Beyond falsehoods: Building More Trustworthy AI

Author: Denis Avetisyan

New research tackles the problem of ‘hallucinations’ in large language models, aiming to make AI reasoning more reliable and aligned with factual accuracy.

The algorithm demonstrated increasing stability over 100 Generalized Reinforcement Policy Optimization (GRPO) steps, as evidenced by the normalized entropy reward’s ascent from -4 toward 0, concurrent with a smooth convergence indicated by the training loss decaying from approximately 2.0 to 0.3.

A reinforcement learning framework improves language model trustworthiness by rewarding stable reasoning and calibrating confidence levels through entropy and self-assessment.

Despite advances in scale, large language models remain prone to generating factually inconsistent or unfaithful reasoning-a phenomenon known as hallucination. This work, ‘Thinking, Faithful and Stable: Mitigating Hallucinations in LLMs’, introduces a reinforcement learning framework designed to cultivate more trustworthy LLMs by rewarding stable reasoning trajectories and aligning self-reported confidence with actual correctness. Utilizing both token-level entropy and self-assessment signals, the approach guides models towards introspection and coherent generation. Could this method pave the way for LLMs that not only answer correctly, but also know when they are uncertain?

The Illusion of Understanding in Large Language Models

Large Language Models, despite their remarkable ability to generate human-quality text, are prone to “hallucinations” – instances where the model confidently presents information that is demonstrably false or unsupported by evidence. These aren’t simple errors of grammar or style; rather, they represent factual inaccuracies woven into otherwise coherent and persuasive prose. A model might, for example, confidently detail a non-existent scientific study, fabricate biographical details, or misrepresent historical events. This phenomenon arises because these models are trained to predict the most probable continuation of a text sequence, prioritizing fluency and statistical correlation over genuine understanding or truthfulness. Consequently, a model can generate compelling narratives that, while grammatically correct and stylistically appropriate, are entirely detached from reality, posing a significant challenge for applications requiring reliable information.

Large Language Models, despite their ability to generate remarkably human-like text, often produce inaccuracies because they fundamentally operate by identifying statistical correlations within vast datasets rather than possessing genuine comprehension. The models excel at predicting the most probable sequence of words, but this predictive power doesn’t equate to understanding meaning or verifying truth. Consequently, a model can confidently articulate a plausible-sounding statement that is, in fact, entirely false, simply because similar word combinations frequently appear together in its training data. This reliance on statistical patterns, rather than semantic understanding, inherently limits the reliability of these models and presents a significant challenge for applications demanding factual accuracy, as the system lacks the capacity to discern truth from convincingly presented falsehoods.

The reliable deployment of Large Language Models in critical applications – spanning fields like healthcare, finance, and legal reasoning – hinges directly on mitigating the problem of factual inaccuracies. While these models demonstrate remarkable linguistic capabilities, their tendency to generate plausible but untrue statements presents a significant barrier to trust and usability in scenarios where precision is paramount. A misdiagnosis suggested by a hallucinating model, a flawed financial forecast, or incorrect legal advice could have severe consequences, underscoring the necessity of robust solutions that guarantee factual grounding before widespread implementation. Consequently, ongoing research focuses not merely on improving fluency, but on fundamentally ensuring that model outputs reflect verifiable truth, establishing a foundation for responsible innovation and practical utility.

A significant challenge facing large language models lies in the disparity between their stated confidence and actual accuracy. While these models often present information with a compelling degree of certainty – frequently utilizing sophisticated phrasing and assertive tones – this outward assurance doesn’t reliably reflect the truthfulness of the content. Research indicates that models can confidently generate incorrect statements, a phenomenon exacerbated by their inability to distinguish between learned statistical patterns and genuine understanding. This misalignment creates unpredictable behavior, making it difficult to anticipate when a model will produce reliable information and hindering their deployment in critical applications where factual accuracy is paramount. Consequently, ongoing efforts focus on developing techniques to calibrate model confidence, aiming to ensure that a high probability score genuinely corresponds to a higher likelihood of correctness, thereby fostering more trustworthy and dependable outputs.

