CIE: Controlling Language Model Text Generations Using Continuous Signals

Abstract: Aligning LLMs with user intent is becoming increasingly relevant to enhance user experience. This calls for designing methods that can allow users to control the properties of the language that LMs generate. For example, controlling the length of the generation, the complexity of the language that gets chosen, the sentiment, tone, etc. Most existing work attempts to integrate users' control by conditioning LM generations on natural language prompts or discrete control signals, which are often brittle and hard to scale. In this work, we are interested in \textit{continuous} control signals, ones that exist along a spectrum that can't easily be captured in a natural language prompt or via existing techniques in conditional generation. Through a case study in controlling the precise response-length of generations produced by LMs, we demonstrate how after fine-tuning, behaviors of LLMs can be controlled via continuous signals -- as vectors that are interpolated between a "low" and a "high" token embedding. Our method more reliably exerts response-length control than in-context learning methods or fine-tuning methods that represent the control signal as a discrete signal. Our full open-sourced code and datasets are available at https://github.com/vsamuel2003/CIE.

Overview: Controlling LLM Text Generations Using Continuous Signals

The paper "CIE: Controlling LLM Text Generations Using Continuous Signals" addresses a significant challenge in the field of natural language processing: aligning LLM outputs with user specifications across multiple dimensions, including response length and other nuanced characteristics. The authors introduce a method called Control through Interpolated Embeddings (CIE) that leverages continuous signals to exert fine-grained control over the properties of generated text, offering a more flexible approach compared to traditional discrete signals or prompt engineering methods.

Methodology

CIE employs continuous control signals that are expressed as interpolated embeddings within a defined spectrum, diverging from conventional discrete tokens or templated prompts. The method fine-tunes LLMs with two control embeddings representing the lower and upper bounds of a given attribute spectrum (e.g., response length). During text generation, a control embedding is interpolated based on user-specified values within this spectrum, allowing nuanced adjustments to the properties of the output. This interpolation transforms the desired attribute control into a vector space representation that can effectively guide the model’s generative process.

Experimental Findings

The authors present empirical results on several datasets, including VerbosityCTRL and Alpaca-LI, demonstrating CIE’s superior capability for response-length control compared to discrete signal approaches like the Ruler method. Improvements are quantified using metrics such as Conditioning Precision Ratio (CPR) and its variants, CPR@k, which considers tolerances around desired output lengths. Across different models, including LLaMA and gemma variants, CIE consistently outperforms prompt-based baselines and discrete token approaches, achieving more precise adherence to user-specified attributes without degrading the quality of the LLM's outputs.

Key Insights and Implications

• Numerical Results:
  • CIE shows significant improvements in CPR and CPR@k metrics, with some model configurations experiencing enhancements of over 20 percentage points in adhering to specified attributes.
  • The approach exhibits robustness across both in-distribution and out-of-distribution data, indicating its generalizability and potential for implementation in varied contexts.
• Practical and Theoretical Implications:
  • Practical: The technique enables users to specify and attain desired output characteristics reliably, fostering applications where precise control over text attributes is critical, such as personalized communication tools and interactive systems.
  • Theoretical: The embedding-based continuous control signals challenge existing paradigms in model conditioning, suggesting possible extensions to other continuous attributes and composite control scenarios involving multiple simultaneous adjustments.

Future Directions

The promising results of CIE indicate unexplored potential for managing other continuous and ordinal text attributes, such as sentiment intensity, linguistic complexity, or even abstract qualities like humor. Further research could investigate the compositionality of control vectors—how they interact when multiple attributes are simultaneously conditioned—and refine the method for broader applications in AI systems requiring adaptive language generation functionalities.

In conclusion, this paper contributes a compelling advancement in implementing continuous control signals within LLMs, shedding light on new possibilities for refining and directing AI-generated outputs according to user-specific demands.