Eyes Show the Way: Modelling Gaze Behaviour for Hallucination Detection
Authors: Kishan Maharaj
Ashita Saxena
Raja Kumar
Abhijit Mishra
Pushpak Bhattacharyya
Venue: EMNLP 2023 Findings (2023)
Keywords: Cognition
Hallucination
NLG
Gaze
Human Attention

Abstract: Detecting hallucinations in natural language processing (NLP) is a critical undertaking that demands a deep understanding of both the semantic and pragmatic aspects of languages. Cognitive approaches that leverage users’ behavioural signals, such as gaze, have demonstrated effectiveness in addressing NLP tasks with similar linguistic complexities. However, their potential in the context of hallucination detection remains largely unexplored. In this paper, we propose a novel cognitive approach for hallucination detection that leverages gaze signals from humans. We first collect and introduce an eye tracking corpus (IITB-HGC: IITB-Hallucination Gaze corpus) consisting of 500 instances, annotated by five annotators for hallucination detection. Our analysis reveals that humans selectively attend to relevant parts of the text based on distributional similarity, similar to the attention bias phenomenon in psychology. We identify two attention strategies employed by humans: global attention, which focuses on the most informative sentence, and local attention, which focuses on important words within a sentence. Leveraging these insights, we propose a novel cognitive framework for hallucination detection that incorporates these attention biases. Experimental evaluations on the FactCC dataset demonstrate the efficacy of our approach, obtaining a balanced accuracy of 87.1%. Our study highlights the potential of gaze-based approaches in addressing the task of hallucination detection and sheds light on the cognitive processes employed by humans in identifying inconsistencies.

Eyes Show the Way: Modelling Gaze Behaviour for Hallucination Detection
Kishan Maharaj* Ashita Saxena* 1 Raja Kumari,
Abhijit Mishra Pushpak Bhattacharyya'
Indian Institute of Technology Bombay, Mumbai, India University of Texas at Austin, Texas, United States
(kishan. maharaj.aaaajj.aaaaaa iitb, as. saxena. as, kumar. iithia..iibe/maaaa com2, pb@cse. iitb.iitbc... ac. in!, abiijitmissrra@ttexss.cem
Abstract
their alignment with the surrounding context and external knowledge. Addressing hallucinations has become paramount, particularly in the context automatically generated text utilizing powerful lan- guage models (LLMs), which often exhibit human- like fluency but are prone to hallucinatory outputs (Zhang et al., 2023; Alkaissi and McFarlane, 2023).
Detecting hallucinations in natural language processing (NLP) is a critical undertaking that demands deep understanding both se- mantic and pragmatic aspects of languages. Cognitive approaches that leverage users' be- havioural signals, such as gaze, have demon- strated effectiveness in addressing NLP tasks with similar linguistic complexities. However, their potential in the context of ballucination detection remains largely unexplored. In this paper, we propose a novel cognitive approach for hallucination detection that leverages gaze signals from humans. We first collect and intro- duce an eye tracking corpus (ITTB-IGG: IITB- Hallacination Gaze corpus) consisting of 500 instances, annotated by five annotators for hal- lucination detection. Our analysis reveals that humans selectively attend to relevant parts of the text based on distributional similarity, sim- ilar the attention bias phenomenon in psy- chology. We identify two attention strategies employed by humans: global attention, which focuses the most informative sentence, and local attention, which focuses on important words within a sentence. Leveraging these in- sights, propose novel cognitive framework for hallucination detection that incorporates these attention biases. Experimental evalua- tions on the FactCC dataset demonstrate the efficacy of our approach, obtaining a balanced accuracy of 87.1%. Our study highlights the po- tential of gaze-based approaches in addressing the task of hallucination detection and sheds light the cognitive processes employed by humans identifying inconsistencies.
Many existing methods for hallucination detec- tion depend on knowledge sources that are explicit such as Wikipedia or knowledge graphs (Manakul et al., 2023; Santhanam et al., 2021; Dziri et al., 2021; Ji et al., 2023) or ingrained in language en- coders such as BERT or RoBERTa (Shen et al., 2023; Zhou et al., 2020). While these traditional approaches reasonably detect hallucinations in text when supplemented with knowledge sources, they face sustainability challenges due the con- stant need for up-to-date knowledge. Obtaining the latest information for hallucination detectors is often impractical, as requires readily available and currett sources of knowledge. To address this issue, we propose alternative approach that lever- ages cognitive and behavioural information from humans in the form of gaze patterns while they analyze text for potential hallucinations.
Our work is motivated by the notion that humans, while reading text for hallucination identification would naturally employ their cognitive faculties to navigate the intricate relationship between lan- guage and real-works knowledge. Linguistically, this involves scrutinizing whether (a) entities in the text adequately placed (e.g., is Canada a right choice of entity) (b) the semantic roles played by the entities are valid w.r.t the context (c.g, Pluto is a planet). This critical examination would often manifest as prolonged fixations' on specific sec- tions of text (e.g., longer fixations on entities and phrases such as Canada, Pluto and planet) that require closer evaluation, resulting in denser and
1 Introduction
Hallucination detection text refers to the task of identifying and validating information that is inaccurately or falsely represented within textual content. Detection of hallucination involves ex- amining the claims made in the text and assessing
'A fixation occurs when the eye is facused on particular
"These authors contributed equally to this work
part of the screen

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more extensive fixation activities, In essence, fixa- tions may serve as invaluable indicators, acting as a reliable surrogate for knowledge-based validation of contextual information pertaining to potential hallucinations. This may open up possibilities for the development of a hallucination detector that can leverage gaze data as primary input, alleviating the reliance on supplementary external knowledge.
obtained an IAA Kappa score of 0.60, which indicates substantial agreement.
We introduce novel concept of attention bias derived by analyzing human annotators' gaze patterns while they carry out the task of hallu- cination detection.
We propose and evaluate a cognitively in- spired BERT-based deep learning framework (Figure 2) for hallucination detection driven by different forms of attention biases seen in human reading. The framework offers su- perior performance (Table 2), and better in- terpretability (Figure 4) against baseline ap- proaches and shows competitive performance with SOTA by attaining the balanced accuracy of 87.1 % (Table 2).
To validate this, we first collect first-of-itskkind eye-tracking data of 5 annotators annotating 500 instances of claim-context pairs, carefully derived from the FactCC dataset (Kryscinski et al., 2020). During the annotation process, we capture the fix- ation patterns of annotators on both the claim and context texts, along with their corresponding la- bels. Notably, our inter-annotator agreement (IAA) Kappa score reaches 0.60, indicating substantial agreement. Behavioural analysis of the annotated data reveals a recurrent pattern where annotators tend to skim through somewhat irrelevant context while selectively focusing on information crucial for establishing or refuting hallucinations. Building upon insights gained from this behavioural analy- sis, we term this selective reading phenomenon as "attention bias". Furthermore, our observations indicate that attention bias can manifest either "global" approach, involving the extraction of sentences containing relevant information about hallucinations, or a "local" approach, focusing on specific phrases within sentences to evaluate the alignment of semantic roles between the claim and the selected phrases.
Related Work
2.1 Hallucination / Inconsistency Detection
Prior work in understanding hallucination includes a survey by Ji et al. (2022) which discusses hallu- cination arising from data, training approach, and inference. Hallucination detection in various NLG tasks has been tackled previously using statisti- cal methods (Wang et al. (2020b), Shuster et al. (2021)) and model-based methods. Model based methods include QA-based approaches (Scialom et al. (2021), Wang al. (2020a), Honovich et al. (2021)) and NLII-ased approaches (Kryscinski et al. (2020), Mishra et al. (2021), Laban et al. (2022a)). Recently, prompt based methods (Arora et al. (2022), Manakul et al. (2023), Agrawal et al. (2023), Dhuliawala et al. (2023)) are being used to detect hallucinations in the text produced by LLMs. Usage of gaze signals in hallucination detection remains an unexplored field, although there is prior work which uses gaze in other NLP tasks (as men- tioned in Section 2.2).
Building upon these insights, we propose mod- ular architecture that incorporates global and lo- cal attention bias using transformer-based deep learning techniques (Vaswani al., 2017) and gaze-based attention saliency module (Sood et al., 2020b). Experimental evaluations on the FactCC dataset demonstrate the efficacy of our approach, outperforming baseline models while attaining bet- ter interpretability. We have sourced our code, data and results for academic usage? ?
Kryscinski et al. (2020) proposed a weakly- supervised model for verifying factual consistency and released dataset FactCC which contains claim-context pairs. We use this dataset to con- duct our experiments. To the best of our knowledge, the usage of gaze signals has not been explored in the past for the task of hallucination detection.
Our contributions are summarised as follows:
We create and share a firstoffiitskkndd eye- tracking corpus (Section 3) for hallucination detection: IITB-HG HTTB-Hallluiiatioo Gaze Corpus) consisting of 500 instances of context and claim pairs, where five annota- tors label the claim as hallucinated or non- hallucinated with respect to the context. We
2.2 Gaze in deep maaning-bassed architectures In recent years, various attempts have been made to investigate the correlation of human attention with the machine attention of pre-trained large
"Github Link

