Title: Beyond Token Probes: Hallucination Detection via Activation Tensors with ACT-ViT
Venue: NeurIPS 2025
Accepted: yes
Keywords: Hallucination detection

Probing classifiers

LLM analysis
Abstract: Detecting hallucinations in Large Language Model-generated text is crucial for their safe deployment. While probing classifiers show promise, they operate on isolated layer–token pairs and are LLM-specific, limiting their effectiveness and hindering cross-LLM applications. In this paper, we introduce a novel approach to address these shortcomings. We build on the natural sequential structure of activation data in both axes (layers $\times$ tokens) and advocate treating full activation tensors akin to images. We design ACT-ViT, a Vision Transformer-inspired model that can be effectively and efficiently applied to activation tensors and supports training on data from multiple LLMs simultaneously. Through comprehensive experiments encompassing diverse LLMs and datasets, we demonstrate that ACT-ViT consistently outperforms traditional probing techniques while remaining extremely efficient for deployment. In particular, we show that our architecture benefits substantially from multi-LLM training, achieves strong zero-shot performance on unseen datasets, and can be transferred effectively to new LLMs through fine-tuning.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents an LLM hallucination detection approach.\nThe motivation is that previous probing layer methods are largely limited by their generalization capability to new tasks, new datasets, and new models.\nThe key idea of the proposed method is to leverage the activation of LLMs.\nAfter the initial extraction of several LLMs with respect to multiple queries, the authors then utilize a ViT model to capture some spatial relationship between responses and LLM layers. \nAcross 15 LLM-dataset pairs, ACT-ViT consistently outperforms prior white-box models and supports zero-shot generalization to new datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper presents an interesting ensemble method to detect LLM hallucination, which may be useful for future research.\n- The proposed method can outperform the previous probing layer baselines by a large margin.\n- Across 15 LLM-dataset pairs, ACT-ViT consistently outperforms prior white-box models and supports zero-shot generalization to new datasets. \n\nWeaknesses:\n- I don't quite grasp the usefulness of this task. To me, the hallucinations for different LLMs are quite subtle and fine-grained, and may range from token-level to sentence-level. And the hallucination even is different for the same two input queries repeated. In other words, hallucination is dynamic, while detection is static. \n- How did the authors get the labels for each response? Is it based on each token?\n- It seems the pooling layers are carefully selected. This will limit the generalization of the proposed method.\n- Are all the involved parameters trainable? If so, why use a ViT model?\n- Following the previous concern, I don't see the necessity of using a ViT model for the ensemble processing. \n\nMinor:\nLine 111, their internal\n\nQUESTIONS:\nI will update my score if the authors can well address my concerns after rebuttal.\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-17"}

comments: SUMMARY:
This paper introduces a novel vision transformer-inspired model for probing LLM activations for the task of hallucination detection. They process all the activations across layers and token positions as input, showing this enables cross dataset and LLM (with LLM-specific adapters) generalization, and strong performance at hallucination detection.

STRENGTHS AND WEAKNESSES:
Strengths:

- Novel and well motivated technical contribution.
- Fast and general purpose framework which can be used in practice with minimal overhead after pretraining.
- Strong empirical results showing generalisation and improvement over other probing and logit based methods

Weaknesses:

- No comparison to other hallucination detection approaches: predictive entropy, semantic entropy, p(true)

QUESTIONS:
- This is framed as hallucination detection but it seems like the task is accuracy prediction. Could you clarify your definitions?
- Have you investigated other probing tasks?
- Have you looked into interpreting the ACT-ViT itself? What layers/tokens are relevant and whether they agree with the single token/layer results?
- Did you compare to other hallucination detection approaches: predictive entropy, semantic entropy, p(true)?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-27