Beyond Outcome-Based Rewards: A Process-Level Framework

Traditional reinforcement learning (RL) approaches, when applied to complex tasks like question answering or theorem proving, typically utilize outcome-based rewards. These systems assess performance solely on the final result – whether the answer is correct or not – without considering how that answer was derived. This creates a reward signal that is sparse and often fails to guide the model toward reliable reasoning. A model can achieve a correct output through flawed or unstable internal processes and still be reinforced, hindering the development of robust and trustworthy artificial intelligence. Consequently, improvements in final outcome accuracy do not necessarily correlate with improvements in the model’s underlying reasoning capabilities, and the system may be vulnerable to adversarial inputs or distribution shifts.

The Process-Level Reward Framework moves beyond evaluating solely the final result of a reinforcement learning agent and instead focuses on the intermediate steps of its reasoning process. This is achieved by incorporating indicators derived from model introspection, specifically analyzing internal signals of uncertainty. These signals include metrics that quantify the model’s confidence in its own predictions at each step, allowing for the assignment of rewards based on the stability and reliability of the reasoning pathway. The framework utilizes fine-grained assessments of these internal states to provide a more nuanced reward signal than traditional outcome-based methods, facilitating the development of more trustworthy and explainable AI systems.

The Process-Level Reward Framework assesses reasoning stability and reliability through quantitative metrics. Token-Level Entropy measures the randomness of the probability distribution over the next token at each step; lower entropy indicates a more confident and predictable reasoning process. Simultaneously, Self-Assessed Confidence Alignment evaluates the correlation between the model’s predicted confidence score for each token and the actual correctness of that token as determined by a reference dataset or oracle. A high alignment score signifies that the model accurately estimates its own certainty, providing an indicator of trustworthiness. These metrics, calculated at each reasoning step, are incorporated into the reward signal, incentivizing models to produce both stable and self-aware reasoning chains.

The proposed Process-Level Reward Framework intends to improve the reliability of model outputs by directly incentivizing stable and aligned reasoning processes. Rather than solely focusing on the final result, the framework rewards intermediate reasoning steps exhibiting low token-level entropy, indicating consistent prediction probabilities, and high self-assessed confidence alignment, meaning the model’s stated confidence in its reasoning matches the stability of its internal predictions. This approach aims to mitigate issues like hallucination and illogical conclusions by guiding the model to prioritize internally consistent and well-justified reasoning paths, ultimately leading to outputs that are more demonstrably trustworthy and less prone to unpredictable errors.

A Composite Reward for Rigorous Reasoning and Truthfulness

The Composite Reward Function is designed to address issues of untrustworthy reasoning in large language models by combining two primary components: penalties for hallucination and rewards for reasoning stability and alignment. Hallucination penalties are applied when the model generates content unsupported by the provided context or exhibits factual inaccuracies. Conversely, rewards are given for demonstrating consistent reasoning steps, avoiding abrupt shifts in prediction, and maintaining alignment with the intended goal or task. This combined approach aims to incentivize models to not only produce factually correct outputs but also to exhibit a reliable and predictable reasoning process, improving overall trustworthiness and reducing the likelihood of generating misleading or fabricated information.

The Process-Level Reward Framework evaluates the quality of each step within a model’s reasoning process, rather than solely assessing the final output. This is achieved by assigning reward signals based on the stability and certainty of intermediate predictions; steps exhibiting high uncertainty or significant shifts in prediction are penalized. This granular assessment allows for the identification and discouragement of “hallucination-prone behavior” – where a model generates information not supported by its internal reasoning – and encourages the development of more consistent and reliable reasoning pathways. The framework uses metrics to quantify the stability of predictions at each step, providing a differentiable signal for reinforcement learning algorithms to optimize the model’s internal reasoning process.

