Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP (2103.00453v2)

Abstract: When trained on large, unfiltered crawls from the internet, LLMs pick up and reproduce all kinds of undesirable biases that can be found in the data: they often generate racist, sexist, violent or otherwise toxic language. As large models require millions of training examples to achieve good performance, it is difficult to completely prevent them from being exposed to such content. In this paper, we first demonstrate a surprising finding: pretrained LLMs recognize, to a considerable degree, their undesirable biases and the toxicity of the content they produce. We refer to this capability as self-diagnosis. Based on this finding, we then propose a decoding algorithm that, given only a textual description of the undesired behavior, reduces the probability of a LLM producing problematic text. We refer to this approach as self-debiasing. Self-debiasing does not rely on manually curated word lists, nor does it require any training data or changes to the model's parameters. While we by no means eliminate the issue of LLMs generating biased text, we believe our approach to be an important step in this direction.

Self-Diagnosis and Self-Debiasing in LLMs: Addressing Corpus-Based Bias

The paper, "Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP," presents a nuanced exploration of techniques to mitigate bias in large pre-trained LLMs. It introduces the concepts of self-diagnosis and self-debiasing, focusing on an innovative approach to recognizing and potentially correcting biases that emerge from training data.

Core Contributions

1. Self-Diagnosis Capability: The paper proposes that LLMs inherently possess the capability to recognize biased behavior in their generated outputs. By employing a self-diagnosis input constructed from prompts about bias characteristics, models like GPT2 and T5 can estimate the probability of an output exhibiting a specific bias. The efficacy of self-diagnosis is positively correlated with model size, with larger models such as T5-XXL demonstrating robust performance in detecting biases using zero-shot assessments.
2. Self-Debiasing Mechanism: Building on the concept of self-diagnosis, the authors propose a novel self-debiasing decoding algorithm. This algorithm modifies the standard output probability distributions of a LLM by leveraging an input prompt designed to encourage biased behavior. The calculated discrepancy between the biased and unbiased token probabilities is then used to downscale probabilities associated with biased outputs, thus reducing bias in the generated text without requiring external training data or altering model parameters.
3. Evaluation Using Benchmark Datasets: The performance of the self-debiasing technique is assessed using the RealToxicityPrompts dataset, which includes prompts designed to produce biased model outputs. Self-debiasing exhibits a significant reduction in biases across six toxicity-related attributes, outperforming methods like manually curated word filters and domain-adaptive pretraining in several dimensions. The authors further evaluate their method on the CrowS-Pairs dataset, showing reductions in socially relevant biases such as gender and racial stereotyping.
4. Template Sensitivity and Human Evaluation: The paper acknowledges template sensitivity in zero-shot learning contexts. Thorough analyses demonstrate that while the robustness increases with model size, modifications in template inputs and descriptions can substantially influence bias recognition accuracy. Human evaluations reinforce the automated findings, indicating that modifications do not degrade text coherence or fluency.

Implications and Challenges

The implications of this research extend across theoretical and practical domains. The findings offer a way to reduce biases dynamically by leveraging LLMs' internal comprehension capabilities and bypassing the need for extensive curated datasets. This approach empowers users to define and fine-tune desired model behaviors more flexibly, accommodating context-specific requirements.

However, limitations persist. The method's current reliance on explicit attribute descriptions and its imperfect handling of complex or subtle biases underscore the need for continuous refinement. Moreover, the evaluation hinges primarily on English datasets, necessitating the exploration of multilingual and culturally diverse benchmarks. Another challenge remains in the computational cost associated with self-debiasing multiple attributes concurrently, which could impact real-time applications.

Future Directions

Future research could focus on enhancing the self-diagnosis and self-debiasing processes' adaptability to novel biases or attributes not represented in training data. Additionally, expanding this research to include multilingual contexts would provide a more comprehensive understanding of its global applicability. Developing a deeper understanding of how implicit biases are encoded in LLMs could further refine these techniques, moving towards genuine bias-free language generation solutions.

This paper provides meaningful insights into mitigating unwanted biases in NLP and points towards new avenues for reducing ethically complex machine learning challenges using intrinsic model features. It sets the stage for further investigations into scalable, flexible, and transparent approaches to bias correction in AI.