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language model (Eberle et al. (2022), Sood al. (2020a), Bensemann al. (2022)). Eberle al. (2022) highlighted the inability cognitive models to account for the higher level cognitive activities like semantic role matching, hence motivating the use of large language models (LLMs) for modelling the human gaze. Hollenstein et al. (2021) showed the efficacy of LLMs in predicting the gaze features for multiple languages, including English, Russian, Dutch and German. Barrett et al. (2018) used nat- ural reading eye-tracking corpus for regularizing attention function multi-task setting.
text length ranges from minimum of 56 words 132 words, with 92 words per context on average.
3.1 Experimental Settings
Participants were provided with detailed task in- structions, including two example claim-context pairs and their expected annotations, displayed on the computer screen for guidance. The annotation guidelines are described in Appendix B.2. Partici- pants completed the tasks individually in moni- tored room, assisted by a research assistant. More details about the experiments and other environ- mental settings for the eye-tracking data collection can be found in Appendix B.1.
The work most similar to us is Sood et al. (2020b), which investigates the integration of the gaze-based text saliency model with vanilla trans- formers (Vaswani al., 2017) for directly incor- porating gaze predictions into the attention mecha- nisms for paraphrase detection and sentence com- pression tasks. However, their proposed architec- ture heavily relies on BiLSTM and vanilla trans- formers, lacking pre-training benefits. In contrast, our approach utilizes pre-trained language models and does not impose constraints the attention functions of these language models. Instead, focus on learning the space for contextual embed- ding transformation based on saliency modelling grounded by human gaze data.
3.2
SitecipanttAnnottttee details
The annotation process involved five participants aged 21-25 (two male, three female) with English as primary language of academic instruction. Among them, one participant had a Bachelor's de- gree, one held a Doctorate, and three had Master's degrees from universities with English as the pri- mary language of instruction. All the participants had valid TOEFL scores with minimum of 100 and an average of 107.2 out of 120, demonstrating acceptable English proficiency.
Claimy remains are found "hhrrday reghtarhood Asuricion, Paraguay.
3 Collection of IIB--GCC (ull--aalucination Gaze Corpus)
Context: (CNN) - Officials have uncovered common grave in Pereguay that contained tortured and lolled under former stronimmnn Alfredo Stroessner, authorities said, Human cémains discovered Thursday Asuincon, Paraguay, neighborhood. Speeking the grave site Thursday, Fernnnn Lugo caled the remnants of "painful period' Paraguay's history. Stroesoner power from 1954-89 and was known es brutal dictator whese regime tortured and kiled hundreds govermment opponents. bodies found Tacumbu, a neighbaroodd in Paraguay's captaal city, Asuncion.
We collect eye-tracking data from annotators while they perform the task of hallucination detection. For the data collection process, use 500 claim- context pairs from the FactCC dataset (Kryscinski al., 2020) as stimuli. The annotators are given a claim-context pair and are asked assess the faithfulness of the claim relation to the context. To the best of our knowledge, no eye-tracking corpus is available for hallucination detection.
bodies victims believed
Hcatmap of human gaze fixation for a claim- context instance while performing the task of halluci- nation detection (red signifies higher values). Fixations are seen only on the most similar sentence. Relatively higher fixation is seen on the words responsible for hal- lucination (i.e., "remains are discovered").
Figure 1:
300 pairs are chosen from the training set, 100 from the validation set, and 100 from the test set of the FactCC dataset and are used similar splits during the training of the local attention bias model to avoid data leaks. The acquisition of the IITB- Hallucination Gaze Corpus is then carried out in three phases, in which we presented annotators with 180, 160, and 160 instances respectively.
3.3 Behavioural Analysis
Participants exhibit a hierarchical approach when scarching for and matching the claim within the context. They begin by thoroughly reading and encoding the claim into memory, either in its en- tirety or by noting key elements like numbers and proper nouns. Subsequently, they sean the con- text sentences from the first to the last, searching
The average number of words in the claim- context pair is 105, the shortest claim having words and the most extended claim having 32. On average, each claim consists of 13 words. The con-

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for sentence corresponding their remembered claim or key elements. When a potential match is found, they focus on that specific sentence, engag- ing in a more detailed reading to verify its coher- ence with the claim. Notably, there is a strong bias towards sentences distributionally similar to the claim within the context. This bias can be linked to the initial lexical processing stage of the reading theory (Just and Carpenter, 1980). It is notewor- thy to mention that semantic similarity's 's influence
pantence-transfoneee models?. Specifically, we se- lect the following models for computing similarity scores: all all-robertu-large-v (Liu et al., 2019), all- mpnet-base-yy (Song et al., 2020), gtr-t5-large (Ni et al., 2021), all-mpner-base--22 (Song et al., 2020), and LaBSE (Feng et al., 2020). These models are chosen for their ability to generate high-quality sentence embeddings while maintaining moderate model sizes. Appendix (Section A.1) gives details on their performances and model sizes.
is less pronounced than distributional similarity. Given the nature of the task, where there is a possi- bility of semantic contradiction between the claim and the relevant sentence in the context, human annotators learn to rely on distributional similarity guiding factor. This behaviour of humans can be seen via heat maps of the fixation of the human gaze on context words. One such example is shown in Figure 1. It can be seen that the relevant sentence is fixated on the most number of times whereas the other sentences have zero/very-lov fixation.
To identify the optimal mechanism for utilizing similarity scores from various models, we employ the following methodology. Using HITB-HGC (de- tailed in Section 3), we leverage fixation data col- lected during the claim labelling process by humans to extract the sentence with the longest fixation du- ration. This sentence, serving as the ground truth for our global attention model, guides our objective of determining the optimal combination of simi- larity scores from five different models based on the most fixated sentence by humans. Upon ana- lyzing the results, we find that voting mechanism yields the highest accuracy for these 500 instances (Table 5). This voting mechanism involves select- ing the sentence that receives the highest similarity scores from the majority of the models. Based on a manual analysis, we find that the voting mecha- nism consistently captures the correct relevant sen- lence, surpassing other alternatives. As a result, we adopt the voting-based ensembling of sentence en- coders our global attention bias model. Interest- ingly, we also observe that using the sentence with the maximum score from only the LaBSE model achieves the second- best accuracy. Thus, LaBSE model can also be used as an alternative cost ef- fective approach. summary, given a claim and context, our pipeline first employs the ensemble global attention model to identify the most relevant sentence. This sentence is then utilized input for the subsequent steps in end-to-end hallu- cination detection model (Figure 2). Notably, the global attention bias model also helps in reducing the number of tokens given as input to the subse- quent model. Figure 6 in Appendix Section A.2 shows a visual representation of how the global attention bias model is implemented.
4 Methodology
Building upon our theory of attention bias, our proposed cognitive framework addresses the hallu- cination task by integrating this behaviour. Figure 2 provides an overview of the framework, featuring the modelling of global and local attention bias.
4.1 Attention Bias
Attention bias is goal-driven cognitive mecha- nism in which humans while making decisions se- lectively focus on relevant information while dis- regarding irrelevant or less salient stimuli (Fadardi et al., 2016). They search for distributionally simi- lar sentences and then verify the semantics at the token level. Figure/ 1 illustrates this behaviour. Upon deeper examination of gaze behaviour in HGC, two distinct types of attention bias were observed global and local attention bias, which we describe in the following subsections.
4.1.1 Modeling Global Attention Bias
Global attention bias represents the human incli- nation to prioritize the most informative sentence in given context. By simulating human atten- tion bias, our approach effectively identifies the most relevant context sentence for given claim. To emulate this behaviour, we propose an ensem- ble approach that utilizes multiple cutting-edge
4.1.2 Modeling Local Attention Bias
Local attention bias refers to the annotator's in- clination to prioritize salient words essential for
Documentationn for sentonce-transffommee models

