new dataset for improved location identification in text.
: The authors found that LLMs often use different descriptive terms based on gender—for example, describing female candidates as "warm" while calling male candidates "role models".
: "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models" – A study comparing pretrained multilingual models against monolingual ones.
: Uses social science-inspired evaluation methods to track bias propagation across language style and lexical content. Resources : Read the Full Paper (PDF) Watch the Presentation (243.mp4) (Direct Video Link) Other Related Papers (Index 243)
In academic circles, "243" often refers to a paper's identifier in a specific conference track. Depending on your interest, you might also be looking for:
This 2023 paper by Wan et al. investigates how large language models (LLMs) may perpetuate social biases when writing recommendation letters. It is highly regarded for its systematic approach to examining language style and lexical content.
: "Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition" – A 2022 paper introducing a new dataset for improved location identification in text.
: The authors found that LLMs often use different descriptive terms based on gender—for example, describing female candidates as "warm" while calling male candidates "role models".
: "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models" – A study comparing pretrained multilingual models against monolingual ones.
: Uses social science-inspired evaluation methods to track bias propagation across language style and lexical content. Resources : Read the Full Paper (PDF) Watch the Presentation (243.mp4) (Direct Video Link) Other Related Papers (Index 243)