Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models

Authors: gabriel y arteaga
thomas b schön
nicolas  pielawski
Venue: NLDL_org_2025_Conference
Published: 2024-09-02 21:14:01

Abstract: Uncertainty estimation is a necessary component when implementing AI in high-risk settings, such as autonomous cars, medicine, or insurances. Large Language Models (LLMs) have seen a surge in popularity in recent years, but they are subject to hallucinations, which may cause serious harm in high-risk settings. Despite their success, LLMs are expensive to train and run: they need a large amount of computations and memory, preventing the use of ensembling methods in practice. In this work, we present a novel method that allows for fast and memory-friendly training of LLM ensembles. We show that the resulting ensembles can detect hallucinations and are a viable approach in practice as only one GPU is needed for training and inference.

Main paper: 
 introduction: 
 LLMs have recently grown in popularity, thanks to their ability to interpret natural language and generate answers that resemble human discussions, even surpassing human performance in specific tasks [1] .However, these models face a significant challenge known as hallucination, where outputs that seem plausible may either deviate from instructions or lack factual accuracy. Hallucinations can broadly be categorized into two types [2] : faithfulness hallucinations, where the LLM deviates from provided instructions, and factual hallucinations, where there is a disparity between the generated content and verifiable facts. The risk arises when individuals unaware of these limitations mistakenly treat such outputs as ground-truth, leading to decisions based on erroneous information -a concern particularly relevant to safety-critical areas such as healthcare.Techniques leveraging natural language inference models and retrieval-based methods to detect hallucinations have shown promise in specific applications like summarization and open-domain question answering [3] [4] [5] . However, the effectiveness of these methods is typically limited to a narrow set of tasks, which restricts their generalizability across the broader spectrum of LLM applications.Given these limitations, uncertainty estimation methods emerge as a compelling alternative for detecting both types of hallucinations [6] . Unlike taskspecific approaches, uncertainty estimation uses the model's own confidence in its predictions to identify if the outputs are unfaithful or factually incorrect.Recent work in uncertainty quantification in LLMs have emerged, with approaches like deep ensembles [7] [8] [9] [10] and sample-based methods which use stochastic sampling techniques [11] [12] [13] [14] [15] . However, samplebased methods seldom provide reliable uncertainty estimates as they rely on the distribution of a single model's outputs, which may not fully capture the true uncertainty in the model's predictions. While deep ensembles advertise more robust uncertainty estimates by aggregating predictions from multiple independently trained models, they come with significant computational bottlenecks, especially when applied to larger LLMs, as they require substantial resources for training and inference.To address these limitations, we propose a fast and memory-efficient deep ensemble method which is able to provide reliable uncertainty estimates. Figure 1 illustrates our proposed method, where Low-Rank Adaptation (LoRA) matrices are added on top of a pretrained model and used for fine-tuning. LoRA allows the whole ensemble to be trained cheaply and flexibly [16] , with each ensemble member having an individual rank-one fast weight matrix 1 . This allows the members to be diverse. An uncertainty metric is used afterward and fed into a binary classifier, which is trained to discriminate between hallucinated and correct predictions.We demonstrate that our method can effectively detect both factual and faithfulness hallucinations. Our method achieves a 97.8% accuracy in detecting faithfulness hallucinations, outperforming existing baselines. Additionally, our method attains a 68% accuracy in detecting factual hallucinations without compromising overall predictive performance. These results suggest that our method not only enhances the detection of hallucinations in LLMs, but also offers a practical solution for deploying these models in resource-constrained settings.The main contributions of this paper are: (1) a fast and memory-efficient method for fine-tuning pretrained LLMs using LoRA matrices and componentspecific rank-1 matrix modifications, which reduces computational overhead and enables the effective use of ensemble methods; (2) a novel approach to hallucination detection that reformulates it as a binary classification task, leveraging uncertainty estimates from LLMs as features to distinguish between hallucinated and accurate content; and (3) demonstrating the practicality of our method for use with LLMs on minimal hardware, requiring only a single A40 GPU for both training and inference, thereby showcasing its efficiency and scalability. 
related work: 