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Hallucheelee/Won-HHllllnnaasss
Qutput
Sigmaid
Reading Comprehenslon gaze dalasets (PROVO and GECO)
IITB-HG (IITB- Hallucination Gaze corpus)
f
Feed Forward Neural Network layers)
Eye-Tree Tracking
Datasets
Fixation scores per loken f2. fn
Contextual embedding per
Gaze Signals (Token wise fixation duration)
e1.c2...cn
Local Attention Blas Model (LAB)
Sigmoid
Linear Layer
Global Altention Bias Model (GAB)
Context sentence, s). most relevant claim
ReLU
(selects a context sentence relevant to clam by an ensembeling approach)
Linear Layer
Sentence Pair Classifier
BERT Layer
BERT Layer
BERT Takenizer
BERT Tokenizer
Context Sentences $1,52 $1,52u...9p
Claim sentence tokens <SEP> tokens of context sentence most similar to the claim Cr1.Cr2.....Ip <SEP> Cr3.Cr2....Ctg
Claim Sentence
Input
Figure 2: Overview of the end-to-end model incorporating global attention and local attention bias for detecting hallucination given claim and context. Figure 6 shows the details of the global attention bias model (1).
hallucination detection, while disregarding irrele- vant words. Modeling local attention bias provides a means to influence the self-attention components (Vaswani et al., 2017) of transformer-basee deep encoders (Devlin et al., 2018 8) employed in down- stream tasks such as hallucination classification. This guided attention approach enhances token- level model interpretability (see Section 6).
The output vector of the LAB model, represent- ing token level importance scores, defined as:
Flab {S1, J2, fa. Spt.94+)
(3)
The first phase of the training involves learning the normalized fixation duration at the token level from the gaze data. These learned parameters are checkpointed and used to instantiate the local at- tention bias model in the next phase of training. The next phase of training involves the joint learn- ing of the hallucination detection model (sentence pair classifier) and local attention bias model. This phase begins with the initialization the local at- tention bias model with the parameters learned the previous phase and proceeds with the training by hallucination detection data on single objec- tive function optimized with binary cross entropy loss the final output. The output of the final model can be defined mathematically as
We model the local attention bias by learning the token level saliency score using eye-tracking data in a separate module called Local Attention Bias. The LAB module is jointly trained alongside the sentence pair classifier to adapt to the -asc-fpec parameters. The overall flow of information is depieted in Figure 2 in blocks labelled (2), (3) and (4) and the training process is described below.
Let the Cj and C, the vectors representing the claim and context pair respectively:
C l.... C. (11, 12, la (..dg))
Y Sigmoid((W (Fiab Eembea) + bb (4)
The input vector is the concatenation claim and context separated by special [SEP] token.
Here W and b are the parameters of the feed- forward network, E represents the contextual embedding and represents the final output. By
I: [C[SEP]]CC
(2)

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incorporating both global and local attention bi- ases, we aim to simulate the cognitive processes observed in humans during hallucination detection.
attention models the PROVO Corpus "LLuke and Christianson, 2018) and the GECO corpus' (Cop et al., 2017). These datasets are collected for the task of reading comprehension and encompass gaze patterns during general-purpose reading, which we believe would be beneficial in effectively initializ- ing our local attention model. In Appendix E, we show a word-Level comparison with other publicly available gaze datasets.
5
Experiments
5.1 Dataset Details
We use the FactCC dataset re- leased by Kryscinski et al. (2020) which consists of IM instances. The data distribution is mentioned in Table 1. Kryscinski et al. (2020) prepare this dataset by introducing perturbations (c.g., entity swapping, pronoun swapping, negation, back trans- lation etc.) a sentence taken from the context. We specifically selected the FactCC dataset for our study for several compelling reasons. Firstly, the dataset exhibits wide range of topics, providing diverse contexts to analyze. This diversity ensures that our experimental design captures broad spec- trum of claim-context relationships. Secondly, the FactCC dataset includes well-defined perturbations, which can be leveraged precisely examine the process of verifying claims based on the given con- text which facilitated a clear and controlled experi- mental setup for our eye-tracking experiment.
FactCC Dataset:
5.2 Experimental Details
In our experimental setup, we conducted training using single NVIDIA A A100-SXM4-80GGB GPU with batch size of 128, and a learning rate of 2e-5 was used during the training process. To capture the majority of the FactCC data, we set the sequence length to 320, covering approximately 95% of the dataset. For training, we used the Binary Cross Entropy Loss as the loss function and utilized the Adam optimizer with a weight decay of 1e-2.
At run oun-time, the detection of hallucination hap- pens as follows. The most relevant context sen- tence is first selected using global attention bias, aiding claim verification. The output of the global attention bias model, combined with the claim, is passed to the local attention bias model trained on cyc-tracking data. The local attention bias model assigns fixation scores to each token, indicating their saliency for hallucination detection. Simul- taneously, the sentence pair classifier model gen- erates contextual embeddings for each token. The embeddings are then weighted using the fixation scores and passed through the feed-forward and sig- moid layers to generate output class labels. Train- ing of the model employs a similar forward flow of information as above, and the updation of weights happens through backpropagation, details of which are skipped for brevity.
Split
H
NH
Total
Train
499,623
503,732
1,003,355
Test
62
441
503
Validation
132
799
931
Total
499,817
504,972
1,004,789
Table 1: Distribution of different types of instances (hal- lucinated (H) and non-hallucinated (NH)) in different splits of FactCC dataset (train, test and validation).
eye-Tracking Corpus: This is the curated eye- tracking corpus Halluccaluiinnioonn Gaze Corpus) we explained in Section 3. The corpus is divided into the train, validation, and test sets, with 60%, 20%, and 20% split, respectively and used as the source of supervision for the local and global at- tention models. It should be noted that the purpose of gaze data was to do a behavioural analysis of humans during hallucination detection. This makes 500 instances enough to gather and validate the insights from this study, which were used to design the proposed cognitive framework and ground the global attention bias model. However, for finetun- ing the local attention bias model, we leverage two other published datasets for pre-training our local
Results
Table 2 displays our experimental results in differ- ent experimental settings as described below:
BERT+GAB: BERT architecture integrated with Global Attention Bias model.
BERT+GAB+LAB. BERT architecture inte- grated with Global Attention Bias and Local Atten- tion Bias models.
BERT+GAB+LAB-GAAE BERT architecture integrated with Global Attention Bias and Local
httpp:://sffio////
//tpps://expsy. .ggett. be/ htd/d//wooadd//eo/

