Transparent AI Enhancements in Human Language and Agent Actions
Open Access DepositedArtificial Intelligence (AI), particularly through the advent of Large Language Models (LLMs), has emerged as a transformative force, reshaping various facets of contemporary society. These advanced LLMs, pivotal in processing and generating human-like text, rely fundamentally on Natural Language Processing (NLP) and Reinforcement Learning (RL) techniques. This research addresses specific challenges related to Transparent AI in both NLP and RL, focusing on enhancing content understanding and agent interpretation, respectively. Transparent AI emphasizes clarity and interpretability in AI systems, which is crucial for building trust and ensuring ethical AI deployment. On one hand, by improving the NLP model's ability to understand human language contents, we aim to enhance the accuracy and efficiency of identifying and categorizing textual data in tasks such as Entity Extraction. On the other hand, interpreting the motivations behind agent actions in RL is essential for developing AI systems that are not only transparent but also ethical and interpretable. Collectively, these contributions advance the field of AI by improving NLP model performance and RL agent decision-making transparency. In the NLP area, utilizing data from the "covid19positive" subreddit, we employed NLP to automatically identify COVID-19 cases and extract their reported symptoms. Firstly, we trained a Bidirectional Encoder Representations from Transformers (BERT) classification model with chunking to identify COVID-19 cases. Then, we developed a novel QuadArm model, which incorporates Question Answering, Dual-corpus Expansion, Adaptive Rotation Clustering, and Mapping, to extract symptoms. To further enhance the above framework, some novel techniques we proposed can be incorporated: (1) An Edit-distance-weighted fine-tuning method that amplifies the emphasis on semantics, thereby improving models' semantic understanding. (2) A Bayesian Iterative Prediction algorithm, which iteratively refines likelihood and prior probabilities until prediction labels converge, thereby enhancing the accuracy and robustness of classification models. (3) A Lexical-based Dual Ranking Interpreter and a Bi-criteria Denoising strategy to strengthen models' explanatory capabilities. (4) the Multiple Synonymous Questions BioBERT, which integrates question augmentation, rather than the typical single question used by traditional BioBERT, to elevate BioBERT’s performance on medical Question-Answering tasks. On the other hand, RL is a powerful tool for solving complex decision-making tasks, but its lack of transparency has been a major challenge in deploying RL systems for real-world decision making. We present the Advantage Actor-Critic with Reasoner (A2CR) framework, incorporating an innovative Reasoner Network Module. The Reasoner is designed for integration into any Actor-Critic-based RL models to enhance their interpretability. A2CR consists of three interconnected networks: the Policy Network, the Value Network, and the Reasoner Network. By predefining and classifying the underlying purpose of the actor's actions, A2CR automatically generates a more comprehensive and interpretable paradigm for understanding the agent's decision-making process. It offers a range of functionalities such as purpose-based saliency, early failure detection, exploration encouragement identification, and model supervision, thereby promoting responsible and trustworthy RL. Evaluations conducted in action-rich Super Mario Bros environments yield intriguing findings: (1) Purpose-based saliencies are more focused and comprehensible. (2) Fluctuations in short-term action purpose classification act as an early warning for potential decision-making failure. (3) Action purpose classification proportions shift with intensifying RL exploration levels, revealing the evolving directions of agent purpose. (4) Convergence in classification proportions signals model training completion.
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