 We believe that distinguishing between the two types of hallucinations introduced by Huang et al. [2] provides valuable insights into the behavior of LLMs and highlights the distinct challenges associated with each category. Therefore, we adopt this terminology throughout this paper.Hallucination Detection in LLMs Various approaches have been proposed to identify when an LLM diverges from instructions or deviates from contextual cues in the input. This work is especially critical in tasks like summarization, where adherence to the provided context is crucial for generating accurate summaries. These methods often leverage natural language inference models to compute entailment scores, which are then used to detect instances of unfaithfulness in the generated outputs [3, 5, 17] .Similarly, some research already focused on detecting when LLMs produce factual hallucinations, where their generated content deviates from verifiable facts. Some methods have been developed for situations where the correct answer is known in advance, such as summarization and open-domain question answering [4, [18] [19] [20] [21] . These approaches often involve comparing the generated content against a source document that is known to be accurate. When the ground-truth is not available, some methods leverage retrieval techniques to extract reliable information for verification [22, 23] or use LLMs themselves, using a prompt pipeline to facilitate hallucination detection [24] .Despite their effectiveness in specific contexts, many of these methods are constrained by their task-specific nature, limiting their generalizability across different LLM applications.Uncertainty Estimation Methods Uncertainty estimation methods are more general, offering greater versatility in hallucination detection [6] . Sample-based methods use sampling decoding techniques to introduce stochasticity in the LLM's responses, where a higher variance of the output is an indication of the model's uncertainty [13] [14] [15] .Another approach involves deep ensembles, which have been hypothesized to provide more informative uncertainty estimates compared to traditional methods [11, 13] . Deep ensembles leverage multiple model instances to capture a range of predictions, thus enhancing the robustness of uncertainty assessments [25] . However, implementing deep ensembles for LLMs through both pretraining [7, 8] and finetuning [9, 10] has primarily been constrained to smaller models. Scaling these ensembles to compete with state-of-the-art LLMs [26, 27] typically requires significant computational resources.Memory-Efficient Approaches To overcome the computational challenges associated with training deep ensembles of LLMs, recent research has focused on memory-efficient alternatives. One such approach, which serves as the backbone of our method, is BatchEnsemble [28] , used to pre-train LLM ensembles more efficiently [29] . However, achieving state-of-the-art performance through pre-training can still be prohibitively expensive.Recent studies have proposed a memory-friendly strategy that fine-tunes LLM ensembles from pretrained weights, rather than training from scratch [30, 31] . This method, referred to as a LoRA Ensemble, assigns each ensemble member its own set of LoRA matrices [32] . While this approach has been utilized to compute uncertainty estimates [30, 31, 33] , it has not been specifically applied to hallucination detection tasks. Our approach is similar in two ways: it reduces training costs by utilizing pre-trained weights, and it employs LoRA matrices during fine-tuning. However, unlike the LoRA Ensemble, our method does not rely on LoRA matrices to represent ensemble members. Instead, after fine-tuning, we merge the LoRA matrices with the pre-trained weights and represent the ensemble using sets of rank-one fast weights.To quantify the uncertainty associated with an LLM's predictions, we use the predictive entropy of the output distribution, a concept rooted in information theory. Let x <t = {x 1 , . . . , x t-1 } represent the preceding tokens, which serve as the input for predicting the target token x t at time step t. The predictive entropy is then defined as:EQUATIONwhere D refers to the overall training data distribution. The predictive entropy can further be divided into its two subcomponents aleatoric and epistemic uncertainties:EQUATIONEpistemic uncertainty captures the lack of knowledge of a system, which shrinks as more data is made available. Conversely, aleatoric uncertainty represents the noise -the variability of the dataand is therefore irreducible [34] .P (x t |x <t ; D) cannot be directly computed and is approximated with:EQUATIONwhere θ m is the parameters associated to the m th model. The estimate of the predictive entropy is an estimate of the predicted uncertainty, and will yield the exact predictive uncertainty as M → ∞ [35] .We employ deep ensembles [25] to approximate Equation (3). However, training those deep ensembles may require prohibitively high computational resources, available to few organizations. We propose using the BatchEnsemble method [28] , but rather than pre-training each ensemble member [29] , we adapt the method to fine-tune already pre-trained models. BatchEnsemble