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Model
Balanced Accuracy
pretability in Section 7.2. We perform error analysis (Section D) understand the scenarios in which our model gives incorrect responses. Our comparative study with the SummaC-Com model (Section 7.4) shows that our model performs better in 92% of the cases in which SummaC-conv makes erroneous predictions.
BERT MNLI
0.5151
BERT FEVER
0.5207
NER Overlap
0.55
MNLI-doc
0.613
FactCC-CLS
0.759
DAE
0.759
FEQA
0.536
QuestEval
0.666
ChatGPT-ZS
0.747
ChatGPT-COT
0.795
500
SummaCZS
0.838
SummaC-Conv
0.895
Irquency
BERT+GAB"
0.69455
300
BERT+GAB+LAB'
0.86371
BERT+GAB+LAB 0.87103
+Gaze'
100
Table 2: Comparison of our work (marked with *) with baselines. Here, GAB LAB refer to Global Attention Bias and Local Attention Bias respectively. BERT-GAB+LAB-GaaE refers to the model with global and local attention bias trained on complete gaze data (PROVO+GCCO+ITB-HCC))
context fixated upon by
This figure shows histogram depicting the percentage of context words that are being fixated upon by annotators over the HITB-HGC It can be seen that only 10-30% context words are heing fixated the most number of times.
Figure 3:
Attention Bias finetuned on complete gaze data. We observe improvements by incorporating global attention bias over the baselines, which high- lights the importance of noise filtering in founda- tional large language models. Furthermore, observed significant enhancements over the previ- ous setting after incorporating the local attention bias model. Notably, the local attention bias model demonstrated further improvements upon the in- troduction of gaze data. However, both the Mc- Nemar and t-tests report statistically significant difference between the two model outputs the p- values never go below 0.05. Table 2 compares our model with all the baselines described in the Ap- pendix We observe that the proposed approach shows significant improvements over the baselines (including ChatGPT-ZS and ChatGPT-COT) and competitive results with the statc-of-the-ar (Laban et al., 2022b).
Error Cate- gory
No. of Instances (%)
Instances where our model is cor- rect (%)
Exact claim
(45.9%)
34/34 (100%) 18/19 (94.73%)
Paraphrased claim Ambiguous claim
(25.7%)
(8.1%)
(100%)
Coreference claim Incorrect true label
8 (9.5%)
8/8 (100%)
7 (10.88%)
2/7 (37.5%)
Table 3:
The distribution of cach type of SummaC- Conv error category. The last column shows the number of instances which model gives correct prediction for each error category.
7.1 Fixation Analysis
7 Analysis
Figure 3 shows the percentage of words being fix- ated upon by annotators in the IITB-HGC. For each instance, we compute the percentage of words that are being fixated upon. It observed that the major- ity of words in each instance are not fixated upon at all. Figure 3 shows that only 10-30% of the words
We perform analysis the fixation data from IITB- HGC and present in Section 7.1. We also show that including complete gaze data increases inter-
"https://openal http://pnnnn..ommmoo/////caa/aga

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[CLS]ishe one was remembered memorial service by these words big heart service by
big smiles, big service [SEP) for collier, who remembered these words: heart smiles, service.. [SEP]
(a) Heatmap for BERT global attention bias + local attention bias model finetuned on gaze data. The predicted label is "Hallucinated"
[CLS] she was the one who was remembered at memorial service by these words big heart, big smiles, big service [SEP] sean collier, who was remembered memorial service by these words heart, smiles big service. [SEP]
(b) Heatmap for BERT global attention bias + local attention bias model without gaze data fine-tuning. The predicted label is 'Hallucinated'
[CLS] she was the one who was remembered at his memorial service by these words : big heart big smiles, big service. [SEP] andifor sean collier, who was remembered at his memorial service by these words: big heart, big smiles, big service [SEP]
(c) Heatmap for BERT- global attention bias based on attentions. The predicted label is 'Not Hallucinated
Figure 4: The figure displays heatmaps depicting the local attention bias scores for all three settings for an example marked as "hallucinated". Darker colours indicate higher scores, while lighter colours indicate lower scores.
in the entire LITB-HGe are fixated the most num- ber of times. We also compute the centence-wise fixation for every instance. We observe that on average, for every instance, 78% of the total fixa- tion is on only 1-2 sentences of the context. Other sentences have very less fixation.
Claim: "The movie is first in the rampaging- dino franchise since "Jurassic Park iüi Pratt's sci- entist Chris Pratt is the movie's 's new trailer."
Context: "The movie is first in the rampaging- dino franchise since "Jurassic Park III" in 2001."
The true label of the current example is "Hallu- cinated" while the prediction by our model is "Not Hallucinated". We can see that there is a significant semantic overlap between the claim and context which could have resulted in the prediction of "Not Hallucinated". It is interesting to note that similar errors (refer Appendix D) were also made by humans during the process of annotation.
7.2 Interpretability Analysis
Let us consider the following example:
Claim: "she was the who remembered at his memorial service by these words: big heart, big smiles, big service"
Context: "and for Sean Collier, who was remem- bered at his memorial service by these words: bigg heart, big smiles, big service"
7.4 Comparison with SummaC-Com
The above claim is hallucinated with respect to the context due the entity replacement of "Sean Collier" with the pronoun "she". Figure 4 displays heatmaps depieting the importance of each words. We observe that the LAB model correctly high- lights the pronoun "she" in both the scenarios of with gaze and without gaze. In comparison, after removing the LAB bias model the word "and" is incorrectly highlighted and the word "she" is not highlighted at all. Moreover, the signal strength important words ("she" in this example) weakens after removing the gaze and completely goes away after removing the LAB module.
To analyze the performance of SummaC-Conv, we reproduced the model and ran it on the test set of FacICC (by following their paper Laban et al. (2022b)). We perform an error analysis to under- stand where the SummaC-Cone model makes er- We find that out of 504 instances in the test set of FactCC dataset, SummaC- model makes er- roneous predictions in 75 instances. We categorize these errors into categories: Exact claim, Para- phrased clain, Ambiguous claim, Coreference claim and Incorrect true label. We describe these categories with examples for each error category in Figure 5. Table shows the distribution of these categories. The last column of Table 3 shows in the number of error instances in each category in which our model performs correctly. can be seen that our model shows superior performance in all cate- gories. The majority of the error cases (~71.6%% in SummaC- C-Cony belong to the category of "Ex-
7.3 Error Analysis
Significant errors in predictions observed due the high semantic overlap between the claim and context which were labelled as hallucinated.
For example:

---

Eiror Category
Example
SummaC
Oar
True
Conv
Model
Label
Exact claim: when claim
[CLS] hillary clinton arnounced she running for president. [SEP]
NH
present in the context
cnn larry upright just day after hillary clinton anncunced
she was running for president. (SEP)
Paraphrased clatm:
[CLS] Bozenslof of sttackers took control of government buildings,
NH
NH
when paraphrased
officials say. (SEP) dozens attackers took control govermment
version of claim present in the context
buildings. including city
prison central bank and radio
station during asssult early thursday.. according officials [SEP)
Ambignous claim: when
[CLS] mark the annive sary of tragic event, the lingering memores of the 2013 marathon blasts will be seen and feltin allisorts of ways.
NH
NH
claim contains
semautically unbiguous information
[SEP] as the boston marathon runners begin and hopefully finish
their exhausting run monday th lingering memories of the 2014
marathon blasts will be seen and felt in all sorts of ways. (SEP)
Coreference in claim:
[CLS] peshmerga forces are national military force of
H
NH
NH
when claim contains a
[SEP) the peshmerga are the national military force of kurdistan. (SEP)
reterence to some entiry in
context
Incorrect true label:
[CLS) hundreds migrants on board may have capsized after being
NH
NH
H
when the true data itself incorrect
louched or swamped by a cargo ship that came to its aid [SEP] catania, italy cnn)) the boat that sank in the mediterranean over the
weokend with hundreds of migrants on board may have capsized after being touched or swamped by cargo ship that came to its aid
lofficial said., [SEP]
Figure The five different types scenarios in which SummaC-Come gives incorrect predictions, An example for each category is shown along with the predictions made by SummaC-Con and Our Model. Here, the darker colours depict higher importance scores while the light colours depict lower importance scores. The format [CLS] <claim> [SEP] <context> [SEP]. The last column shows the true label, i.c. H: Hallucinated and NH: Non-hallucinated.
act claim" and "Paraphrased claim". Our model gives the correct prediction in 52 out of 53 of these cases, which is due to the global attention bias mod- model to focus the relevant
orthogonal and cognitive-inspired approach that harnesses human eye-gaze patterns for hallucina- tion detection. To support our approach, we cre- ated and analyzed eye-tracking corpus, which revealed various attention biases exhibited by hu- mans during hallucination searches. Building upon this insight, we designed deep learning archi- tecture that combines gaze data and hallucination classification data for training. Our experiments demonstrate promising results, indicating that a gaze-driven model exhibits superior performance and improved interpretability. Moving forward, we intend to explore deeper cognitive features, such as progressive and regressive saccades, further enhance hallucination detection. Additionally, ex- tending this work to multilingual and multimodal settings a key objective on our agenda.
ule that enables parts of the context and give correct output whereas SummaC-Coni lacks that ability. Similarly, in the cases of ambiguous claims or co-reference, our model performs better. The last error category, In- correct true label, includes 8 cases where the true label itself found to be wrong. Here, in 5 out of 7 cases, we find that our model gives the same pre- diction the true label. Figure 5 shows examples for each error category, with the heat maps of our model for each example. The heat maps depict the local attention bias scores generated by our model where more scores to the necessary tokens, which helped in making a correct prediction.
8 Conclusion and Future Work
Limitations
Hallucination detection is a pertinent and unre- solved task in computational linguistics, often necessitating the incorporation of extralinguistic information into models. While traditional ap- proaches benefit from leveraging diverse knowl- edge sources, we recognize the limitations rely- ing on outdated information and propose a rather
The existing cognitive framework falls short when it comes to addressing situations of internal incon- sistency where multiple sentences within given context contradict one another. Moreover, the cur- rent research overlooks intricate linguistic phenom- ena such as sarcasm and thwarting, focusing exclu- sively on hallucinations that emerge from perturba-

