Fast and Accurate Factual Inconsistency Detection Over Long Documents
Authors: Barrett Martin Lattimer
Patrick CHen
Xinyuan Zhang
Yi Yang
Venue: EMNLP 2023 Main (2023)
Keywords: Hallucination detection
Inconsistency detection
hallucination
automatic evaluation
metric
long document
efficient
fast
accurate
long
natural language generation
task agnostic
nlp
nlg
TL;DR: Introducing SCALE, a task agnostic hallucination/factual inconsistency detection model for long documents and ScreenEval, a novel factual inconsistency detection dataset for long dialogue documents.

Abstract: Generative AI models exhibit remarkable potential; however, hallucinations across various tasks present a significant challenge, particularly for longer inputs that current approaches struggle to address effectively. We introduce SCALE (Source Chunking Approach for Large-scale inconsistency Evaluation), a task-agnostic model for detecting factual inconsistencies using a novel chunking strategy. Specifically, SCALE is a Natural Language Inference (NLI) based model that uses large text chunks to condition over long texts. This approach achieves state-of-the-art performance in factual inconsistency detection for diverse tasks and long inputs. Additionally, we leverage the chunking mechanism and employ a novel algorithm to explain SCALE's decisions through relevant source sentence retrieval. Our evaluations reveal that SCALE outperforms existing methods on both standard benchmarks and a new long-form dialogue dataset ScreenEval we constructed. Moreover, SCALE surpasses competitive systems in efficiency and model explanation evaluations. We have released our code and data publicly to GitHub.

Fast and Accurate Factual Inconsistency Detection Over Long Documents
Barrett Martin Lattimer
Patrick Chen
Xinyuan Zhang"
ASAPP blattimer@asapp.coo com
ASAPP
Abstractive Health
pchenéasapp.com
dylanz04266gmail. com
Yi Yang
ASAPP
yyangêasapp.com
Abstract
Prior research on inconsistency detection has predominantly dealt with short documents in of- fline settings (Laban et al., 2022; Schuster et al., 2022; Utama et al., 2022) and relied heavily on sentence-level text matching techniques. Conse- quently, these methods exhibit slow performance in processing longer documents and suffer from poor calibration. Such characteristics pose sub- stantial challenges in implementing them in real- world online environments, where incorporating inconsistency detection could potentially result a substantial increase in latency. Additionally, the absence well-calibrated scores complicates the balancing act between mitigating the risk of in- corporating hallucinations and excluding pertinent information from the model output. Given the expo- nential growth in context sizes (maximum allowed tokens of an input) of contemporary large language models (LLMss), there an increasing urgency to develop efficient and effective approaches for inconsistency detection in lengthy documents.
Generative models exhibit remarkable po- tential; however, hallucinations across vari- ous tasks present a significant challenge, par- ticularly for longer inputs that current ap- proaches struggle to address effectively. We in- troduce SCALE (Source Chunking Approach for Large-scale inconsistency Evaluation), task-agnostic model for detecting factual in- consistencies using novel chunking strategy. Specifically, SCALE is Natural language in- ference (NI.) based model that uses large text chunks to condition over long texts. This ap- proach achieves state-of-ther thrtthc-rrt performance in factual inconsistency detection for diverse tasks and long inputs. Additionally, we leverage the chunking mechanism and employ a novel algo- rithm to explain SCALE's decisions through relevant source sentence retrieval. Our evalu- ations reveal that SCALE outperforms exist- ing methods on both standard benchmarks and a new long-form dialogue dataset ScreenEval we constructed. Moreover, SCALE surpasses competitive systems in efficiency and model explanation evaluations. We have released our code and data publicly to GitHub!.
In addressing the challenges, we introduce SCALE (Source Chunking Approach for Large- scale inconsistency Evaluation). method designed for efficient detection of factual inconsistencies generated sentences by identifying related source text snippets. SCALE consists of two crucial com- ponents. First, it builds on a Natural language in- ference (NLJ) based method, integrating a novel chunking mechanism for rapid and accurate online performance in diverse natural language genera- tion (NLG) tasks. Second, model explanation is essential for real-time deployment of inconsistency detection systems, facilitating swift human inspec- tion to determine model configurations. We show that our chunking mechanism improves calibration scores and enables the use of a binary search tree algorithm for rapidly locating relevant source text snippets for target sentence, ultimately enhancing
1 Introduction
Large Language Models (LLMs) have shown im- mense promise in various applications, but deploy- ing them in real-time presents certain challenges such as hallucinations (Cao et al., 2018; Falke et al., 2019; Kryšciáski et al., 2019; Fabbri et al., 202 Honovich et al., 2022). Hallucinations, or factual inconsistencies generated by model relative to source document, can mislead the user and un- dermine trust in LLMs. Thus, detecting factual inconsistency in LLM generations is crucial for the future of LLMs, especially with the growing popularity of platforms like ChatGPT.
"Wark done while at ASAPP.
For instance, OpenAl GPT-4 and Anthropic Claude sup- port context sizes up to 32k and 100k tokens, respectivcly.
1https: / hhw.b com/ http://apreeerrchssclaeeeesee