optimizes memory usage, which is a critical advantage over traditional ensembles. In a standard ensemble, memory requirements grow linearly with the number of ensemble members, with complexity increasing to O(M mn) per layer, where M is the number of ensemble members and mn represents the size of the weight matrices. In contrast, BatchEnsemble reduces memory complexity to O(mn + M (m + n)) per layer, significantly lowering the memory footprint by sharing a single weight matrix U across all ensemble members and augmenting it with trainable vectors r i ∈ R m×1 and s i ∈ R n×1 . The outer product of these vectors yields a fast weight matrix V i , allowing each ensemble member's weight matrix to be represented as the Hadamard product of the shared weight U and the fast weight V i :EQUATIONWe further adapt this method by substituting the shared weight with a pre-trained weight, setting U = ω pretrained . To introduce diversity into the ensemble, we randomly initialize the fast weights. This initialization must be done carefully to preserve the knowledge stored in the pre-trained weights; initializing the fast weights with a mean of 1 is crucial to avoid disrupting the pre-trained knowledge. For more details on the weight initialization procedure, please refer to the Appendix.To minimize the computational demands during fine-tuning, we apply the LoRA method [32] . We retain the pre-trained weight matrix U as U 0 and introduce low-rank matrices B ∈ R m×r and A ∈ R r×n , where r ≪ min(m, n). This approach allows us to update U as U 0 + BA, reducing the number of parameters that need to be trained while maintaining model performance. In contrast to the original LoRA paper [32] , our method applies LoRA to all modules, which we believe enhances ensemble diversity and leads to more reliable uncertainty estimates.We hypothesize that injecting noise into the ensemble during training may enhance model diversity and improve uncertainty estimates. To explore this, we employ anchored ensembling [36] as one of our methods. However, this approach can be unstable when scaled to larger models. To address this issue, we incorporate a normalization term, as suggested in [6] , to stabilize the training process.To detect hallucinations in an LLM, we design a subtask for a dedicated classifier. This task is framed as a binary classification problem, where the classifier is trained on a dataset containing uncertainty estimates from our ensemble and corresponding binary labels indicating whether the LLM is hallucinating or not. The choice of classifier depends on the practitioner's needs. For instance, one might choose a fast inference model like a shallow decision tree to minimize computational overhead, or opt for a more expressive model, such as a random forest, for enhanced performance. 
design of experiments: 
 We aim to design experiments that can clearly differentiate between faithfulness and factual hallucinations, enabling us to assess the performance of our method for each type.We evaluate our proposed adaptations to BatchEnsemble during fine-tuning, including BatchEnsemble with noise injection (NI). For uncertainty-based experiments, we compare against two baselines: a sample-based method that approximates the output distribution using repeated prompting with stochastic sampling (temperature = 0.5, top-p = 0.99, top-k = 5) [13, 15] , and a LoRA Ensemble, which applies regularization to LoRA B matrices as described by Wang et al. on MMLU and SQuAD datasets [30] .All models, including baselines, are fine-tuned on all modules using LoRA adapters with a rank of r = 8 and a scaling factor of α = 32. For BatchEnsemble, the LoRA matrices are merged into the shared weights after training. Unless otherwise specified, we use an ensemble size of 4 across all experiments. This ensemble size determines the number of rank-1 matrices for BatchEnsemble, the number of adapters for the LoRA ensemble, and the number of samples generated by the samplebased method. Additionally, all models, including baselines, use the uncertainty measures described in Equation (2) .We also evaluate our methods against a single model fine-tuned from pre-trained weights to ensure fair performance comparison. All models in this study leverage Mistral-7B-Instruct-v0.2 as a core component, either by directly fine-tuning the pretrained weights or by incorporating them into more complex architectures like BatchEnsemble, where the shared weights are replaced by the pre-trained weights [26] .To detect faithfulness hallucinations, we use the SQuAD and SQuAD 2.0 datasets [37, 38] . The datasets consist of contexts and questions, with SQuAD featuring answerable questions and SQuAD 2.0 including unanswerable ones. We instructed the LLMs to respond with "I don't know" if the answer was not in the context. Any other response to unanswerable questions indicated a faithfulness hallucination. Initially, we trained the models only on answerable questions, but this led to hallucinations across all test points. We then adjusted our approach by including 1/3 unanswerable questions in training to balance the model's responses.For factual hallucination detection, we use the MMLU dataset [39] , as