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tions. Recognizing the diverse origins of halluci- nations is crucial, and effectively addressing this challenge may require datasets encompassing a wider range of real-world scenarios and pragmatic instances.
Joshua Bensemann, Alex Peng, Diana Benavides-Prado Yang Chen, Neset Tan, Paul Michael Corballis, Patri- cia Riddle, and Michael Witbrock. 2022. Eye gaze and self-attention: How humans and transformers attend words in sentences. In Proceedings of the Workshop Cognifive Modeling and Computational Linguistics, pages 75-87, Dublin, Ireland. Associa- tion for Computational Linguistics.
Ethies Statement
Damiel Cer, Mona Diab, Eneko Agirre, Inigo Lopez- Gazpio, and Lucia Specia. 2017. Semeval-2017 task I: Semantic textual similarit-mmlliinguaal and cross-lingual focused evaluation. arXiv preprint arXiv: 00055.
Conducting eye-tracking experiments for hallucina- tion detection tasks requires commitment to ethi- cal principles, respect for participants' rights and well-being, and the responsible use and reporting the collected data. To this end, we took several steps. Prior to conducting such experiments, we ob- tained informed consent from participants, clearly explained the purpose, procedures, and potential risks involved. Privacy and confidentiality was en- sured by safeguarding participants' personal infor- mation and anonymizing their data. To minimize any discomfort or potential harm to participants during the eye-tracking sessions, adequate breaks and measures to prevent eye strain were provided. Participants were informed about their right to with- draw from the study at any point without penalty consequence. After collection, participants' eye movement data have been treated with utmost re- spect and handled in accordance with applicable data protection regulations. is also worth not- ing that while selecting examples from the FactCC dataset for the eye- eve-tracking experiments, we strived for diversity and inclusivity to avoid perpetuating or reinforeing any social, cultural, or gender biases.
Uschi Cop. Nicolas Dirix, Denis Drieghe, and Wouter Duyck. 2017. Presenting geco: An eyetracking cor- pus monolingual and bilingual sentence reading. Behavior research methods, 49:602-66 615.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and
Kristina Toutanova. 2018. Bert: Pre- Pre-training of deep bidirectional transformers for language understand- ing, arXiv preprint 1rXiv:1810.0480
Shehzaad Dhuliawala, Mojtaha Komeili, Jing Xu, Roberta Raileanu. Xian Li, Asli Celikyilmaz, and Ja- son Weston. 2023. chain-of-verificate reduces hal- lucination in large language models. arXiv preprint a3X9v:2309.110 1495.
Esin Durmus, He He, and Mona Diab. 2020. Feqa: A question answering evaluation framework for faithful- ness assessment in abstractive summarization. arXiv preprint 2005::2005.03754.
Nouha Dziri, Andrea Madotto, Osmar Zaiane, and Avishek Joey Bose. 2021. Neural path hunter: Re- ducing hallucination in dialogue systems via path grounding. arXiv preprint arXiv:2104.08455
Oliver Eberle, Stephanie Brandl, Jonas Pilot, and An- ders Segaard. 2022. Do transformer models show similar attention patterns to task ses--speciiic human gaze? In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume I: Long Papers), pages 4295-4309, Dublin, Ireland. Association for Computational Linguistics.
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challenge corpus for sen-
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A
Global Attention Bias
Kaitao Song. Xu Tan, Tao Qin, Jianfeng Lu, and Tie- Yan Liu. 2020. Mpnet: Masked and permuted pre- training for language understanding. Advances in Neural Information Processing Systems, 33:16857- 16867.
A.1
Models Used for Ensembling
As mentioned in Section 4.1.1, we employ an en- semble approach using the following sentence- transformer models: all-roberta-large-v/ (Liu et al., 2019), all-mpnet-base-vl (Song et al., 2020), gtr-t5-large (Ni et al., 2021), all-mpnettbase-v2
Ekta Sood, Simon Tannert, Diego Frassinelli, Andreas Bulling, and Ngoc Thang Vu. 2020a. Interpret- ing attention models with human visual attention in machine reading comprehension. arXiv preprint
(Song et al., 2020), and LaBSE (Feng et al., 2020). Table 4 describes the model sizes and perfor- mances? generating sentence embeddings.
arXiv:2010.06396
Ekta Sood, Simon Tannert, Philipp Müller, and Andreas Bulling. 2020b. Improving natural language process- ing tasks with human gaze-guided neural attention. Advances in Neural Information Processing Systems, 33:6327-6341.
Model
Performance
Model Size
all-roberta- large-v1 all-mpnet- base-vl
70.23
1360 MB
James Thore, Andreas Vlachos, Christos Christodooulpoouls,, and Arpit Mittal. 2018. Fever: a large-scale dataset for fact extraction and verification. arXiv preprint SE3i:1803.05333
69.98
420 MB
gtr-t5-large all-mpnet- base-v2
69.90
640 MB
69.57
420 MB
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, tukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30.
Table 4: Model sizes and performance generating sentence embeddings for different sentence-transformee models. The performance is the average performance (%) on encoding sentences over 14 diverse tasks from different domains.
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020a. Asking and answering questions to evaluate the factual consistency of summaries. arXiv preprint arXiv22004.04228.
The LaBSE model is a multilingual embedding model but shows good Semantic Textual Similarity
Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, and Changyou Chen. 2020b. Towards faithful neu- ral table-to-text generation with matching constraints, arXiv preprint 20X.0:2005.00969.
SSurce: https://www/ sbert, http:///www.bbbtt.../////////eee net//ocss/peetriined models. html

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Approach
Accuracy
Throughout the annotation process, eye position. and pupil size were meticulously tracked using an infrared video-based eye tracker (Byelink 1000 plus Head Supported Version 5.03, SR Research) at a sampling rate of 2000 Hz for both eyes. The eye tracker had an instrumental spatial resolution of 0.01 and a microsaccade resolution of 0.05", Be- fore each iteration, the eye tracker was calibrated using a 9-point grid. Participants were instructed to sequentially fixate their gaze on the dot presented at each of the nine locations in random order. The calibration process was repeated until the discrep- ancy between two measurements at any point was below 0.5", or the average error across all points was less than 1
Voting
0.9608
Only LaBSE
0.9589
Linear Regression 0.9512
Uniform Distribution 0.9501
Table 5: Accuracy of various ensembling mechanisms
used to determine the most accurate way to simulate human global attention bias.
(STS) benchmark (Cer et al., 2017) performance (72.8) measured by Pearson's r.
A.2 Implementation of Global Attention Bias
Section 4.1.1 discussed how global attention bias is modelled. Figure 6 shows a visualization of this modelling indicating how the global attention bias model has been incorporated into framework. Table 5 shows the accuracy of various ensembling mechanisms which we explored determine the most accurate way to simulate human global atten- tion bias.
B.2 Annotation Guidelines
The instructions outlined task that involves read- ing claim and determining its faithfulness sistency with respect given context. Partici- pants were instructed press the "+" key if the claim was faithful and the "_" key if unfaithful. The task emphasized the importance of careful reading and attention to detail. Before starting the annotation session, participants were asked to count the times the letter "FF" occurred in given text as a warm-up exercise. This exercise was in- tended ensure that participants read the upcom- ing instances carefully. During the annotation ses- sion, participants were presented with examples of claims and corresponding contexts. They were expected to evaluate the faithfulness each claim based on the highlighted text in the given context. Participants were reminded that their judgments should be based on the provided context and not require additional information. The examples pro- vided demonstrated the expected annotation pro- cess. In Example 1, the claim accurately reflected the information given in the context, leading to an accurate annotation. In Example 2, the claim contradicted the information the context, result- ing in an unfaithful annotation. Participants were encouraged to maintain efficiency regarding time and accuracy while annotating. The task to be approached search and matching exercise, focusing on the highlighted text within the context. Once participants familiarized themselves with the instructions and examples, they could click to start the annotation session.
B Eye tracking Experiment for IITB- LTTB-Hallucination Gaze corpus
B.1 Experimental and Environmental Settings
We use the SR Research Experiment Builder soft- 8 for the eye-tracking data collection experi- ment. It should be noted that while sampling in- stances from the FactCC dataset, we ensured mini- mal variance in the length of selected instances. During data acquisition, careful measures were taken to minimize sound and light interference in the room. Participants were positioned 70 em away from a 24-inch BenQ XI.2420Z. Widescreen LED Backlit TN Monitor, which had display di- mensions of 569x337.8mm and resolution of 1920x1080 pixels, resulting in a display area of 531.36x298.8m mmm.. The monitor had a vertical re- fresh rate of 144 Hz. Sentences were presented in black font on a light grey background using a 20-point Arial font, equating to a letter height of 0.8mm. To ensure stability, chin rest was pro- vided, and participants were instructed to maintain their head position throughout the experiment to avoid motor artefacts. Refreshments and breaks were provided participants to promote relaxation between iterations.
httt::/www..r-rresaacc..omm