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the explanation of model behaviors. Current benchmark datasets for factual inconsis- tency detection predominantly feature short docu- ments. In order to evaluate SCALE using a more realistic dataset with long documents, we intro- duce ScreenEval assess the factual inconsistency of summary sen- tences generated by humans, Longformer, and GPT- 4 in comparison to actual long-form dialogues. ScreenEval encompasses 52 dialogues, averaging over 6,000 tokens per dialogue. The use of dia- logue in this dataset poses considerable unique challenges such as long-distance coreference res- olution and significant noise between utterances. To the best of our knowledge, ScreenEvall is the longest dialogue based dataset for factual inconsis- tency detection presently available.
encountered challenges (Khot al., 2018), recent advancements have shown that NLI models can ef- fectively assess the factual consistency of generated text (hypothesis) with respect to a source (premise) (Utama et al., 2022). This progress can largely be attributed to addressing the granularity problem, which arises from the abundance of current NLI datasets predominantly comprised of short, single- sentence premises and hypotheses (Williams al., 2017: 7: Nie al., 2019; Thome et al., 201 8a; Schus- ter et al., 2021a).
novel dataset designed to
SCALE is an NLI based method and our find- ings indicate that utilizing larger premise chunks enhances efficiency and outperforms sentence de- composition. Although SeNILI (Schuster et al., 2022) extended NLI based methods to longer doc- uments, it adhered to the sentence decomposition assumption and focused solely on summarization tasks. SummaC (Laban et al., 2022) investigated various aggregation techniques for NLI scores ob- tained from sentence decomposition to generate overall summary scores. Meanwhile, SWING (Huang et al., 2023) developed a loss function to train models for improved NLI performance, yield- ing mixed outcomes.
In our experiments, we first show that SCALE outperforms and is better calibrated than baseline methods across various NLG tasks on the standard factual inconsistency detection benchmark TRUE (Honovich et al., 2022). We then assess ac- curacy, speed, and model explanation (via relevant text retrieval evaluation) dataset for long document factual inconsistency detection. Our findings indicate that SCALE sur- passes strong competitors in the majority of tests. The key contributions of this paper are:
the new ScreenEval
In QA based methods, a question is first gen- erated based on a summary sentence, and a QA system is used to give an answer. A summary is considered factually consistent if the generated an- swer significantly overlaps with the original sum- mary (Durmus al., 2020). Prior research focused on using different question generation strategies (Scialom et al., 2019) or overlap measures (Deutsch and Roth, 2021). the experiments, we consider the most competitive QuestEval (Scialom et al., 2021 and QAFactEval (Fabbri et al., 2021b).
We introduce SCALE, a reference- NLI based factual inconsistency detection method with novel chunking strategy for versatility across domains and extended documents.
We show SCALE's broad applicability in NLG domains by attaining statc-of-the- per- formance on the TRUE benchmark.
We build ScreenEval, a novel dataset designed for factual inconsistency detection in long di- alogues, and then demonstrate SCALE's su- periority in accuracy. efficiency, and model explanation evaluations on the dataset.
Detecting Factual Inconsistencies in Long Docu- ments Prior work on factual inconsistency perfor- mance in long source documents has been limited in scope. For example. ContractNLI (Koreeda and Manning, 2021) concentrates on legal documents, which differ significantly from dialogues in terms of challenges. Likewise, LongEval (Krishna et al., 2023) emphasizes human evaluation strategies for scoring, without considering dialogues. To our knowledge, this paper presents the first dataset for evaluating factual inconsistency in long-form dia- logues, ScreenEval, thus addressing substantial gap in the literature.
2 Related Work
Factual Inconsistency Detection There are two main directions in factual inconsistency detection: Natural language inference (NLI) based and ques- tion answering (QA) based methods. NLI based methods, pretrained NLI models can be utilized to determine whether given "premise" factually entails a "hypothesis." Although initial attempts

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Source Document
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Lorem ipsum dolor amat, consectelur adiisscing elt.
bibendum lacinia tollus eget frugiat.
Qutsque portitor
prompt
0.4
tempor
eget fouglat
Donec vol sapion blandl.
elementum purus egel, pulvinar
prompt
(fpremise))
Sed tempus, mi id condimentum malesuada, risus, quam magna noque. Nam blandit, loctus at sodales frrnilia, nisi mauris egestas
Question: does this imply "(hypothesis))" ? Yes or no?
Flan-T5
interdum
prompt
0.2
prompt
0.1
Quis facilisia Iiuia arau au neque. Phosellus ut vahicula orat.
Figure I: Chunking mechanism for SCALE to produce score given a source document and generated text. The souree document broken into chunks (represented by dashed lines) and each chunk fed into a prompt as the premise. The highlighted generated text is fed into all prompts the hypothesis. Each prompt is then run through Flan-T5 and the resulting logits are used to compute the entailment score.
3 SCALE
lem (Utama et al., 2022). To bypass the granularity problem, natural generalization is to split both D and G into sentences and run M pairwise on each of those sentences then using aggregation function to generate the final entailment proba- bility, Numerous papers have used this approach generate competitive results (Schuster et al., 2022; Laban et al., 2022) however this generalization is hindered by a few shortcomings.
In this section we elaborate on our approach taken for our inconsistency detection model SCALE. Firstly, formally define the use of chunks and NLI in SCALE, aiming improving the accuracy and efficiency the system. Secondly, we propose to explain the model output by retrieving the rele- vant source sentence for a target, and show how the relevant sentence retrieval can be improved through the use of chunks.
First, the sentence decomposition of D and G does not properly capture the context provided D. By decomposing D and G into single sen- tences D = d21,d2,... .d....,dd ,,Dp) and G = :IG)) and put into the model to
3.1 Chunking Mechanism for NLI based Model
(91.92,- .2..... -j..... :.a....:jj........... evaluate as (d;, 9j), the context and long term dependencies present in D that may have factu- ally supported Oj very likely could not be repre- sented in d, Multiple sentences (e.g.. (13(1.3.6) d4) together in unison may be needed to support a single claim Jj- However, evaluating Jj against each sentence individually M (d1, gj), M (da, 95). M (ds.9) 9j) would likely lead to artificially low scores. Second, evaluating pairwise sentences of D and G is slow. It requires DI |G' model runs to obtain final score for one sentence gy.
Our approach uses NLI (Dagan al., 2006) as a building block for factual inconsistency detec- tion. An NLI model M provides the relationship between a premise p and a hypothesis h, M (p, h) with probabilities of three labels: entailed, neu- tral, and contradictory. For example, given an NLI model M h), source document D with set of facts Fp. and generated text G with set of facts FG. if FG C FD we would expect (D, G) to produce high entailment probability.
We define factual consistency between a gen- erated text and source document as FG C Fp. Canonical NLI models cannot be properly used for factual inconsistency detection because both p and h commonly single sentences in NLI mod- els, however in the factual inconsistency task their equivalents and almost always contain multi- ple sentences which M cannot effectively handle, leading to an issue known as the granularity prob-
SCALE poses a different solution to the granu- larity problem by decomposing /) into much larger chunks, which can be visualized in Figure 1. For- mally, SCALE decomposes document D into of N chunks C C1, C2 ......... Cy such that UcCC = D. SCALE can handle chunks of ar- bitrary length only limited by memory require- ments, thus drastically increasing the context win-