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses a research question:\n- How to solve the dynamic existence of hallucination information depending on the location of tokens and layers?\n\nPrior work of probing classifiers has shown that the performance of hallucination detection depends on the selected location of tokens and layers. First, the paper shows that each LLM has a different location of tokens and layers for the best performance (See Fig. 2). This finding implies that we need to specific classifier for the given LLM, which is not generalizable to the different LLM.\n\nBased on the findings, the paper defines Activation Tensor (ACT) which has three dimensions: the number of layers, the number of tokens, and the dimension of the hidden state. ACT contains rich information on hallucination within a model and is used for detecting hallucinations. The paper proposes ACT-VIT architecture which takes ACT as input and hallucination classification as output. To leverage data of diverse LLMs, the paper introduces the adapter and pooling to align the spatial and feature dimensions across LLMs. \n\nExperiments are composed of In-domain (ID) and out-of-domain (OOD) setups. In ID, the performance of ACT is larger than the probing methods. Note that probing methods are strong baselines because the classifiers are trained in a supervised way. In OOD, ACT-VIT shows the dataset and LLM generalization; for the LLM generalization, the paper fixes VIT and trains the adaptor. The paper validates the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n- The paper provides the analysis that the location of hallucination information is different depending on LLMs. These findings are connected to the generalization and motivates the design of ACT and ACT-VIT architecture for dynamic existence of hallucination and generalization.\n- ACT is large; thus, the paper considers the efficient design to use it. \n- Although probing classifiers show promising performance in hallucination, the performance of OOD is low. The paper validates that the proposed method is effective in OOD setup.\n\n### Weaknesses\n- Is the sequence length N fixed? Is it zero-padding to match the max sequence length?\n- What is the dataset size for pre-training?\n- Is the adaptor reusable if the hidden dimensions are aligned?\n\nQUESTIONS:\nSee above Weaknesses.\n\nLIMITATIONS:\nYes, the paper includes Limitations in Sec. 6 and  Broader Impact in Appendix D,", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes ACT-ViT, a novel architecture for hallucination detection in Large Language Models (LLMs) that leverages full activation tensors rather than individual token-layer pairs. Traditional probing classifiers are limited by their static, LLM-specific nature and fail to generalize across models. ACT-ViT treats activation tensors as image-like structures and applies a Vision Transformer (ViT)-inspired backbone, enabling joint training across multiple LLMs. The model uses linear adapters to align internal representations from different LLMs and supports efficient zero-shot generalization and fast fine-tuning for unseen models. Extensive experiments across 15 LLM-dataset combinations demonstrate ACT-ViT’s superior performance, scalability, and transferability compared to standard probes and logit-based baselines.\n\nSTRENGTHS AND WEAKNESSES:\nThe strengths of this manuscript are summarized as follows:\n1.\tThe paper addresses an important and timely problem—hallucination detection in LLMs. This is a critical research area with growing relevance, given the widespread deployment of LLMs in high-stakes applications.\n2.\tThe paper provides a clear and accurate analysis of the limitations of existing methods and articulates guiding principles for improvement.\n3.\tThe proposed method is grounded in novel empirical observations revealed through insightful preliminary experiments, which effectively motivate the design of the approach. The method itself is thoughtfully constructed, leveraging structured activation tensors and vision-inspired architectures to address the identified challenges.\n4.\tThe paper is clearly written and well-structured, making it easy to follow.\nThe weaknesses of this manuscript are summarized as follows:\n1.\tThe paper focuses on a specific sub-direction of hallucination detection—probing classifiers—but does not sufficiently justify the significance of this direction. Although the authors cite several related works, many of them are only loosely connected to probing-based hallucination detection, which weakens the motivation and perceived importance of the chosen research scope.\n2.\tThe proposed method, while effective, appears somewhat straightforward and lacks deeper conceptual innovation.\n3.\tThe experimental section is somewhat limited and lacks strong ablation or comparative studies.\n\nQUESTIONS:\n1.\tPlease explain the importance and state-of-the-art relevance of probing classifiers in LLM Hallucination Detection (HD). Could you cite some directly related literature? In the related work section, the discussion does not seem to be directly relevant to HD (for example, citation [6]). It appears that probing classifiers are neither a cutting-edge nor a mainstream approach in HD.\n2.\tThe effectiveness of your method may be significantly affected by longer input and output text lengths. Please discuss and experimentally validate the impact of long input and output length on the performance of your approach.\n3.\tPlease provide a deeper analysis and explanation of the phenomena observed in Figure 2. Additionally, how does the proposed method adaptively focus on important activations? It appears that the approach simply relies on brute-force training rather than a truly adaptive mechanism.\n4.\tFor the linear adapter (LA), your method trains a separate adapter for each LLM. If your LA can generalize to different LLMs, then traditional classifiers should also be able to generalize across models. For a new model, how do you select the most suitable pre-trained LA from the existing ones?\n5.\tIn the experimental section, I believe the authors should include the following ablation studies for comparison: (1) Token[*]: Similar to Probe[*], but selects the optimal layer-token combination on the test set. This represents the oracle solution and should yield the best performance, serving as the upper bound for your method, even though it is not feasible in practice. (2) ACT-VIT without complete activations: Select the optimal layer-token combination on the validation set, then replicate this token to form the Activation Tensor (A). The rest of the process (model and training process) remains the same as ACT-VIT. (3) ACT-VIT without complete layers: Select the optimal layer on the validation set, then replicate all tokens from this layer to form the Activation Tensor (A). The rest of the process remains the same as ACT-VIT. (4) ACT-VIT with only the last layer: Select all tokens from the last layer, replicate these tokens L times to form the Activation Tensor (A). The rest of the process remains the same as ACT-VIT. These ablation studies can demonstrate that your motivation (adaptive selection) is responsible for the improvements, rather than additional parameters or training. Additionally, please explain why the Logits and Token methods are missing in the Off-domain Hallucination Detection experiments.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}