---

Claim Embedding Cur
Most similar Synt
all loberta-large-v
Context Sentences Embeddlings ml m0
Claim Embedding Cm2
all-mpnet-base-vi
Context Sentences Embedoings @ 60my. 0f
Most similr sentence Suz2
Claim Embedding mma
Sentence voted similar by majority modele
Context
gtr-t5-large
Context Sentences Embediings
Most aimiler sentence
$1, 12.2 sn
... Bfly
Claim Embedding Cy4
Impul
a
Context Sentences Embadclings 61 67 ... 600m
Most similar sentence
Output
Claim Embedding Cet
Most similar sentence
LaBSE
Context Sentences Embeddiigg e/ 52 enlns
Different modele ueed in ensemble
Embedcings computed the models
Most simliar sentence
GLOBALATTENTION
Figure 6: Modelling global attention bias using an ensemble approach. Each model ml .m2 computes sentence embeddings for claim and context sentences. The sentence which is most similar to the claim is found by using cosine similarity between the claim emhedding and sentence embedding for each model. The context sentence voted most similar by the majority of models taken as the output of the global attention model.
Baselines Definition
FEQA (Durmus et al., 2020); incorporates question answering based approach to detect the inconsistency from the extracted answers from document summary pair.
In Section 6 we show comparison study of the results of our model with other baseline models. The details of those baseline models are given in this section. We have considered the following models as a baseline for comparison defined in (Luo et al., 2023) for the given task.
QuestEvvl (Scialom et al., 2021): incorporates QA based metrics in the FEQA approach.
ChatGPT-ZS(LLu et al., 2023): Chat-GPT in zero shot setting
BERT MNLI (Kryscinski al., 2020): BERT trained on MNLI dataset (Williams et al., 2018)
ChatGPT-COT (Luo al., 2023): Chat-GPT in chain of thought setting (Wei et al., 2022)
BERT + FEVER (Kryscinski et al., 2020): BERT trained on FEVER dataset (Thorne et al., 2018)
SummaCZS (Laban al., 2022b): incorpo- rates the NLI-based approaches for generating entailment scores at sentence level granularity followed by statistical aggregations like mean, median etc.
MNLI-doc (Liu al., 2019): finetunes Roberta model on MNLI datasct and incor- porates entailment-base based approach for hallu- cination detection
SummaC-Conv (Laban et al., 2022b); incor- porates convolution layer for aggregation in the previous approach
NER Overlap (Laban et al., 2021): incorpo- rates named entity matching from claim to context
D Human Error evaluations
FACTCC-CLS (Kryseinski al., 2020):
In this section, provide further example in Figure 7 to describe the of semantic overlap. The actual label for the specific claim-context pair provided Figure 7 is "Hallucinated." while the prediction by the human annotator "Not Hal- lucinated." " This mismatch can attributed to the
Roberta model finetuned FACTCC dataset
DAE (Goyal and Durrett, 2020): incorporates NLI based approaches in dependency are for detecting hallucination

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Task
Dataset
Tokens (words)
Natural Reading
Zuco 1.0 (Hollenstein et al., 2018)
21,629
Natural Reading
Dundee (Kennedy et al., 2013)
56,212
Natural Reading
PSC (Kliegl et al., 2004)
1,138
Natural Reading
GECO (Cop et al., 2017)
114,080
Natural Reading
Provo (Luke and Christianson, 2018)
2,689
Natural Reading
CopCo (Hollenstein al., 2022)
34,897
Natural Reading + Relation Extraction
Zuco 2.0 (Hollenstein et al., 2020)
15,138
Sarcasm Detection
Sarcasm corpus (Mishra et al., 2016)
~25,000
Cognate Detection
Cognate Corpus (Kanojia et al., 2021)
3,195
Hallucination
WTTB-Hallucinatinn Gaze corpus*
52,000
Table 6: Summary of Gaze Datasets and Word Counts for Various NLP Tasks
Claim:
Disputes over the constitutionality of the Marriage Protectiototcttion
Act gathered simig: en riaws on
Context: (CNN)- -- Publicly expressing deeply held emotions is not always easy. When atterding a rally, displaying clever sign can attract more attention than even the most powerful chant. Tuesday's protests in front of the J.S., Supreme Court attracted outspoken crowds on both sides of the oeer over same-sex marriage. The the Derense of Marriage Act drew similar crowds. Here are 16 sighs mixed bravery and humor with strong point of view. Share your images of the rallies by tagging #cnnireport on on Instagram, or share your reaction to the hearings. Mobile users click here to see the Storify.
arguments Wedric
the Instabaiionaai
Figure Example showing a higher semantic similarity between the claim context. this example, the true label is hallucinated and the predicted label by the annotator is non-hallucinated.
considerable semantic similarity between the "Mar- riage Protection Act" the claim and the "Defence of Marriage Act" of the context, leading to the an- notator's prediction of "Not Hallucinated."
of Marriage Act drew similar crowds. Here are signs that mixed bravery and humor with a strong point of view. Share your images of the rallies by tagging ennireport on Instagram, or share your re- action to the hearings. Mobile users click here to see the Storify.
Claim: Disputes the constitutionality of the Marriage Protection Act gathered similar crowds on Wednesday.
E Comparison with Other Datasets
Context: (CNN) Publicly expressing deeply held emotions is not always easy. When attending rally, displaying clever sign attract more attention than even the most powerful chant. Tues- day's protests in front of the U.S. Supreme Court attracted outspoken crowds on both sides of the debate over same-sex marriage. The arguments Wednesday on the constitutionality the Defense
As mentioned in the Dataset Details section 5. we provide a word-level comparison with other publicly available gaze datasets.


