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Unraveling the tapestry: The imperative of human valuation in guiding LLM’s decision-making

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In the dynamic landscape of artificial intelligence, Large Language Models (LLMs) stand as formidable entities, capable of processing vast amounts of information and making decisions that impact users. However, the allure of these models also brings forth ethical considerations, especially when they are entrusted with decision-making for individuals.

This blog post explores the crucial role of human evaluation in steering LLMs toward fairness, particularly in scenarios where biases may seep into the decision-making process.

The biased tapestry of historical data

Historical data, the bedrock upon which LLMs are trained, is not without its imperfections. As Cao et al. (2021) aptly point out, “LLMs may possess incorrect factual knowledge,” and biases ingrained in historical records can inadvertently find their way into the outputs of these language models. This becomes particularly concerning when LLMs are tasked with making decisions for individuals, as biased information can lead to discriminatory outcomes.

Human evaluation within LLMs

Evaluation is the process of assessing and gauging the performance, effectiveness, or quality of a system, model, or process. It plays a pivotal role in ensuring the reliability and appropriateness of outcomes in various fields.

Human evaluation, specifically, refers to the assessment conducted by individuals to gauge and interpret results, often incorporating nuanced insights, ethical considerations, and a deep understanding of societal norms. In the context of artificial intelligence, human evaluation becomes essential for navigating complex decision-making scenarios and addressing biases that may elude algorithmic scrutiny.

Human evolution vs. Historical biases

One noteworthy aspect of human evaluation is the recognition that humans themselves have evolved over time. While historical biases persist in the data, the bias of human evaluators can affect the human evaluation result.

For example, consider historical datasets that may contain biased views on gender roles. Human evaluators, informed by contemporary perspectives, can identify and rectify such biases, contributing to a more nuanced and just understanding of language.

The evolution in human perspectives provides a lens through which biases can be identified and rectified. As society progresses, individuals become more attuned to inclusivity and fairness, providing a valuable counterbalance to the biases inherent in historical data.

Deciphering decision-making in LLMs

When LLMs are bestowed with decision-making capabilities, the stakes are high. LLMs, even with efforts to enhance safety, can generate harmful and biased responses.

For instance, imagine an LLM tasked with evaluating job applications. Without vigilant human evaluation, the model might inadvertently favor certain demographics, perpetuating biases present in historical hiring data. Human evaluators, by contrast, bring cultural insights and ethical considerations to the table, ensuring that LLM decisions align with contemporary notions of fairness.

Therefore, human evaluation becomes an indispensable tool in deciphering the complex web of decisions made by LLMs. Human evaluators bring a nuanced understanding of cultural contexts and societal norms, enabling them to identify and address biases that may elude algorithmic scrutiny.

The ethical quandary: Can LLMs replace human evaluation?

A pivotal ethical concern arises when considering the potential replacement of human evaluation with LLM evaluation.

Let’s consider a hypothetical scenario: an LLM tasked with generating responses to user queries about mental health. Without human evaluators, the model might inadvertently generate responses that lack empathy or understanding. Human evaluation, rooted in ethical considerations and emotional intelligence, becomes crucial in refining LLMs to respond responsibly to sensitive topics.

Chiang and Lee (2023) argue for the coexistence of both evaluation methods, recognizing the strengths and limitations of each. Human evaluation, rooted in ethical considerations and a deep understanding of societal dynamics, is deemed essential for the ultimate goal of developing NLP systems for human use.

Conclusion: A harmonious collaboration


The journey through the intricate terrain of artificial intelligence and Large Language Models (LLMs) underscores the paramount importance of human evaluation. As we unravel the tapestry of LLM decision-making, it becomes evident that historical biases ingrained in training data pose real ethical challenges. Human evolution, both in societal attitudes and individual perspectives, provides a dynamic lens through which biases can be identified, rectified, and crucially balanced.

The hypothetical scenarios presented, from evaluating job applications to responding to queries about mental health, illuminate the potential pitfalls of relying solely on LLM evaluation. The ethical quandary of replacing human evaluation with LLM assessment is delicately examined, with Chiang and Lee (2023) advocating for a collaborative coexistence of both methods.

Ultimately, human evaluation emerges as the linchpin in ensuring fair, ethical, and unbiased outcomes in LLM decision-making. It acts as a counterbalance to historical biases, providing a nuanced understanding of cultural contexts and societal norms.

As we propel into an era where the tapestry woven by LLMs reflects technological prowess, HTCNXT ensures that this fabric is woven with the ethical standards and progressive ideals demanded by contemporary society. By incorporating human evaluation into the decision-making fabric of LLMs, we not only mitigate historical biases but also align our solutions with contemporary ethical standards. This collaboration between artificial intelligence and human insights is not merely advisable but imperative.

Our integration of human evaluation within the HTCNXT Platform is a testament to our dedication to responsible AI. We provide users with a powerful tool that not only harnesses the capabilities of LLMs but also ensures that the solutions built on our platform comply with the highest standards of fairness and responsibility. Through this collaboration, HTCNXT empowers users to navigate the evolving landscape of artificial intelligence with confidence, knowing that their decisions align with both technological excellence and ethical considerations.


De Cao, N., Aziz, W., & Titov, I. (2021). Editing Factual Knowledge in Language Models (Version 2). arXiv.

Chiang, C.-H., & Lee, H. (2023). Can Large Language Models Be an Alternative to Human Evaluations? In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics.


Aviral Sharma

AI Engineer







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