Towards Controllable Biases in Language Generation (2005.00268v2)

Abstract: We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.

Controlling Biases in Natural Language Generation: A Summary

The paper "Towards Controllable Biases in Language Generation" by Sheng et al. addresses the challenge of managing societal biases within natural language generation (NLG) systems. The authors propose a methodology to control and analyze biases embedded in the outputs of NLG models, focusing on demographic mentions as a point of influence.

Methodology and Framework

The authors' approach is centered around the concept of "adversarial triggers," which are crafted inputs designed to modify the output behavior of generative LLMs such as GPT-2. These triggers can be used to induce specific bias polarities in generated text—negative, neutral, or positive—when input prompts contain demographic mentions like "Black person" or "woman." Two primary scenarios are explored: inducing opposed biases between demographics and equalizing biases across different groups.

The framework comprises several components:

1. Bias Triggers: By extending techniques from gradient-based adversarial trigger phrase search, the authors develop triggers that pre-emptively reorient the bias polarity in generated content.
2. Bias Analysis and Mitigation: They propose two distinct objectives—one to induce biases for diagnostic purposes and another to mitigate them. The diagnostic objective helps understand the biases' nature by prompting models to generate text with intended biases, while the mitigation objective seeks to produce less negatively biased text.
3. Evaluation Metrics: The notion of "regard," reflecting the social perception towards a demographic group, is employed to quantify biases in the text. This measure is used in the trigger search algorithm to achieve desired bias control objectives.

Experimental Results

The paper's experiments utilize the GPT-2 model and evaluate generated texts' bias levels through both automatic and human assessments. The methodology reveals the capacity to:

• Induce negative or positive biases towards selected demographics by finding and inserting specific trigger phrases.
• Mitigate biases such that the disparity in regard score between different demographics is decreased, effectively promoting more balanced output.
• Generalize these bias control mechanisms to broader contexts, including dialogue systems like DialoGPT.

The automatic evaluations documented substantial differences in generated text bias when using different trigger conditions, displaying the method's potential for bias manipulation. Human evaluations aligned with these findings, reinforcing the reliability of the regard classifier.

Implications and Future Work

The implications of this research are substantial for user-facing NLG applications, where biased language can perpetuate societal stereotypes and negative perceptions of certain demographics. Practically, this framework for bias control can be integrated into dialogue systems, machine translation, and other NLG applications, potentially leading to more equitable and balanced AI outputs.

Theoretically, this research illustrates how adversarial methods can pinpoint and correct biases within LLMs, providing insights into the limitations and biases ingrained in such models' training data.

Looking forward, future work might focus on refining this framework for further generalization across diverse languages and demographic representations. Additionally, exploring the interface between this bias control approach and other NLP tasks could widen its applicability and efficacy in mitigating biases more broadly across AI systems.

The paper offers a valuable contribution to the ongoing discussion of fairness in AI, emphasizing the importance of developing methodologies that address inherent biases in contemporary LLMs.