Reviews:
[{"review_id": "irw5qLIeyx", "reviewer": "Reviewer_EMNe", "summary": "", "questions": "A.\tThe figure 2 in the paper is too simplistic, and the axes are not consistent, making it visually unappealing. The author may consider revising the figure for better presentation. Similarly, in figure 3, the arrows and symbols representing clicks have some issues. The author could optimize these two figures for better clarity and visual appeal. \nB.\tIn the results section, there is indeed a lack of necessary analysis of the experimental outcomes. It is hoped that the author can provide additional explanations and clarifications.\nC.\tIn the results section, while some ablation results are included, they are not sufficiently detailed. The author could further conduct more comprehensive ablation experiments, such as excluding the use of global attention and local attention and observing the results with only “BERT+GAZE”.", "limitations": "", "rating": 3, "confidence": 2, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear esteemed reviewer, thank you for the reviews.\n\nWe would like to clarify a few points -\n -   The experimental results were obtained on **FactCC** (section 5.1.1: line 441), a dataset **publicly released by Kryscinski et. al. (2020)**. Our contribution was to add gaze signals to this dataset which was used in our framework (only to train local and global attention). We specifically relied on FactCC (section 5.1.1: line no.448) due to the presence of a broad range of claim-context relationships and well-defined perturbation crafted by introducing variations in the context which facilitated the eye-tracking experiments.\n -   We have performed an analysis of the experimental outcomes as mentioned in Section 7 which shows that the proposed framework has the capability of much better interpretability (see Figure 4). We have also addressed error scenarios in the Limitation sections of the paper. Apart from this, we also show a behavioural analysis of humans while they perform the task of hallucination detection in section 3.3 which provides significant psycholinguistic insights.  \n -   Here are some of the additional analyses that provide further insights:\n    \n\t - Interpretability:\n\t\t - In non-hallucinated instances, we notice that identical words appearing in both the claim and context are strongly focused on. This suggests that the claim and context share similar meanings aligning with the idea that similar semantic presence in claim and context leads to consistency.\n\t\t - In Hallucinated instances, we observe that perturbations of the context responsible for hallucinations are given more importance scores.\n\t - Error Analysis:\n\t\t - We observe an **overall error rate of 10.44%.**\n\t\t - We observe that only **0.4%** of instances were misclassified as hallucinated due to semantic inconsistency in the instances.\n\t\t - We observe that **10.04%** of instances were misclassified as non-hallucinated due to high semantic overlap between claim and context as mentioned in the Limitation section.\n\t\t\n\t But we humbly ask for further clarification about what the reviewer exactly means by “further analysis of experimental outcomes” and what additional details are requested in the explanation of baselines.\n\n-   Regarding the performance of the SummC-Conv model: the FactCC dataset is imbalanced and noisy, the SummaC-Conv binning strategy provides the architecture with good noise reduction capability resulting in a slightly better balanced accuracy score.\n    \nResponse to “Questions For the Authors”\n\n1.  Thank you for pointing this out, we will improve the figures and make them more consistent. It would be great if you could provide more input on how to make the figure more visually appealing.\n    \n2.  As mentioned before, we provide analysis in Section 7 and we talk about the error scenarios in the Limitations section and in the appendix. We also conducted some further analysis and reported the same above in this response\n    \n3.  In our architecture, it is **not feasible to integrate the “GAZE” signal** directly with the BERT architecture since it requires a local attention bias / global attention bias module, contrary to the other architectures mentioned in section 2.2 (line no.196 to 210).  The gaze signal is required to ground the Global attention bias (for sentence level importance: line no. 349 to 356) and finetune the Local attention bias model (for token level importance: line no. 395 to 400). Hence “GAZE” signal has no direct interaction with the BERT, therefore we have not reported GAZE+BERT ablation results.  \n      \n***\nThanks a lot for all the valuable feedback. Should our response meet your expectations, we kindly request your consideration in the possibility of favorably adjusting the scores.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.\tThe innovation in this paper is remarkable. Through analysis, it is evident that humans selectively focus on relevant parts of the text based on distributional similarity, akin to the phenomenon of attention bias in psychology. This paper identifies two attention strategies employed by humans: global attention (focusing on the most informative sentences) and local attention (focusing on important words within sentences). Leveraging these insights, the paper proposes a novel cognitive framework for hallucination detection that integrates these attention biases derived from human gaze data.\n2.\tThe experiments in this paper are comprehensive and effectively validate the effectiveness of the human eye-gaze patterns for hallucination detection.\n3.\tThis paper collects the first eye-tracking data from five annotators, annotating 500 instances of claim-context pairs carefully extracted from the FactCC dataset. During the annotation process, the paper captures consistent patterns of the annotators' gaze on the claim and context texts, along with their respective labels. Notably, the inter-annotator agreement (IAA) Kappa score reached 0.60, indicating substantial agreement among the annotators.", "reasons_to_reject": "1.\tThe experimental results in this paper were obtained using a dataset constructed by the authors, and there is no validation on other datasets to verify the reliability of the proposed method.\n2.\tIn the results section, the author merely lists their experimental results and the compared baselines without conducting further analysis on the experimental outcomes. Additionally, the explanations for these baselines in the appendix are overly simplistic.\n3.\tIn the experimental results of Table 3, the baseline \"SummaC-Conv\" achieves higher performance than the results presented in the paper. However, the author does not provide any explanation or interpretation.", "questions_to_authors": "A.\tThe figure 2 in the paper is too simplistic, and the axes are not consistent, making it visually unappealing. The author may consider revising the figure for better presentation. Similarly, in figure 3, the arrows and symbols representing clicks have some issues. The author could optimize these two figures for better clarity and visual appeal. \nB.\tIn the results section, there is indeed a lack of necessary analysis of the experimental outcomes. It is hoped that the author can provide additional explanations and clarifications.\nC.\tIn the results section, while some ablation results are included, they are not sufficiently detailed. The author could further conduct more comprehensive ablation experiments, such as excluding the use of global attention and local attention and observing the results with only “BERT+GAZE”.", "paper_topic_and_main_contributions": "This is a paper about hallucination detection. In this paper, In order to address the sustainability issue arising from traditional methods that require the latest knowledge, this paper proposes a novel approach to hallucination detection by leveraging the gaze pattern in the form of human cognitive and behavioral information. The main contributions of this paper are as follows:\n1.\tThis paper creates and shares the first eye-tracking corpus for hallucination detection, which includes 500 instances of context and claim pairs annotated by five annotators.\n2.\tThis paper's behavioral analysis of the annotated data reveals a recurrent pattern where annotators tend to browse through some irrelevant contexts while selectively focusing on information crucial for establishing or refuting hallucinations. By analyzing the gaze patterns of human annotators during the hallucination detection task, a novel concept of attention bias is introduced.\n3.\tThis paper introduces and evaluates a BERT-based cognitive-inspired deep learning framework for hallucination detection driven by attention biases in various forms observed during human reading. Experimental evaluations on the FactCC dataset demonstrate the effectiveness of our approach, surpassing baseline models while achieving improved interpretability.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "RlpziSa6rn", "reviewer": "Reviewer_Qrwq", "summary": "", "questions": "\n1. Have you considered exploring the integration of gaze information with trending open-source LLMs for hallucination detection in your future research? How do you think gaze signals could potentially augment the capabilities of LLMs in detecting hallucinations?\n\n2. In Section 5.1.3, could you clarify the differences between your proposed dataset and the other published datasets? What are the specific characteristics that set your dataset apart from the others?\n\n3. Do you think it would be beneficial to adopt a multi-modal approach for hallucination detection, incorporating gaze information with other modalities like text or audio? How do you envision a multi-modal approach enhancing the performance of hallucination detection models?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear esteemed reviewer, thank you for your reviews. \n\nHere are a few points addressing the analysis:\n -   We have performed an analysis of the experimental outcomes as mentioned in Section 7 which shows our system (BERT+GAB+LAB+gaze) has the capability of much better interpretability (see Figure 4). We have also addressed error scenarios in the Limitations section of the paper. Apart from this, we also show a behavioural analysis of humans while they perform the task of hallucination detection in section 3.3 providing significant psycholinguistic insights.\n    \n -   Here are some of the additional analyses that provide further insights:\n\t - Interpretability:\n\n\t\t- In non-hallucinated instances, we notice that identical words appearing in both the claim and context are strongly focused on. This suggests that the claim and context share similar meanings. This aligns with the idea that similar semantic presence in claim and context leads to consistency.\n\t\t- In Hallucinated instances, we observe that perturbations of the context responsible for hallucinations are given more importance scores.\n\n\t - Error Analysis:\n\t\t - We observe an **overall error rate of 10.44%**.