Mitigating Evaluation Bias in Large Language Models

Core Concepts

The author identifies and addresses likelihood bias in Large Language Models (LLMs) used for evaluation, proposing a method to mitigate the bias successfully.

Abstract

Large Language Models (LLMs) are commonly used for evaluating natural language tasks, but they may exhibit likelihood bias. The author investigates this bias and introduces a method to mitigate it by using biased instances for in-context learning. Experimental results show significant improvement in evaluation performance after applying the proposed method.
Key points:

Likelihood bias can lead to overrating high-likelihood sentences and underrating low-likelihood ones.
The proposed method successfully mitigates likelihood bias in LLM-based evaluators.
Experiments on data-to-text and grammatical error correction tasks demonstrate the effectiveness of the mitigation strategy.
Hyperparameters, dataset details, computational budget, and additional discussions on intrinsic/non-intrinsic evaluation criteria are provided.

Stats

"Our experiments show that both evaluators based on GPT-3.5 and Llama2-13B indeed suffer from likelihood bias."
"BiasScore ranges from -1 to 1, where 1 indicates strong likelihood bias."
"For GEC, GPT-3.5 overall displays a stronger bias across all criteria (0.43) than Llama2-13B (0.21)."

Quotes

"Large Language Models exhibit robust language comprehension capabilities."
"Our method successfully mitigates this bias, also improving evaluation performance significantly."

Key Insights Distilled From

Likelihood-based Mitigation of Evaluation Bias in Large Language Models

by Masanari Ohi... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2402.15987.pdf

Deeper Inquiries

How can fine-tuning models be compared to in-context learning for mitigating biases?

Fine-tuning models and in-context learning are two approaches used to mitigate biases in language models. Fine-tuning involves adjusting the parameters of a pre-trained model on specific tasks or datasets, allowing it to learn task-specific information. This method is effective at reducing biases related to the training data but may not fully address biases that arise during inference.
On the other hand, in-context learning involves providing additional context or examples during the evaluation process. By using highly biased instances as few-shot examples for in-context learning, models can learn from specific instances where bias is prominent. This approach helps address bias that occurs during evaluation by focusing on real-world scenarios rather than general training data.
In comparison, fine-tuning is more focused on adapting the model's overall behavior based on new data, while in-context learning targets specific instances where bias is observed. Fine-tuning requires retraining the entire model with new data, which can be computationally expensive and time-consuming. In contrast, in-context learning provides a targeted way to adjust model behavior without extensive retraining.
Both methods have their strengths and limitations when it comes to mitigating biases. Fine-tuning offers a comprehensive adjustment of the model's parameters but may not always capture nuanced biases present in individual instances. In contrast, in-context learning allows for targeted bias mitigation based on specific examples but may require careful selection of instances and prompt design.

How might reducing likelihood bias impact social biases present in evaluators?

Reducing likelihood bias has implications beyond improving evaluation metrics; it can also help address social biases present within language models themselves. Likelihood bias refers to an overreliance on high-likelihood outputs by evaluators like large language models (LLMs), leading them to favor certain types of responses over others based solely on superficial characteristics like word order or sentence structure.
By mitigating likelihood bias through techniques such as utilizing highly biased instances for in-context learning, we can potentially reduce disparities caused by these surface-level differences and ensure fairer evaluations across various inputs regardless of their likelihood scores alone.
Furthermore, addressing likelihood bias indirectly tackles deeper societal issues embedded within language models—such as gender stereotypes or racial prejudices—that could influence how LLMs evaluate text inputs. For instance, if an LLM consistently assigns higher likelihood scores to sentences conforming to stereotypical gender roles ("She is nurturing"), this could perpetuate biased evaluations that reflect societal norms rather than objective quality criteria.
Therefore, reducing likelihood bias not only enhances evaluation accuracy but also contributes towards minimizing social biases ingrained within evaluators like LLMs. By promoting fairness and impartiality in assessments through unbiased scoring mechanisms, we take steps towards creating more equitable AI systems that align with ethical standards.

What ethical considerations should be taken into account when addressing biases in language models?

When addressing biases inherent within language models like Large Language Models (LLMs), several ethical considerations must be carefully evaluated:

Transparency: It's crucial to transparently communicate how mitigation strategies are implemented and their effectiveness at reducing biases.

Accountability: Establish clear accountability frameworks regarding who is responsible for implementing mitigation techniques and monitoring outcomes.

Fairness: Ensure that all demographic groups are equally represented during dataset collection and model training processes.

4 .Privacy: Safeguard user privacy rights by anonymizing sensitive information used during training datasets.
5 .Bias Detection: Implement robust mechanisms for continuously detecting and rectifying emerging forms of bias post-deployment.
6 .Informed Consent: Obtain informed consent from individuals whose data is utilized for training purposes.
7 .Diversity & Inclusion: Promote diversity among teams working on developing algorithms as diverse perspectives enhance sensitivity towards potential sources of prejudice
8 .Continuous Monitoring: Regularly monitor system performance post-deployment using relevant metrics
9 .Mitigation Strategies: Employ diverse strategies such as debiasing algorithms or adversarial testing methodologies
By integrating these ethical considerations into every stage of development—from dataset creation through deployment—developers can create more responsible AI systems that prioritize fairness,
accountability ,and transparency while effectively combating inherent algorithmic
biases prevalent within LLMs