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0.5
0.3
to hypothesis h. Using a new search tree ap- proach enabled by chunking, SCALE is able retrieve dj context while using far fewer model runs than previous approaches. We use a greedy search tree approach that evaluates a hypothesis us- ing SCALE against progressively smaller chunks to find the highly relevant text from the source doc- ument. For the following example, assume we use binary search tree (BST) each level, dividing the text into two chunks. This process can be vi- sualized in Figure 2. Given hypothesis h, we want to find the most relevant utterance di the source text. We begin by dividing the entire source document into two large chunks. SCALE is used to calculate the score between both chunks and the hypothesis h and then use the higher scoring chunk as the new source text. The new source text is then divided into two chunks, and continues to de- scend in this manner until the chunk size becomes a single sentence or utterance d3. The best scoring chunk is then chosen to be the supporting proof of the hypothesis from the source document.
Ted: I'm Bamey: On...
Ted: I'm no...
Ted: I'm no...
Bamey: Oh
Robin: I th...
Bamey; Oh
Robin: th...
Robin: th...
Well...
0.8
Tedl Eamay
0.4
Ted: Wall...
Barnyy: My...
Well...
Bamey- My-..
Ted:
Bamoy: My
0.7
Ted: Fine...
Ted: Fine...
Ted: Fine...
Robin: Hi...
Ted: Oh hi...
0.2
Rabin:
Robin: Hi...
Ted: Wow L...
Ted: Oh hil..
Sentence: "Ted tells Barney that it's fine
Robin: Can...
Rabin: List...
Bamey: Hi...
Ted: Wow I...
Rabin: Can..
Bamey: Hi...
Figure 2: Visualization of SCALE's in context retrieval using chunks to find the most relevant source utterance given sentence. Each chunk is scored by SCALE, as shown by the gray boxes.
dow provided to the model through the premise. The generated text G is broken into sentences G = (9), .......-......... -....---(-..) We propose that decomposing D using chunks rather than sentences does not negatively affect the granularity problem but rather enables superior contextual capture in model M, boosts accuracy, and requires signifi- cantly less model runs.
This retrieval approach is able to significantly reduce the number of model calls needed find the relevant text from a source document. Previous approaches commonly break the source document down by sentence which requires O(n) model calls for a source document with n sentences. Whereas our BST approach only needs (loo(()) model calls in order to find the most relevant utterance in the same source document of n sentences.
SCALE uses Flan-T5 (Chung et al., 2022) as a backbone NLI model M. SCALE obtains the probability that a chunk & entails a generated sen- tence 9j through the following steps. First, log- its are obtained by prompting M with the follow- ing: logits = ("Icc) Question: does this imply "[g,)"? Yes or no?"). The entailment probability between gj and c is then calculated by
Notice that we proposed the binary search scheme due to its simplicity and its connection to the popular binary search tree. practice, div- ing the source text into only two chunks might cause out of GPU memory issues. In this case, we could generalize the proposed approach into dif- ferent chunk splits. For example, could divide the remaining tokens into three chunks or larger for the search of each block until the model fits the chunk. We could also use different chunk sizes for different layers so long as fits in the memory.
P Pentail SoftMax(logits logits("No"))
To obtain the overall entailment score for gener- ated sentence gy. the results are aggregated over all possible a by,
SCALB(C,gg) max (Pemait (Ci, 9j)) i= =....N
to obtain final measure of factual consistency.
ScreenEval Dataset
3.2
Model Explanation via Relevant Source Text Retrieval
We introduce a novel dataset for evaluating incon- sistency detection long form dialogues called ScreenEval. This dataset uses TV scripts and sum- maries pulled from the SummScreen (Chen et al., 2021) dataset. In addition to the provided human summarics, we generate summaries using Long- former and GPT-4 on 52 scripts from the Summ-
To produce explainable and interpretable scores for factual consistency and empower necessary hu- man inspection, it is important to justify the score by retrieving relevant text from the source. For- mally, the retrieval task involves finding the most relevant sentence d, in document with respect