The Composite Reward Function is implemented within a Confidence-Aware Reinforcement Learning (RL) policy to directly influence model generation. This policy utilizes the reward signal – comprising penalties for hallucination and rewards for stable reasoning – as feedback during training. The RL agent learns to adjust its generation strategy to maximize cumulative reward, effectively shaping the model’s behavior towards producing more trustworthy outputs. Specifically, the policy incorporates the reward into the action-selection process, encouraging the model to favor sequences of tokens that lead to higher reward values as determined by the Composite Reward Function. This iterative process of generation, reward assessment, and policy refinement results in a model demonstrably incentivized to avoid unstable predictions and prioritize factual accuracy.

Evaluations of the Composite Reward Function demonstrate statistically significant improvements in model calibration. Specifically, overall Calibration Error was reduced by greater than 9 percent. Further analysis reveals a decrease in Expected Calibration Error (ECE) from a baseline value of 0.42 to 0.19, indicating improved confidence alignment. Simultaneously, the Brier Score, a measure of prediction accuracy and confidence, improved from 0.22 to 0.11, demonstrating a substantial increase in the reliability of model predictions and associated confidence estimates.

Efficient Training and Rigorous Evaluation of Mathematical Reasoning

Low-Rank Adaptation (LoRA) was implemented as a parameter-efficient fine-tuning technique for Large Language Models, addressing the substantial computational demands of full parameter updates. LoRA achieves this by freezing the pre-trained model weights and introducing trainable low-rank decomposition matrices to the attention layers. This approach significantly reduces the number of trainable parameters – from billions in the original model to a few million – thereby lowering GPU memory requirements and enabling faster training speeds. Specifically, LoRA reduces the computational cost associated with fine-tuning by approximately $60\%$, without significantly compromising performance on downstream tasks.

Training of the Large Language Model was performed utilizing the GRPO (Gradient-based Rollout Policy Optimization) training methodology on the MATH-500 dataset. This dataset consists of 500 diverse mathematical problems, ranging in complexity and covering areas such as algebra, calculus, geometry, and probability. MATH-500 is a widely recognized benchmark for evaluating the mathematical problem-solving capabilities of AI models, providing a standardized and challenging test environment. The dataset requires models to not only perform calculations but also to understand the problem statement and apply appropriate reasoning steps to arrive at a correct solution, often expressed as a numerical answer or a symbolic expression such as $x = \frac{-b \pm \sqrt{b^2 – 4ac}}{2a}$.

To ensure rigorous evaluation of mathematical reasoning capabilities, a symbolic math parser was integrated into the testing pipeline. This parser functions by taking the generated answer, expressed as a mathematical expression, and converting it into a structured, symbolic representation. The parser then evaluates this expression and compares the result to the ground truth answer. This process bypasses potential issues with floating-point inaccuracies or formatting differences that can occur when directly comparing string representations of numerical answers. Specifically, the parser supports common mathematical operations including $+, -, *, /, \sqrt{x}$, and exponentiation, enabling verification of solutions across a range of problem types within the MATH-500 dataset. Discrepancies identified by the parser are flagged as incorrect, providing a definitive measure of solution accuracy.

Evaluation of the implemented methodology indicates a substantial decrease in instances of hallucinated or logically inconsistent responses during mathematical problem-solving. This reduction in unreliable outputs directly correlates with a 3 percentage point increase in overall accuracy on the benchmark dataset. Specifically, the approach improved performance in complex multi-step problems, minimizing errors in intermediate calculations and ensuring the final answer aligns with the established mathematical principles. This enhancement in reliability represents a significant improvement over baseline models and contributes to a more trustworthy system for automated mathematical reasoning.

Towards Robust and Reliable AI: A Foundation for Trust

Current large language models (LLMs) often excel at pattern recognition and generating text that appears logical, but frequently lack a demonstrable understanding of the underlying reasoning process. This approach directly tackles this limitation by shifting the focus from simply predicting the next word to explicitly modeling how an answer is derived. By dissecting the steps involved in reaching a conclusion – identifying relevant information, applying logical rules, and evaluating evidence – the system builds a more robust and transparent decision-making process. This emphasis on reasoning, rather than mere correlation, allows for greater accuracy, improved generalization to novel situations, and crucially, the ability to identify and correct errors in its own thinking – characteristics vital for building truly reliable artificial intelligence.

