JAM: Controllable and Responsible Text Generation via Causal Reasoning and Latent Vector Manipulation

2025-02-28

AI Summary

Large language models (LLMs) suffer from opaque text generation, lacking interpretability and accountable controllability. To address this, we propose the first controllable generation framework that tightly couples causal inference with latent-space vector manipulation. Our method first discovers and models the intrinsic causal structure underlying LLM generation within its latent space; then performs fine-grained, traceable interventions on latent vectors guided by a learned causal graph; and finally establishes a multidimensional evaluation framework integrating HHH (Helpful, Honest, Harmless) principles and GPT-4 alignment. Empirically, our approach achieves up to 22% improvement over state-of-the-art controllable generation methods on critical metrics—including toxicity mitigation and value alignment—while significantly reducing computational overhead. Human evaluations confirm enhanced satisfaction and factual consistency. The framework thus enables interpretable, intervention-aware, and computationally efficient responsible generation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

While large language models (LLMs) have made significant strides in generating coherent and contextually relevant text, they often function as opaque black boxes, trained on vast unlabeled datasets with statistical objectives, lacking an interpretable framework for responsible control. In this paper, we introduce JAM (Just A Move), a novel framework that interprets and controls text generation by integrating cause-effect analysis within the latent space of LLMs. Based on our observations, we uncover the inherent causality in LLM generation, which is critical for producing responsible and realistic outputs. Moreover, we explore latent vectors as fundamental components in LLM architectures, aiming to understand and manipulate them for more effective and efficient controllable text generation. We evaluate our framework using a range of tools, including the HHH criteria, toxicity reduction benchmarks, and GPT-4 alignment measures. Our results show that JAM achieves up to a 22% improvement over previous Controllable Text Generation (CTG) methods across multiple quantitative metrics and human-centric evaluations. Furthermore, JAM demonstrates greater computational efficiency compared to other CTG methods. These results highlight the effectiveness and efficiency of JAM for responsible and realistic text generation, paving the way for more interpretable and controllable models.

Problem

• Enhance interpretability and control in text generation.
• Integrate causality analysis for responsible text outputs.
• Manipulate latent vectors for efficient controllable generation.

Innovation

• Integrates cause-effect analysis in LLM latent space
• Manipulates latent vectors for controllable text generation
• Improves efficiency and responsibility in text generation