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Screen test set. We then hire human annotators to classify the factual inconsistency of each sum- mary sentence and identify relevant supporting utterances for factually consistent summary sen- tences. ScreenEval is released publicly. Details of how we use Longformer and GPT-4 and collect human annotation can be found in the Appendix in the TRUE benchmark is condensed down into source document, generated text, and factual incon- sistency label. The datasets are distributed across different tasks as shown in Table 2.
Metric
Count
# of documents
52
# of summary sentences avg. # of utterances per doc
624
309
avg. # of sentences per summary
4
avg. # of tooens per doc avg. # of tokens per summary
6073
Task
Examples
Datasets
101
Summarization
5.245
5
avg. of tokens per summary sentence
26
Dialogue
10,613
3
# factually consistent sentences # factually inconsistent sentences
168
Fact Verification
81,263
2
58
Paraphrasing 8,000
1
avg. # of relevant utterances
5
Table 2: Number of examples and datasets for each task in the TRUE benchmark.
Table 1: Statistics for the ScreenEval dataset. These statistics use the Flan-T5 tokenizer
Summarization datasets are from FRANK (Pagnoni et al., 2021). SummEval (Fabbri et al.. 2021a), MNBM (Maynez et al., 2020), QAGS- CNNDM (Wang et al., 2020), and QAGS-XSum (Wang et al., 2020). Dialogue datasets include BE- GIN (Dziri et al., 2022), Q² (Honovich al., 2021), and DialFact (Gupta et al., 2021). Fact Verification datasets are FEVER (Thorne al., 2018b) and Vi- taminC (Schuster al., 2021b). The Paraphrasing dataset is PAWS (Zhang et al., 2019h).
The SummScreen dataset is comprised of sub datasets pulled from different sources Forever- Dreaming and TeggaSite.. We use the Forever- Dreaming subset of SummScreen, (SummScreen- FD) to create ScreenEval due its manageable summary size and diversity of shows and gen- res, spanning a total of 21 genres. SummScreen- FD uses duman--witten gold summaries from Wikipedia and TVMaze. Table shows statistics related to SvalenEval. Notably, the average num- ber of tokens in a source document is 6,073, which, to the best of our knowledge, makes ScreenEval the longest dialogue based inconsistency detection dataset created. We provide 52 documents with an associated 624 summary sentences, 455 of which are artificially generated using Longformer and GPT-4. Summaries are kept at a high level, cover- ing major plot points and character developments. This leads to shorter and more concise summaries that average only run 101 tokens.
FereenEval Our datasest ScreenEval compares the inconsistency detection ability of methods on long form dialogue. Details can be found in Sec. 4
5.2 Competitive Systems
TRUE provides inconsistency detection base- lines from 4 different inconsistency detection styles, namely n-gram based methods (token level F1), model based methods (BERTSSore (Zhang et al., 2019a), BLEURT (Sellam et al., 2020), FactCC (Kryscinski et al., 2020), BARTScore (Yuan et al., 2021), CTC (Deng et al., 2021)), NLI based meth- ods (ANLI (Honovich et al., 2022), SummaC (Laban et al., 2022)), and question answering (QA) based methods (Q2 (Honovich et al., 2021), QuestEval (Scialom et al., 2021b)).
For ScreenEval we compare SCALE to 8 mod- els which use NLI, QA, modern GPT systems, and older n- n-gram and semantic similarity methods. The baseline models consist two NLI based sentence decomposition approaches seNtLI (Schuster et al., 2022), and SummaCconv (Laban et al., 2022), a state-of saat-oo-the--an QA based model QAFactEval (Fab- bri et al., 2022), multidimensiinal QA model UniEval (Zhong et al., 2022), semantic similar- ity model based method BERTScore (Zhang et al., 2019a), an n-gram overlap method ROUGE (Lii,, 2004), and the two recent OpenAI models Chat- GPT, and GPT-4.
5 Experiments
5.1 Datasets
TRUE The TRUE benchmark contains 11 datasets from different NLG tasks. We compare our approach others on this dataset to show how SCALE performs across a multitude of NLG tasks on inconsistency detection. Notably, the average number of tokens per example in the TRUE bench- mark small, generally less than 512. Each dataset

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We also compare the performance of SCALE's search tree based relevant utterance retrieval with compare SCALE to the retrieval performance of Super- Pal (Ernst et al., 2020) which was shown to have superior retrieval capabilities in LongEval. We also compare against seNtLI (Schuster et al., 2022) which was designed to perform retrieval to identify factual inconsistencies over long documents.
of predicted labels that match true class labels in bin, formally defined
other recent retrieval models.
Bx)(Bx)= = |B&| ieB
1( E1(0 v.).
(1)
where d and yi are the predicted and true class la- bels for sample i. Confidence in bin Bg shows the average predicted score in bin, formally defined as
For SCALE, we include three variants of Flan- T5 as the backbone, namely base, XL, and XXL.
con = BB
Σ P
(2)
iE
5.3 Metrics
where pi is the model output score for sample i. Then the following equation is used to calculate ECE,
Accuracy Evaluation We compare the perfor- mance of methods primarily using four met- rics, ROC_AUC score, Pearson correlation, Kendall Tau correlation, and Fl _Mcro score. We employ the ROC_AUC score to quantify the abil- ity of different methods accurately identifying true positives and true negatives. Pearson and Kendall_ Tau correlations show the relationship be- tween methods and labels by measuring the cor- relations between the two. Finally the Fl Macro score is used to compare the continuous outputs of SCALE the discrete outputs of GPT-4 and ChatGPT. To obtain an Fl score for SCALE we use the optimal threshold to convert its continuous output into discrete values.
K
Bxl
ECE =
acc B+) conf((B:)l, (3)
using equation (1) and (2). A lower ECE indicates better calibration.
Relevant Source Text Retrieval if each model could return the correct utterance identified as relevant by human labelers. We report the recall of retrieval results.
Results
6.1 TRUE
Efficiency Evaluation We measure wall clock time in seconds for all of our experiments on ScreenEval. Wall clock time demonstrates how SCALE can be used efficiently in online setting especially when compared other models.
We first evaluate SCALE on the TRUE benchmark to confirm SCALE is NLG task agnostic and gen- eralizes well to the factual inconsistency detection.
Accuracy Evaluation Results For the TRUE benchmark shown Table 3, SCALEXXL pro- vides superior performance in out of datasets, and SCALEX achieves superior per- formance in 8 datasets compared to other non- SCALE models. Notably, other models were not previously able to perform well across all tasks, with Q Q having superior performance across datasets and ANLI having superior performance across 5. These results demonstrate SCALE's abil- ity to perform well across domains and against a large variety of model types.
Model Explanation Evaluation We evaluate Calibration and Relevant Source Text Retrieval for model explanation.
Calibration is the measure of how close the pscudo-probabile probability outputs of model to the actual probability of correct prediction. For ex- ample, well calibrated model that produces score of 0.2 would have 20% probability of being classified as 1. this paper, we use Expected Cali- bration Error (ECE) (Guo et al., 2017) to compare the calibration of SCALE to other commonly used models.
Model Explanation Evaluation Results Not only does SCALE provide superior performance on the TRUE benchmark, but it is also highly calibrated across NLG tasks. Table 4 shows the RCE of multiple methods across the TRUE bench- mark datasets. Note that a lower ECE bet- ter. SCALE provides the best calibration on
Given model outputs spanning from to 1, ECE separates the outputs into K equally sized bins B₃ between 0 and and takes the difference between accuracy acc and confidence conf in each one. The accuracy of a bin B₁ is the average amount