\n\t\t - We observe that only **0.4%** of instances were misclassified as hallucinated due to semantic inconsistency in the instances.\n\t\t - We observe that **10.04%** of instances were misclassified as non-hallucinated due to high semantic overlap between claim and context as mentioned in the Limitation section.  \n      \n    \nHere are a few points that justify the size of the data:\n\n-   We humbly state that the purpose of gaze data is to do a behavioural analysis of humans during the task of hallucination detection. This makes 500 instances enough to gather and validate the insights from this study which were used to design the proposed cognitive framework and ground the global attention bias model.\n    \n-   For finetuning the local attention bias model, we have added additional sentence-level fixation scores taken from Provo and Geco Corpus making a total of 5673 instances making it sufficient for the stated purpose.  \n      \n    \n-   Here is a word-level comparison with other publicly available gaze data sets.\n| Task                        | Dataset               | Tokens       |\n|-----------------------------|-----------------------|--------------|\n| Natural Reading | Zuco 1.0              | 21,629 words |\n| Natural Reading + Relation Extraction         | Zuco 2.0              | 15,138 words |\n| Sarcasm Detection           | Mishra et al. (2016)  | ~25000 words |\n| Natural Reading              | Dundee                | 56,212 words |\n| Cognate Detection           | Kanojia et al. (2021) | 3,195 words   |\n| **Hallucination**               | **Ours***                 | **52,000 words** |\n\n  \n\n**Response to “Questions For the Authors”**\n\n1.  Yes, we have considered the integration of gaze information in the trending LLMs. This is one of the items on our next agenda. \nThe gaze information is highly beneficial for less powerful LLMs that lack proper semantic/pragmatic understanding. Essentially the purpose of gaze in LLMs is to induce inductive bias towards certain prior which can act as a surrogate to world knowledge. \nLLMs with large parameter space can benefit from gaze to enhance explainability or instil human behaviour in the model when instances requiring human judgment are considered.  \n      \n    \n2.  In the context of the proposed architecture, the FactCC dataset has only textual instances for hallucination (without gaze) and the PROVO+GECO dataset has textual instances for the natural reading task along with gaze. Our contributed dataset **has gaze features along with textual instances for hallucination**.  \n      \n    Here are some more details pointing out the differences in our dataset from other publicly available eye-tracking datasets:  \n    -   The prior published eye-tracking datasets mostly consider natural reading tasks and seldom delve into task-specific cases like sarcasm detection, sentiments etc. To the best of our knowledge, **no prior dataset is available for hallucination detection**.  \n    -   Moreover, this dataset would find its utilities in **information retrieval tasks** as the given task involves searching for relevant context and then matching the same with the given claim. A similar kind of behaviour can be expected in the task of information retrieval.  \n      \n\n3.  Yes, we think the use of a multimodal approach can be beneficial for hallucination detection. Audio and video can provide important information like **tonal behaviour (pauses may characterize inconsistency)** and **facial expressions (surprise)** which can be important indicators of inconsistency. We believe that these signals can significantly benefit very niche sets of instances that can not be disambiguated from textual information.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1) This paper shares a valuiablel eye-tracking corpus, this may be very helpful for this task in the future.\n\n2) The proposed method seems sound and effective to some extent.\n\n", "reasons_to_reject": "\nThe analysis section is relatively brief and lacks depth, requiring a more comprehensive and thorough analysis. \n\nThe corpus comprising only 500 instances might be considered small for certain tasks, raising concerns about the generalizability and statistical significance of the findings.", "questions_to_authors": "\n1. Have you considered exploring the integration of gaze information with trending open-source LLMs for hallucination detection in your future research? How do you think gaze signals could potentially augment the capabilities of LLMs in detecting hallucinations?\n\n2. In Section 5.1.3, could you clarify the differences between your proposed dataset and the other published datasets? What are the specific characteristics that set your dataset apart from the others?\n\n3. Do you think it would be beneficial to adopt a multi-modal approach for hallucination detection, incorporating gaze information with other modalities like text or audio? How do you envision a multi-modal approach enhancing the performance of hallucination detection models?", "paper_topic_and_main_contributions": "This paper focuses on hallucination detection and introduces a novel cognitive approach that utilizes gaze signals from humans. \n\nThe authors begin by collecting a valuable eye-tracking corpus comprising 500 instances, which serves as the foundation for their proposed hallucination detection method. \n\nThey demonstrate the consistency of human behavior and attention bias phenomenon in psychology, providing insights that support the effectiveness of their approach.\n\n\n", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "leHdJ0SPiY", "reviewer": "Reviewer_WXZX", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear esteemed reviewer, We thank you for your reviews.\n\nWe've analyzed our experiments in Section 7, revealing that our system (BERT+GAB+LAB+gaze) significantly enhances interpretability. Notably, comparing Figure 4a (BERT+GAB+LAB finetuned on gaze) with Figure 4c (BERT+GAB based on raw attention) highlights the interpretability improvement from incorporating gaze. This underlines the distinction between gaze information and standard attention in hallucination detection.\n\nWe have done similar analysis on other instances as well which show a similar trend. We plan to include in the camera-ready version if accepted.\n\nHere are some additional insights on interpretability analysis:\n\n-   In non-hallucinated cases, we notice high attention on identical words in both the claim and context, which aligns with the idea that shared semantics between them promote consistency.\n    \n-   In hallucinated instances, we observe that certain parts of the context that were perturbed to form a hallucinated claim are given more importance scores.\n    \n\nHere are some points which highlight the utility of the proposed cognitive framework in contrast to traditional methods:\n\nOur proposed attention bias framework: global (sentence level importance) and local (token level importance) is inspired by human behaviour. We emphasize that only local attention bias can be contrasted with ordinary attention, as the latter lacks the concept of sentence-level significance.\n\nThe utility of global and local attention biases is explained below:\n\n-   **Global attention bias** focuses on relevant sentences, eliminating noise from inputs. In section 3.3 (line 302), we highlight that **only 20-30% of tokens per instance** are required on average for inference due to global attention bias. This significantly reduces the computational requirement and enables the use of LLMs trained for a low number of input tokens. This can also help other NLP tasks with high input sizes.  \n      \n    \n-   **Local attention bias** (boosts importance scores of the words): Figure 4 shows that LAB improves interpretability compared to vanilla architecture. Fine-tuning LAB with gaze induces inductive bias gathered from human behaviour resulting in better and more natural token level interpretability. Consequently, analyzing local attention also yields richer psycholinguistic insights.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Showing that the gaze information is beneficial for detecting hallucination.", "reasons_to_reject": "The paper does not show *how* the gaze information is different from ordinary attention for detecting hallucinations.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes to leverage gaze information to attend more appropriately for\ndetecting hallucination on the output of large language models. Specifically, first the\nmost relevant sentence is selected from global attention, also obtained from gaze\ninformation, and afterwards local attention gives a fixation score for each word to\nbe fed into a hallucination detector neural network.\nExperimental results indicate that the accuracy of the final model is slightly worse than\nthe state-of-the-art, but basically equally well. Simple example is given for anecdotal\nexplanation.\n\nBasically the idea is interesting and works well, it is a bit pity that the property of\nthe proposed approach is not well investigated. Since the performance itself does not\nseem to\tbe a final goal\t(since sota model performs better), *where* and *why* the gaze\ninformation works is the most important for this research. For that purpose, a simple\nanecdotal example in Section 7 is clearly not enough, and some systematic comparison\nneeds to be executed. If the gaze information is shown to be somewhat orthogonal to the\ninformation used in non-gaze approaches, the impact of this paper will be larger\nupon publication.\n", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper "Hallucination Detection Using Attention Biases Derived from Human Gaze Data" proposes a novel approach to hallucination detection by leveraging gaze patterns in the form of human cognitive and behavioral information. The main contributions of the paper include the creation and sharing of the first eye-tracking corpus for hallucination detection. 

However, there are also several weaknesses as the reviewers raised. Firstly, the experimental results were obtained using a dataset constructed by the authors, and there is no validation on other datasets to verify the reliability of the proposed method. Secondly, the explanations for the baselines in the appendix are overly simplistic, and the authors merely list their experimental results and the compared baselines without conducting further analysis on the experimental outcomes. Thirdly, the paper does not provide a comprehensive analysis of the differences between the proposed dataset and the other published datasets, and it is unclear what specific characteristics set the dataset apart from the others.

A final note is that there are quite a few hallucination related studies released before the submission deadline of EMNLP 2023. But unfortunately, the authors seem to be unaware of these work,  and thus the novelty and reliability of this work can be questionable.
Decision: Accept-Findings