the pre-trained Mistral 7B model [26] used it as a benchmark without train-ing on it. MMLU contains multiple-choice questions from diverse knowledge areas. Models were instructed to select one of the available choices; incorrect answers were labeled as factual hallucinations.We assess the predictive performance of our models on downstream tasks to evaluate if improved uncertainty estimates impact model accuracy. Models are fine-tuned on SQuAD and MMLU datasets, and evaluated using the F1 score, exact match for SQuAD, and accuracy for MMLU [37, 39] .To test out-of-distribution detection, all models are fine-tuned on answerable questions from SQuAD 2.0 and evaluated on unanswerable ones, assessing the models' capacity to recognize shifts in data distribution.To evaluate the quality of the uncertainty estimates produced by our method and the baseline approaches, we trained five distinct classifiers: k-Nearest Neighbors, logistic regression, decision tree, random forest, and support vector machine. These classifiers were trained to distinguish between hallucinated and non-hallucinated predictions using uncertainty estimates derived from the outputs of our method and the baselines.For each task-faithfulness hallucination detection, factual hallucination detection, and Out-of-Distribution (OOD) detection-we created custom datasets based on the outputs of our method and the baseline approaches. In all cases, 5000 data points were propagated through the models to generate predictions, which were then annotated as hallucinated or non-hallucinated depending on the task-specific criteria. The resulting datasets were split into 80% for training and 20% for testing, with the split performed randomly using a seed of 42 to ensure consistency across experiments.The annotation methods for hallucinations were task-specific. For faithfulness hallucination detection, as described earlier, any response to unanswerable questions in the SQuAD v2 dataset that deviated from "I don't know" was labeled as hallucinated. For factual hallucination detection, we used multiple-choice questions from the MMLU dataset, where incorrect answers were labeled as hallucinated and correct answers as non-hallucinated. For OOD detection, we followed the same annotation procedure as faithfulness hallucination detection, using the same set of unanswerable questions from SQuAD v2, with the primary difference being how our method and the baselines were trained for this task. In Table 1 , we report the top-1 classification accuracy achieved by the best-performing classifier on the uncertainty estimates from our baselines and models. Detailed classification accuracies for each classifier are provided in Appendix B. All methods, including the baselines, demonstrate a relatively high accuracy-exceeding 92%-in detecting cases where the model faithfully hallucinates. Table 3 describes an example of our method's response to both answerable and unanswerable questions. It shows a notable increase in uncertainty when the model encounters an unanswerable question. The uncertainty sharply decreases after the model generates the first token, suggesting that once it commits to a hallucinated token, it becomes more prone to continue hallucinating-a phenomenon referred to as the "snowballing effect" [40] . More, the high accuracy achieved using the models' uncertainty estimates supports the idea that LLMs' internal states possess an inherent understanding of the generated, hallucinated content, a finding similar to that observed by Azaria & Mitchell [41] .For detecting factual hallucinations, the LoRA Ensemble's uncertainty estimates result in the highest accuracy. A possible explanation is that the high weight decay strategy applied to the LoRA B matrix [30] introduces substantial stochasticity among the ensemble members, leading to improved uncertainty estimates. However, this enhanced expressiveness in uncertainty estimation comes at the cost of predictive performance, which will be discussed further in the next subsection. Additionally, while the sample-based method demonstrates slightly better performance than our proposed approach, this marginal improvement may be attributed to randomness during training or to the task itself. Producing uncertainty estimates for a single token (the choice in a multiple-choice setting) may inherently be easier for the sample-based method.All methods generally performs worse when encountering OOD data points. This indicates that, while LLMs are versatile, uncertainty-based approaches still remain limited in their ability to detect hallucinations for examples that does not lie in-distribution, highlighting the need for further research on detecting hallucinations in OOD scenarios. Predictive performance Table 2 presents the predictive performance of all evaluated models. The results indicate that while all models require fine-tuning to achieve optimal performance on downstream tasks, our proposed BatchEnsemble method with LoRA fine-tuning on shared weights consistently outperforms each baseline across all metrics. Notably, the LoRA ensemble performs worse than the single model despite being an ensemble. This performance discrepancy is