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Metric
FRANK
SummEEal
MN8M
QAGS-C
QAGS-X
BEGIN
0%
DialFact
PAWS
PEVER
VIC
FRIVER
vents
87.8
78.8
68.7
83.5
70.9
79.7
80.9
86.1
89.7
88.4
81.4
80.7
ANLI
80.5
$21
82.6
72.7
77.7
86.4
93.2
88.3
81.3
SCss
89.1
81.7
71.3
80.9
78.1
82.0
77.
84.1
88.2
93.2
97.9
81.4
76.1
61.4
46.2
63.8
51.1
86.4
65.9
72.3
51.1
51.8
61.4
BLEURT
82.8
66.7
64.5
71.6
57.2
86.4
12.4
73.1
68.3
59.5
61.8
71.4
QuestEval
84.0
70.1
65.3
64.2
56.3
84.1
72.2
77.3
69.2
72.6
66.5
71.4
FacCCC
76.4
75.9
59.4
76.4
64.9
64.4
63.7
55.3
64.0
61.9
56.3
66.7
BART
86.1
73.5
60.9
80.9
53.8
86.3
64.9
65.6
77.5
64.1
63.2
72.2
84.3
77.2
62.8
69.1
49.5
87.9
70.0
64.2
77.5
63.3
62.5
71.4
SCALE 88.0
79.4
74.2
81.0
81.4
79.3
84.3
91.6
96.9
93.9
89.3
84.0
SCALE,
89.8
83.3
73.2
82.1
84.3
77.8
86.3
91.0
91.9
94.5
91.5
84,4
SCALE.
90.8
91.5
73.1
85.2
85.3
79.2
86.6
93.4
96.7
94.8
92.7
88.1
Table TRUE benchmark results. ROC_AUC scores multiplied by 100 readability. Since SCzs uses FEVER and VitC in training, these two datasets are excluded when computing the average.
Metric
FRANK
SummEval
MNBM
QAGS-C
QAGS-X
BEGIN
Q².
DialFact
PAWS
FEVER
VaC
Avg
UniEval
0,333
0.238
0.173
0.26
D.315
0.507
0.153
D.396
0.333
0,084
0.242
0.276
BERTScere
0,2343
0.0730
0.1568
0.3200
0.483
0.3119
0.1760
0.1388
0.1923
0.4217
0.0648
0.233
SummaC:
0.199
0.322
0.128
0.067
0.143
0,085
0.412
0.116
0.355
0.109
0.185
0.184
SummaC,
0.122
SCALEs
0.093
0.243
0.026
0.060
0.116
0.069
0.286
0,071
0.060
0.209
0.150
0.126
SCAL Faom
0.1
0.043
0.215
0.144
0.117
0.149
0.103
0.117
0.019
0.058
0.049
0.108
Table 4: TRUE Calibration Results. ECE of each method on TRUE benchmark datasets (lower is better)
average and SCALEXx outperforms other non-
1.0
Perfectly Colibrated
SCALE models in calibration on over half of the datasets in the TRUE benchmark.
SCALEurg
SummaCa
Fraction pastives (repitive class:
0.8
Summa
UniEval
BERTScore
0.6
visual example the calibration results can be analyzed with the calibration curves in Figure 3. While most models are severely uncalibrated and underestimate the fraction of positives in Fig- ure 3, SCALE is capable of sticking extremely close to the perfectly calibrated line. The closest model SummaCCone can be seen overestimating positive examples before scores reach 0.4. We hy- pothesize that the large context window is the key to better calibration in SCALE as it includes more information. This makes the underlying NLI model less likely to be biased toward specific range of tokens which leads to extreme confidence based on certain short text. To empirically justify this, we perform further experiments the proposed ScreenEval dataset shown in Figure We can ob- serve that for chunk size < 400, the calibration score (the lower the better) is much higher than larger chunk size 500 to 1000. This shows that a larger chunk size could enable the NLI model to extract more useful information to provide appro- priate confidence when making the prediction. We also this knowledge to support our decision to use 512 tokens as our chunk size for all experiments in this paper. The enhanced calibration achieved by SCALE allows it to be more interpretable as probability, making it valuable tool for compari- son tasks.
0.4
02
0.6
1.0
Moan pradicted probability (Positive class:
Figure 3: Calibration curves the PAWS benchmark
6.2 ScreenEval
We then evaluate the performance of SCALE's chunking capabilities against other models in a long form dialogue setting using ScreenEval. We evaluate the factual inconsistency detection and relevant utterance retrieval of SCALE compared with other models and explore the unique problems posed by long form dialogue evaluation.
Accuracy and Efficiency Evaluation Results We compare the factual inconsistency detection performance of multiple models on ScreenEval in Table 5. SCALE significantly outperforms other methods across all measures. While the state-of- the-art QA model QAFactEval was able perform well on SvreenEval, SCALE still showed supe- rior performance across all metrics. Notably, even

---

SCAL Euge
7000
74
Calbration Score
0.31
72
5000
70-
4000
0.29
3000
0.28
500
1000
1500
2000
2500
3000
Figure 4: Effect of different chunk sizes on calibration performance on ScreenEval dataset.
Figure 5: Effect of different chunk sizes on SCALEtorge performance and time on ScreenEval dataset. ROC_AUC score is multiplied by 100 for read- ability.
Metric Pearson
Kendall-Tau
ROC-AUC
Time
Rougo-1 0,120
0.093
57.6
BERTScore
0.130
0.076
56.2
SummaC,
0.011
50.9
1133
UniEval
0.066
0.094
57.6
1139
long. Similarly, there is sharp decrease in model run time up until 1000 tokens. Figure 5 substan- tiates our approach to the granularity problem by illustrating that larger number of tokens in the premise leads to more effective method.
scNiLI 0.005
0.096
42.2
12688
QAFactEval 0,331
0.293
73.5
12132
SCALE 0.28
0.24
69.3
SCALE
0.391
0.322
76.1
1991
Table 5: Factual inconsistency detection results on ScreenEval. ROC_AUC multiplied 100 for read- ability.
Model
Fl-score macro
Cost
Time (s)
SCALExL
73.86
5425
SummaCcome and seNtLI, which designed to deal with long documents, have poor performance on ScreenEvall.
SCALEbage
68.74
1991
GPT-4
77.95
$102
7255
ChatGPT
56.5
$5
2544
Along with its superior performance, SCALE is able to run faster than other LLM based methods ScreenEval also shown in Table 5. For fair comparison, set the batch size for all mod- els and run with all other default settings. We do not include BERTScore due it's truncation of the document, making timing not comparable. Most notably QAFactEval, which was closest in perfor- mance to SCALE was 6 times slower than SCALEtarge in wall clock time. Even faster though was SCALE which was 17 times faster than QAFactEval while only achieving slightly worse performance across all metrics ScreenEval,, and outperforming all non-SC Onn-CCLEE methods other than QAFactEval. The SCALEBase model running at 1.1 seconds per score for long documents could realistically used in an online setting to more accurately evaluate factual inconsistency.
Table 6: Compare SCALE with ChatGPT and GPT-4 on ScreenEval.
We additionally compare SCALE with Chat- GPT and GPT-4 on ScreenEvaal in Table 6. Due to the discrete nature of GPT-4 and ChatGPT's output, we choose ideal threshold for SCALE and com- pare maero F1-Scores the ScreenEval dataset. While GPT-4 able outperform SCALEx in macro Fl F1-Score, SCALE shows to be significantly better in terms of time and cost. ChatGPT is more comparable terms of time and cost to SCALE; however, there is a significant performance drop in macro F1-Score. ChatGPT is also limited by its 4096 token length limit at the time of writing and must use truncated conversations from SvreenEval. These results help conclude that while GPT-4 has superior performance, SCALE is able pro- vide a faster, more affordable model that can be used locally an online setting to produce contin- uous scores.
Chunk size proves have large effect on the ability of SCALE's performance and time as seen in Figure SCALE sees a sharp increase in performance up until the chunk size is 1000 tokens