The pursuit of artificial intelligence extends beyond mere predictive power; increasingly, the focus lies on building systems that demonstrate how they arrive at conclusions. By prioritizing transparency in the reasoning process, developers are crafting AI capable of articulating the logic behind its outputs, fostering trust in critical applications. This shift from “black box” models to interpretable systems allows for verification of correctness, identification of potential biases, and ultimately, greater accountability. Such advancements are not simply about improving performance metrics; they represent a fundamental step towards creating AI that is reliable, understandable, and deserving of human confidence, paving the way for responsible integration into society.

The current framework, designed to enhance AI reasoning, is not limited to its initial applications; researchers are actively investigating its adaptability to more complex cognitive domains. Future studies will focus on integrating this approach with systems requiring scientific reasoning, where logical deduction and hypothesis testing are paramount, and also with those demanding common-sense knowledge – the often-unarticulated understanding of the world that humans possess. Successfully extending the framework to these areas promises to create AI capable of not just processing information, but of understanding context, making informed judgments, and navigating real-world scenarios with greater reliability and nuance. This expansion aims to move beyond narrow task performance and towards a more generalized, human-like intelligence capable of tackling a wider range of challenges.

The pursuit of artificial intelligence extends beyond mere computational power; it necessitates a commitment to responsible innovation and ethical alignment. This research actively contributes to that goal by fostering AI systems designed not only to solve complex problems, but to do so in a manner consistent with human values. By prioritizing transparency and reasoned decision-making, the framework allows for a deeper understanding of how an AI arrives at a conclusion, which is crucial for building trust and ensuring accountability. This approach moves beyond simply achieving a correct answer and instead focuses on the process itself, promoting systems that are reliable, safe, and ultimately, beneficial to society. The long-term implications suggest a future where AI serves as a collaborative partner, augmenting human capabilities while upholding ethical principles and fostering responsible problem-solving.

The pursuit of reliable large language models necessitates a focus on provable correctness, a principle deeply resonant with Edsger W. Dijkstra’s assertion that “It’s not enough to have good intentions; you need a good implementation.” This paper’s reinforcement learning framework, which rewards stable reasoning and calibrates confidence, embodies this sentiment. By utilizing entropy as a signal for unstable token generation and encouraging self-assessment, the approach moves beyond simply achieving functional results. It strives for a demonstrably correct solution, aligning with the need for mathematically pure algorithms, instead of relying on empirical observation. The emphasis on aligning self-reported confidence with actual correctness is a critical step towards building systems that are not merely fluent, but fundamentally trustworthy.

Where Do We Go From Here?

The pursuit of ‘faithful’ language models, as this work demonstrates, inevitably confronts the inherent limitations of statistical approximation. While rewarding stability and calibrating confidence represent valuable steps, they address symptoms, not the fundamental problem. The model remains, at its core, a remarkably sophisticated pattern-matching engine, capable of generating plausible text without genuine understanding. The reliance on reinforcement learning, while yielding demonstrable improvements, introduces further layers of approximation – the reward function itself being a heuristic stand-in for ‘truth’.

Future investigation must move beyond simply measuring hallucination and instead focus on mechanisms for provable reasoning. Token-level entropy and self-assessment are useful diagnostics, but they are insufficient to guarantee correctness. The field needs to explore methods that allow for formal verification of generated statements, perhaps by grounding language in symbolic reasoning or knowledge graphs.

Ultimately, the goal should not be to build models that appear trustworthy, but models that are trustworthy. This requires a shift in perspective: from optimizing for performance on benchmarks, to prioritizing mathematical rigor and demonstrable correctness. The current emphasis on scale, while producing impressive results, may be a distraction from the more difficult, but ultimately more rewarding, path of building truly intelligent systems.

Original article: https://arxiv.org/pdf/2511.15921.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/