@inproceedings{sahoo-etal-2024-addressing,
    title = "Addressing Bias and Hallucination in Large Language Models",
    author = "Sahoo, Nihar Ranjan  and
      Saxena, Ashita  and
      Maharaj, Kishan  and
      Ahmad, Arif A.  and
      Mishra, Abhijit  and
      Bhattacharyya, Pushpak",
    editor = "Klinger, Roman  and
      Okazaki, Naozaki  and
      Calzolari, Nicoletta  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-tutorials.12",
    pages = "73--79",
    abstract = "In the landscape of natural language processing (NLP), addressing the challenges of bias and hallucination is paramount to ensuring the ethical and unbiased development of Large Language Models (LLMs). This tutorial delves into the intricate dimensions of LLMs, shedding light on the critical importance of understanding and mitigating the profound impacts of bias and hallucination. Divided into two parts, the first part delves deep into the complexity of bias propagation in LLM development, where we dissect its origins and far-reaching impacts. We then present innovative methodologies for mitigating diverse forms of bias, including dynamic word embeddings and robust benchmarking strategies. The second part of the tutorial discusses hallucination - a prevalent issue in generative AI systems such as LLMs. Through advanced data-driven techniques, we decode its intricate effects and complexities, followed factually-driven mitigation strategies. Furthermore, we shed light on the pivotal role of human cognitive behavior in the context of hallucination, drawing insights from cognitive data, including human eye-tracking data. Ultimately, this cutting-edge tutorial serves as a guiding light, equipping participants with indispensable tools and insights to navigate the ethical complexities of LLMs, thus paving the way for the development of unbiased and ethically robust NLP systems.",
}

In the landscape of natural language processing (NLP), addressing the challenges of bias and hallucination is paramount to ensuring the ethical and unbiased development of Large Language Models (LLMs). This tutorial delves into the intricate dimensions of LLMs, shedding light on the critical importance of understanding and mitigating the profound impacts of bias and hallucination. Divided into two parts, the first part delves deep into the complexity of bias propagation in LLM development, where we dissect its origins and far-reaching impacts. We then present innovative methodologies for mitigating diverse forms of bias, including dynamic word embeddings and robust benchmarking strategies. The second part of the tutorial discusses hallucination - a prevalent issue in generative AI systems such as LLMs. Through advanced data-driven techniques, we decode its intricate effects and complexities, followed factually-driven mitigation strategies. Furthermore, we shed light on the pivotal role of human cognitive behavior in the context of hallucination, drawing insights from cognitive data, including human eye-tracking data. Ultimately, this cutting-edge tutorial serves as a guiding light, equipping participants with indispensable tools and insights to navigate the ethical complexities of LLMs, thus paving the way for the development of unbiased and ethically robust NLP systems.
Sahoo, Nihar Ranjan Saxena, Ashita Maharaj, Kishan Ahmad, Arif A. Mishra, Abhijit Bhattacharyya, Pushpak
Addressing Bias and Hallucination in Large Language Models