Enhancing Personalization and Mitigating Bias in Language Models through Context Steering

Concetti Chiave

Context Steering (CoS) is a simple training-free method that can be easily applied to autoregressive language models to control the influence of contextual information on the generated text, enabling personalization and bias mitigation.

Sintesi

The paper introduces Context Steering (CoS), a technique that can be used to control the influence of contextual information on the output of autoregressive language models. The key insight is that language models capture the relationship between the context and the generated text in terms of token prediction likelihood, which allows the authors to compute the influence of the context. This enables them to amplify or tune down the influence of the context in downstream generations by a factor of λ, allowing fine-grained control over the language model's output.
The authors showcase a variety of applications of CoS, including:

Enhancing personalization: CoS can generate more personalized responses by amplifying the influence of the user's context. The authors demonstrate this through a user study on personalized movie summarizations.

Mitigating bias: CoS can reduce the impact of biases present in language models by tuning down the influence of problematic contexts. The authors evaluate this on the Bias Benchmark for QA (BBQ) dataset and on the Implicit Association Test (IAT).

Quantifying implicit hate speech: The authors combine CoS with Bayesian Inference to infer the level of hate in online tweets, and show that the inferred levels correlate well with human evaluations.

The key advantages of CoS are that it is a training-free, inference-time technique that can be easily applied to existing language models, and that it provides practitioners with fine-grained control over the influence of contextual information, enabling a wide range of applications.

Statistiche

"Newton's second law of motion, also known as the law of acceleration, states that the acceleration of an object is directly proportional to the force applied and inversely proportional to the object's mass."
"Mathematically, this is expressed as F = ma."
"For example, let's say you have two cars of the same size, but one has a much heavier mass. If you apply the same force to both cars, the lighter car will accelerate faster."

Citazioni

"When querying a large language model (LLM), the context, i.e. personal, demographic, and cultural information specific to an end-user, can significantly shape the response of the LLM."
"Proper usage of the context enables the LLM to generate personalized responses, whereas inappropriate contextual influence can lead to stereotypical and potentially harmful generations (e.g. associating "female" with "housekeeper")."
"One common approach to address this challenge is to fine-tune LLMs on contextually appropriate responses. However, this approach is expensive, time-consuming, and not controllable for end-users in different situations."

Approfondimenti chiave tratti da

CoS: Enhancing Personalization and Mitigating Bias with Context Steering

by Jerry Zhi-Ya... alle arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01768.pdf

Domande più approfondite

How can Context Steering be extended to handle multiple contexts simultaneously and control their relative influence on the generated text?

To extend Context Steering to handle multiple contexts simultaneously, we can introduce a mechanism to combine and weigh the influence of each context on the generated text. One approach could involve assigning weights to each context based on their relevance or importance to the generation task. These weights can then be used to modulate the influence of each context on the output text. By adjusting these weights, practitioners can control the relative impact of each context on the generated text.
Additionally, we can explore techniques such as attention mechanisms or hierarchical modeling to allow the model to attend to different contexts at different levels of granularity. This way, the model can capture the interactions between multiple contexts and generate text that reflects a nuanced understanding of the input information.
Overall, by incorporating mechanisms to handle multiple contexts and control their relative influence, Context Steering can provide a more flexible and adaptive approach to generating text that is tailored to the specific needs of the user or application.

What are the limitations of Context Steering in terms of the length of the input prompt and the complexity of the contextual information?

One limitation of Context Steering in terms of the length of the input prompt is that as the prompt becomes longer, the influence of the context may diminish. This is because the context is typically pre-pended to the prompt, and as the prompt grows in length, the impact of the context may be diluted. In such cases, the model may struggle to maintain a strong connection between the context and the generated text, leading to less personalized or relevant outputs.
Regarding the complexity of the contextual information, Context Steering may face challenges when dealing with highly intricate or abstract contexts. If the contextual information is too complex or ambiguous, the model may struggle to accurately capture the nuances of the context and incorporate it effectively into the generated text. This can result in outputs that are either overly simplistic or fail to reflect the subtleties of the input context.
Furthermore, the interpretability of the model's decisions may be compromised when dealing with complex contextual information, making it challenging for practitioners to understand and adjust the model's behavior effectively.

How can the principles behind Context Steering be applied to other types of generative models beyond autoregressive language models, such as diffusion models or generative adversarial networks?

The principles behind Context Steering can be adapted to other types of generative models by incorporating mechanisms to modulate the influence of contextual information on the generation process. For diffusion models, which model the data distribution directly, contextual information can be integrated into the conditioning variables to guide the generation of samples. By adjusting these conditioning variables based on the context, practitioners can steer the generation process towards desired outcomes.
In the case of generative adversarial networks (GANs), contextual information can be used to inform the generator network about specific attributes or characteristics that should be present in the generated samples. By conditioning the generator on the context, the GAN can produce more tailored and contextually relevant outputs.
Overall, the key idea is to leverage contextual information to guide the generative process in a way that enhances personalization, mitigates bias, and improves the relevance of the generated outputs across a variety of generative models beyond autoregressive language models.