likely attributed to the significant weight decay strategy implemented by Wang et al. [30] , which involved fine-tuning only the query and key modules. The combination of high regularization and fine-tuning all modules appears to result in suboptimal performance. Additionally, the results from BatchEnsemble+NI further suggest that implementing regularization strategies effectively in practice poses substantial challenges.All experiments were conducted on a single A40 GPU. Figure 2 illustrates the performance of BatchEnsemble and highlights its advantages. On the left side of the figure, it is shown that as the number of ensemble members increases, the rate at which inference speed improves for BatchEnsemble is lower compared to the sample-based approach. This suggests that BatchEnsemble, by processing all ensemble members' predictions simultaneously in a single forward pass, is faster in inference than the baseline method. On the right side of the figure, the limitations of using a Vanilla ensemble [25] for uncertainty estimation in LLMs are demonstrated. Specifically, as the number of ensemble members increases, the parameter size grows linearly with the Vanilla method, whereas the increase in BatchEnsemble's parameter size remains negligible. 
discussion: 
 This paper focused on exploring uncertainty-based methods for detecting hallucinations in LLMs, and demonstrating the possibility of faithfulness and factual hallucination detection. While comparing against methods beyond uncertainty estimation could provide valuable insights, no standardized benchmark currently exists for systematic comparison between uncertainty-based and other detection strategies. We framed hallucination detection as a binary classification task and designed benchmarks accordingly. However, creating a comprehensive benchmark that incorporates other methods meaningfully remains a challenge. Addressing this gap presents a promising direction for future research.Another area for future exploration is the use of alternative metrics beyond predictive entropy and its subcomponents. Methods such as EigenScore [15] or semantic entropy [13] may offer better performance for specific tasks by capturing uncertainty in more nuanced ways. Integrating these metrics into approaches like BatchEnsemble could enhance both efficiency and robustness of hallucination detection by reducing inference time while maintaining or improving reliability.Our experiments were limited to exploring the method with a single pretrained language model. However, we hypothesize that the method works with other LLM sizes and architectures. Future work could explore the impact of factors such as model size on overconfidence and whether this is reflected in the model's uncertainty estimates, providing fur-ther insights into the robustness and scalability of uncertainty-based hallucination detection.Finally, we believe that aleatoric and epistemic uncertainty may correspond to faithfulness and factual hallucinations, respectively. Our findings suggest that current methods for measuring epistemic uncertainty are not diverse enough to confirm this hypothesis. Future work could involve developing more sophisticated noise injection techniques to generate diverse ensemble members, which may enhance the accuracy of epistemic uncertainty measurements while maintaining predictive performance. Such efforts could open new research directions to better understand how different uncertainty components relate to faithfulness and factual hallucinations. 
conclusion: 
 We have developed a memory-efficient method to fine-tune LLMS using LoRA matrices and rank-1 matrix modifications. A key contribution of our work is demonstrating how pretrained models can be enhanced with an uncertainty estimation component to enable effective hallucination detection. Our approach demonstrated the ability to distinguish between hallucinated and non-hallucinated content, achieving high accuracy in faithfulness detection, even in resource-constrained settings.While our uncertainty-based methods provide valuable tools for hallucination detection, and have shown strong performance in detecting faithfulness hallucinations, challenges remain, particularly in detecting factual hallucinations and managing out-ofdistribution data points. Nevertheless, our approach is an important step towards reducing harmful outputs from LLMs, which is crucial for deploying these models in safety-critical environments.The combination of uncertainty estimation with flexible fine-tuning approaches like BatchEnsemble and LoRA shows strong potential for enhancing LLM reliability and robustness. Future work could build on these findings by integrating more sophisticated uncertainty metrics and establishing standardized benchmarks, paving the way for more effective and generalizable hallucination detection strategies. demic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725.uncertainty. We also experimented with including epistemic uncertainty as a feature, but this reduced the performance, likely due to the correlation between the features. Therefore, we chose to exclude epistemic uncertainty and use the aforementioned features instead.  
 