---

Metric Recall
Time (s)
8
Limitations
seNtL.I 0.343
35.35
SuperPal 0.406
6.13
While SCALE performs well at inconsistency de- tection there are some limitations to this approach. SCALE only uses the "Yes" and "No" logits compute it's entailment score, however only using those two logits specifically could lead to loss ac- curacy due to other information possibly flowing to similar tokens such as "yes", "no". Using logits for scoring purposes may cause loss of information to other similar logits.
SCALE 0.463
4.67
SCALExI 0.471
12,46
Table 7: Relevant source text retrieval results on ScreenEval. Time is measured in average number of seconds taken to retrieve the most relevant utterance on ScreenEval conversations.
Model Explanation Evaluation Results We now compare SCALE's BST retrieval approach with SuperPal and SeNtLI. SCALE outperforms both in terms of time and performance as shown in Table 7. S identifies the most relevant utterances correctly 47% of the time compared to 34% for seNtLI and 41% for SuperPal. SCALE's BST retrieval requires significantly fewer model calls, allowing it to pinpoint relevant utterances without having to score each one individually like the other methods. This results in higher retrieval recall for both SCAL. and SCALExL More- over, because SCALEtarge requires far less model calls it is able to provide faster results than Super- Pal or seNtLI without comprimising effectiveness. This enhanced performance shows how SCALE could be used in online setting for fast and accu-
Finally, even though SCALE is able achieve better calibration in the aggregate, still struggles with calibration on certain tasks and this can even vary by model size. Consistent calibration of scor- ing methods across NLG tasks should be goal for future methods.
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In this paper, we introduce cutting-edge NLI based factual inconsistency detection method called SCALE. We show that SCALE is an NLG task agnostic factual inconsistency detection method by achieving state-of-the-art results across four distinct NLG tasks and 1 datasets within the TRUE benchmark. We show that across NLG tasks SCALE is also superior in calibration, providing scores that are interpretable and enables accurate comparison of scores. We introduce new dataset called ScreenEvall that is the first of kind for long form dialogue factual inconsistency evalua- tion. SCALE is shown to significantly outperform all models other than GPT-4 in factual inconsis- tency detection on this dataset. However, we show cost effective
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Appendix
A.4 Human Annotation
We lable ScreenEval using workers from amazon mechanical turks with the promip shown in Fig- ure 6 and Figure 7. For each task, a worker is presented with TV script from ScreenEval along with a highlighted summary sentence. The worker is instructed to first read through the source dia- logue. Then, the worker is instructed click either Yes No button to indicate whether the high- lighted summary sentence is consistent with the source document. Each utterance in the TV script will be presented alongside a check box where if the worker chose "Yes" to the consistency question, they will be asked to select the relevant utterances that led their answer.
A Details of Construction of EvalenEval Dataset
We create summaries for ScreenEval using two summarization models, GPT-4 and Longformer, as well as human generated summaries. The models that we use for summarization are designed have a large token context window, giving them both the ability to globally attend over the long source dialogues we provide. Below, we will first explain how to leverage Longformer, GPT-4 to generate summaries, and then explain the process and the cost of human labeling.
We had human annotators label each instance, and 61 % of the time all three annotators agreed. Workers were paid 0.27 per task. We ensured the quality of annotators through a number methods. First, we filtered annotators to just those located in the US and Canada to increase the chances of high fluency in English on our reading comprehen- sion task. Additionally the workers had to have MTurk "Master" qualification, greater than 95% task approval rate, and greater than 5000 tasks approved. The dataset was labeled in batches of 30 at a time and closely monitored by the authors.
A.1 Building the Dataset
We first generated longformer summaries for each script in the test set. To keep the annotation task rea- sonable, and to filter out any rambling summaries, we filter out any TV scripts with a longformer or human summary that had more than 6 sentences only sentence. We still preserve 52 of the TV scripts by doing this, as the median number of summary sentences in longformer summary was 4 and human summary was 3. In order meet GPT-4's token limit requirements, from the remain- ing TV scripts we chose all that had less than 8, 100 tokens. Our final dataset consists of 52 TV scripts that have an average length of 6073 tokens with 624 summary sentences.
Workers were only rejected if they did not list rele- vant utterances as instructed or listed non existent utterances, and these workers were able to dispute rejection via email.
B Prompts Used for GPT
A.2 Longformer
B.1 ChatGPT/GPT-4
We use the same baseline model as in Summ- Screen to generate summaries in ScreenEval, Longformer model finetuned on SummScreen- FD's training set. This model uses a transformer based sequence to sequence architecture to globally attend over the entire dialogue. Longformer is able to take as many as I 16384 tokens as input.
[Dialogue}
Question: does the previous conversation factually imply "(Summary Sentence) "?
Answer Yes No.
A.3 GPT-4
GPT-4 is a large language model that has shown near human level performance in a wide variety of tasks, including summarization. We task GPT-4 to summarize each document in ScreenEval using the prompt "Summarize in 5 sentences or less: ". To accommodate for the roughly 8k token limit on the GPT-4 model, we specifically select documents in ScreenEval that are under 8k tokens.

---

Instructions
Summary
Gaognaptic's Mara senkea, Dascattu spacecrutt angas
planet. commanncer
accriloee rrvoived sopional eptacole also featunee with valronnuta sutu acperts, plapicca peychcokolooal chei long-Sunatinn spacee bollors miteetlyy the owno must core temra with the loes of their commander and continum their riies on sotabili human preseroe Marrs.
Source Document
"Scral" within the taxt bux untire document
Mars welcome
2: Moo Worning, dysteme offiino.
31 Robiot Foucoal: Pammission to swilch from primary to backugo.
Spwyen
bediorropubiinn
Sunyyr: We naady evarytieng Irans.
Hara Seung: Mission control, that Daaddalua, we looking red planet.
Echert
Figure 6: Prompt provided to mturk labellers
Sawyen We ready
7: Han Souig! Mission contrnl. this Doodalus, we'rr looking red planet
R: Kcheng
Sawyor we
fight evershoat?
daay.
Marta Komar:
Sum. 75.5 kilomttoos.
12: Rebert Foucsult: Mission control confimmed the rooer is 2,000 loloo over maximum pagload
Seung:
are
Decause thina that ouatinn. wvart lust insldw species hos alvonss been dhiven build bevand what we Vee miiorsted all mach. Creaaed setfaments. conatructed
Questions
summary contente tactialy consittont with the cource??
YOU ANSWERED YES"! lst the uttrancee mbore that support your conclusion below, soparated by commas. Try to MORE THAN ONE.
Type NUMBERS hore d.e. 215)
Figure Prompt provided to mturk labellers