 Appendix: We explore two widely-used weight initialization strategies for our fast weights: He initialization [43] and Xavier initialization [44] . We hypothesize that the pre-trained Mistral 7B model [26] may have used one of these methods, making them natural choices for our approach. The right side of Figure C .1 shows the generated responses for a sample data point from the SQuAD dataset, where the answer is nonsensical. This outcome is expected because the ensemble members are formed by the multiplicative product of shared and fast weights. Initializing the fast weights close to 0 effectively nullifies the knowledge embedded in the pre-trained weights. To address this, we adopt a simple solution: we maintain the variance of the He and Xavier initializations but set the mean to 1. This adjustment ensures that the ensemble members do not completely overwhelm the pre-trained weights.The left side of Figure C.1 illustrates this modified approach. We observe that using He initialization with a mean of 1 produces diverse yet coherent predictions. The increased variability results from the higher variance of He initialization compared to Xavier initialization. We argue that greater diversity in the ensemble enhances uncertainty estimates. Therefore, we select He initialization with a mean of 1, as shown in option (a) of For the SQuAD and SQuAD v2 datasets we format our prompts to the LLMs using the following template: An example of a hallucinated answer from the LLM. Specifically, note the text highlighted in orange, which states that Park Skaryszewski is located by the right Vistula bank. When the LLM encounters the question, "What park is close to Vistula street?", it incorrectly assumes that Vistula bank and Vistula street refer to the same geographical location. Consequently, it hallucinates by generating the incorrect answer of Park Skaryszewski, even though Vistula street has never been explicitly mentioned in the provided context. Consequentially, when it produces this hallucination, it produces a higher uncertainty measurement than that of the correct answer described in Table D .1.LoRA matrices are rank-r matrices added elementwise to the weight matrix, while the fast weights -rank-1 matricesare multiplied elementwise. 