Reviews:
[{"review_id": "PEawomexAw", "reviewer": "Reviewer_2Gpy", "summary": "", "questions": "", "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": "- On the release of code and dataset \\\nWe thank the reviewer for the kind remarks and note we will release the code and dataset publicly in the final version. This is also stated explicitly in L24 and L25 in the abstract.", "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": "- This work introduces SCALE, a reference-free, NLI based factual inconsistency detection method with a novel chunking strategy for versatility across domains and extended documents.\n- SCALE is an NLG task agnostic factual inconsistency detection method by achieving state-of-the-art results across four distinct NLG tasks and 11 datasets within the TRUE benchmark.\n- This work introduces a new dataset called ScreenEval for long form dialogue factual inconsistency evaluation. \n- SCALE is shown to significantly outperform all models other than GPT-4 in factual inconsistency detection on this dataset. It is also significantly more cost effective and faster than GPT-4 for evaluation on ScreenEval.\n", "reasons_to_reject": "- It would be better to publicly release code and dataset.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This work introduces a cutting-edge NLI based factual inconsistency detection method called SCALE.\nSCALE is an NLG task agnostic factual inconsistency detection method by achieving state-of-the-art results across four distinct NLG tasks and 11 datasets within the TRUE benchmark.", "excitement": 4, "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": {}}, {"review_id": "7zoqhLQTDD", "reviewer": "Reviewer_p7Jm", "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": "- On implementation details \n\\\nThe most basic form of chunking as described on L231-L239 is performed on a word by word basis such that the union of all the chunks is equivalent to the source document. This can also be visualized as the dashed lines in the Source Document in Figure 1. We will be revising the final version to make these more clear.      \n\n- On the reason for good calibration performance\n\\\nWe thank the reviewer for pointing this out. Our main contribution is to show SCALE could further extend the applicability of NLI-based methods. In addition to accuracy, SCALE comes with various good properties and “better calibration” is one of them. We saved our discussion of Calibration for the Experiments section under L421-L448, but we will be re-organizing the paragraph to make it more clear in the final version.   \\\nWe hypothesize that the large context window is the key to better calibration in SCALE as it includes more information. This makes the underlying NLI model less likely to be biased toward a specific range of tokens which leads to extreme confidence based on certain short text. To empirically justify this, we perform further experiments on the proposed ScreenEval dataset. We change the chunk size from 100 to 1000 and observed that for chunk size < 400, the calibration score (the lower the better) is much higher (e.g., size=100 Score = .332, size = 300, Score = .305) than larger chunks (e.g., size = 500, Score = .272, size = 1000 Score = .278). This shows that a larger chunk size could enable the NLI model to extract more useful information to provide appropriate confidence when making the prediction.  \\\nIn sum, larger chunks not only help to improve accuracy (as shown in Fig. 4), it could also improve the calibration. We will be adding these new experiments and analysis in the final version.\n\\\n- On the Figure Clarity\n\\\nWe thank the reviewer for pointing this out! We have high quality PDF formatted images that have already been swapped in for the next version of the paper.", "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 proposed SCALE based on chunking is simple and effective in efficiently handling long texts. \n2.\tThe authors demonstrate that SCALE outperforms existing methods on both standard benchmarks and the new ScreenEval dataset. This superior performance indicates that the proposed method provides an effective solution to the problem of factual inconsistency detection.\n3.\tThe paper presents ScreenEval, a novel dataset for evaluating factual inconsistency detection, specifically designed for long dialogues. This dataset can serve as a valuable resource for future research in this area, enabling a more realistic and extensive evaluation of methods.\n", "reasons_to_reject": "1.\tSome implementation details can be better described, such as the techniques of decomposing documents into chunks.\n2.\tThe paper mentions calibration scores in the introduction section but omits them in the method section. The reason why the proposed method can yield better calibration scores is not explicitly stated, which may lead to confusion or unanswered questions for the readers.\n3.\tThe figures are a bit blurry and could be improved.\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper proposes a new method called SCALE for detecting factual inconsistencies between generated text and source documents. SCALE is a task-agnostic model that uses Natural Language Inference (NLI) and a new chunking strategy to efficiently handle long texts. The paper also presents ScreenEval, a new dataset designed to test the factual inconsistency of generated summaries in comparison to actual long-form dialogues. SCALE is demonstrated to outperform existing methods and to perform effectively on various tasks and long inputs, with ScreenEval serving as a realistic testing ground for long document inconsistency detection.", "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": {}}, {"review_id": "NQst0Q57Dn", "reviewer": "Reviewer_xTxN", "summary": "", "questions": "1. how do you decide size of the chunk? Is it decided just by the context window of a model? One way to improve the technical innovation of this paper is to automatically decide the size of the chunking window since using huge chunking window can include irrelevant information.\n\n2. on line 281-285, what if the model can not fit the chunk since here you claim to break the document into two chunks.\n \n", "limitations": "", "rating": 2, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "- On Core Idea and Innovation.\n\\\nThe literature for factual consistency detection currently relies on a misguided assumption that sentence based decomposition is the superior approach to decomposing a document for testing factual consistency, when in fact it severely degrades performance of all models. SCALE identifies this flaw in the literature, proposes an accessible solution to the problem (chunking), and rigorously tests this new solution over both old (TRUE benchmark) and new (ScreenEval) datasets. Additionally, we are the first to test current factual consistency state of the art metrics on long form documents (>512 tokens) which is a vital new area given the ever growing context lengths of modern LLMs such as GPT-4. We show that all other state of the art methods severely underperform on these long documents while SCALE is able to maintain its performance by using chunking.\n\\\nThe simple and effective method of chunking significantly outperforms other complex factual consistency detection systems and is innovative. We believe that SCALE deserves to be included in the conference preceding so that many people can leverage the proposed method to build a practical system to solve this high-impact problem.\n\n- On Relevant Source Text Retrieval\n\\\nWe thank the reviewer for this observation, and note that it is possible to do NLI based relevant source text retrieval. However, previous approaches such as seNtLI, which uses retrieval as a central part of their paper, suffer from efficiency (each sentence must be individually scored to find the most relevant source) and underperformance due to out of context scoring (again, acting under the flawed assumption that the surrounding text doesn’t influence the factual consistency score). Chunking naturally enables the use of binary search trees which naturally solves both of these problems by using less model calls and selecting the relevant source in context of the entire story, keeping in theme with the paper. The results of this change can be seen in Table 7. We will make the motivation for using search trees as an algorithm more clear in the final version of the paper. \n\n- On what if the model can not fit the chunk\n\\\nWe proposed the binary search scheme (i.e., two chunks) due to simplicity and its connection to the popular binary search tree. But notice that we could generalize it into different chunk sizes. For example, we could divide the remaining tokens into three chunks or larger for the search of each block until the model fits the chunk. We could also use different chunk sizes for different layers so long as it fits in the memory. We will explain this concept in the final version of the paper.\n\n- On the details of the ScreenEval dataset \n\\\nWe had 3 human annotators label each instance, and 61% of the time all three annotators agreed. Workers were paid $0.27 per task. We ensured the quality of annotators through a number of methods. First, we filtered annotators to just those located in the US and Canada to increase the chances of high fluency in English on our reading comprehension task. Additionally the workers had to have an MTurk “Master” qualification, greater than a 95% task approval rate, and greater than 5000 tasks approved. The dataset was labeled in batches of 30 at a time and closely monitored by the authors. Workers were only rejected if they did not list relevant utterances as instructed or listed non existent utterances, and these workers were able to dispute rejection via email. Additional screenshots and details of the MTurk annotation task can be found in Appendix A.4. We will be adding all the details in the final version.\n\n- On the selection of chunk size\n\\\nWe analyzed the effect of chunk size in Section 6.2(L528 - L536). In sum, chunk sizes between 500 to 1000 gives the best overall performance and reasonably fast inference time. Thus we set the chunk size to be 512 throughout our experiments. As suggested by the reviewer, using a huge chunking window might include irrelevant information and our experiment as shown in Figure 4 justified that the performance will drop a bit if the chunk size is too large.", "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. the motivation of detecting factual inconsistency in long documents is clear and promising.\n2. the paper is well-written and easy to follow\n3. proposed a new dataset named ScreenEval which is the longest dialogue based dataset for factual inconsistency detection.\n4. achieves better performance comparing to sentence based nil.\n\n", "reasons_to_reject": "1. The proposed method lacks innovation. The core idea of scale is to break a document into large chunks different from previous methods which break a document into sentences. \n\n2. The \"interpretation via Relevant Source Text Retrieval\" is merely an adhoc straight-forward application of the NLI model and a binary search tree algorithm. \n\n3. some details are missing to support the quality of the proposed dataset. Did you have multiple annotators to label each instance? If so, what is the agreement between annotators? If not, how did you select the annotators and how did you ensure the quality of their annotations. what is the payment for the annotators?", "questions_to_authors": "1. how do you decide size of the chunk? Is it decided just by the context window of a model? One way to improve the technical innovation of this paper is to automatically decide the size of the chunking window since using huge chunking window can include irrelevant information.\n\n2. on line 281-285, what if the model can not fit the chunk since here you claim to break the document into two chunks.\n \n", "paper_topic_and_main_contributions": "Propose a NLI based model to detect factual inconsistency in long documents, namely SCALE (Source Chunking Approach for Large-scale inconsistency Evaluation). Propose a valuable datasets to evaluate the model's performance on identifying factual inconsistency in long documents. Propose to leverage chunking method to retrieve relevant sentences as supporting evidence to support the model's decision.", "excitement": 2, "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": {}}]

Author rebuttal:

Meta-review: This work proposes a method for detecting factual inconsistencies in long documents by applying Natural Language Inference (NLI) to chunks of the document. Additionally, the authors introduce a new dataset, ScreenEval, which is a dialogue-based dataset designed for factual inconsistency detection.

The method is simple, scalable, yet outperforms the baseline approaches. Furthermore, the new dataset is expected to be a valuable resource for the research community. Overall, I don't see any significant issues with the work, aside from minor points related to clarity.
Decision: Accept-Main