Decision: The paper aims to detect hallucinations of LLM by estimating the uncertainty of the model.

The reviewers raised several questions which were mostly answered by the authors, most of which were about clarification of the text.

I recommend accepting the paper for a presentation.

Reviews: review: title: title: Interesting work in memory-efficient ensembling, but does it work for other models?
paper_summary: paper_summary: The article proposes a method to detect language model hallucinations, i.e. responses which disregard instructions or include incorrect information, based on uncertainty estimation. The authors base their approach in information theory, arguing that model uncertainty can be decomposed into epistemic uncertainty (tied to the model's knowledge of the data) and aleatoric uncertainty (representing variation in the data), and that these uncertainties are correlated with model hallucinations.

To provide uncertainty estimation for existing pretrained language models, the authors propose augmenting language models with multiple LoRA adapters, which are randomly initialized and then finetuned separately on the target task. The uncertainty is then estimated from the estimated predictive entropy of the adapters. To detect hallucinations, the authors generate a labelled dataset of uncertainty estimates and correctness labels for a set of model prompts and responses.

The authors evaluate their uncertainty estimation and hallucination detection on the Mistral-7B-Instruct-0.2 language model, and compare their approach against a baseline prompt-based method with repeated sampling, and the LoRA Ensemble method by Wang, Aitchison and Rudolph. They find that their method performs worse than LoRA Ensemble in classifying factual hallucinations and out-of-distribution examples, but performs best out of the sampled methods when classifying faithfulness hallucinations, achieving 97.8% accuracy on their test set based on SQuAD 2.0. After finetuning all of the evaluated models on questions from the SQuAD and MMLU datasets, the authors also find that their ensemble achieves better accuracy on questions from the respective test sets than the LoRA Ensemble and the original model finetuned by itself.
main_review: main_review: The authors' revisions include necessary details about the ensemble setup during their experiments, which support their argument that the LoRA adapters are serving as a memory-efficient approximation of a full model ensemble. Additionally, the authors have clarified key points, and thoroughly addressed the reviewers' questions and suggestions. While I would like a larger evaluation of the approach with different ensemble hyperparameters and base language models, the authors acknowledge this as a direction for future work, and their detailed experimental descriptions would also allow other researchers to perform this evaluation.

With these revisions, I am comfortable accepting this paper, and would like to see it presented at the conference.
strength_weakness: strength_weakness: The approach is presented as generally applicable to instruction-tuned language models with decoders - however, the experiments only evaluate the approaches on a single pretrained language model. Evaluations with multiple pretrained language models are necessary to establish how the hallucination detection performs with weaker or stronger pretrained models, and could also provide confidence estimates for the reported results.

While the experimental conditions themselves are well documented, the parameters for the LoRA ensemble itself - such as the number of adapters and their size - are not given in the article. Since these parameters present a tradeoff between inference time, memory use and the results on the downstream tasks, omitting them from the article makes the results challenging to replicate. Additionally, it is not clear which dataset the training set for the hallucination classifiers is derived from.

Finally, I think the paper would be stronger by referencing model editing techniques such as ROME [1], which attempt to change factual and conceptual associations in language models, while retaining their overall question answering capabilities. Ideally, these techniques could generate models with more factual errors but the same instruction following capabilities as the original, allowing experiments to substantiate the authors' hypothesis that faithfulness and factual errors are correlated with the estimated aleatoric and epistemic uncertainty.

Additional questions which did not significantly impact the decision:

* In the "Uncertainty Estimation" subsection, does $\mathcal{D}$ refer to the overall data distribution?

* Since the weight decay of the LoRA Ensemble and noise injection lead to worse predictive results, are there any objectives or mechanisms in the main method which maintain diversity among the ensemble members (avoiding the case where B and A in all LoRA ensemble members go to zero?)

* Since the faithfulness error detection is based on unanswerable questions from SQuAD 2.0, including factual questions with definite answers outside the question context, are there faithfulness hallucinations where the model is factually correct? Do the uncertainty estimates reflect this?


[1] Meng, K., Bau, D., Andonian, A., & Belinkov, Y. (2022). Locating and editing factual associations in GPT. Advances in Neural Information Processing Systems, 35, 17359-17372.
title: title: Review for hallucination detection in LLMs.
paper_summary: paper_summary: This paper focuses on leveraging an ensemble of Large Language Models (LLM) to generate uncertainty estimates which can be used to detect LLM hallucinations. A chief contribution was the utilisation of Low-Rank Adaptation of Large Language Models (LoRA) to train the LLM ensembles in a memory-efficient and flexible manner. The uncertainty estimates computed were used as features in binary classification models to distinguish between correct and hallucinated responses. The hallucinations were categorised into faithfulness and factual hallucinations to detect potentially harmful predictions for LLM applications in high-risk settings.
main_review: main_review: Based on revised edition of submission I am happy to accept this submission.
strength_weakness: strength_weakness: In the caption of the presented Figure 1, LoRA if left unabbreviated and as the figure appears before the Introduction section the unabbreviated form is not known to the user and this could be further clarified. Also, it can be further clarified in the caption that V are the fast weight matrices and the pattern behind the B and A vector boxes makes it difficult to read the text on them. 

In Tables 1 and 2, highlighting the presented method such as with the BatchEnsemble (ours) would improve the clarity. Also specifying how the top-1 accuracies in Table 1 were computed from the five classfiers would be useful.

Lastly, the sentence " All models utilize..." in line 301 within Section 4 could be worded to further emphasise the model used was Mistral-7B-instruct-v.02 with pre-trained weights.