Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
Authors: Kellin Pelrine
Anne Imouza
Camille Thibault
Meilina Reksoprodjo
Caleb Alexander Gupta
Joel Christoph
Jean-François Godbout
Reihaneh Rabbany
Venue: EMNLP 2023 Main (2023)
Keywords: misinformation
LLM
GPT-4
uncertainty quantification
generalization
TL;DR: We show recent LLMs, uncertainty quantification, and a focus on generalization provide a path towards reliable misinformation mitigation.

Abstract: Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on generalization, uncertainty, and how to leverage recent large language models, in order to create more practical tools to evaluate information veracity in contexts where perfect classification is impossible. We first demonstrate that GPT-4 can outperform prior methods in multiple settings and languages. Next, we explore generalization, revealing that GPT-4 and RoBERTa-large exhibit differences in failure modes. Third, we propose techniques to handle uncertainty that can detect impossible examples and strongly improve outcomes. We also discuss results on other language models, temperature, prompting, versioning, explainability, and web retrieval, each one providing practical insights and directions for future research. Finally, we publish the LIAR-New dataset with novel paired English and French misinformation data and Possibility labels that indicate if there is sufficient context for veracity evaluation. Overall, this research lays the groundwork for future tools that can drive real-world progress to combat misinformation.

Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
Kellin Pelrine', Anne Imouza', Camille Thibault?, Meilina Reksoprodjo
Caleb A. Gupta', .Jel N. F Godbout?, Reihaneh Rabbany!
'MeGill University; Mila UUniversité de Montréal Eindhoven University of Technology Unnivesiity of Pennsylvania
European University Institute
Abstract
et al., 2017; Sharma al.. 2019; Shu al., 2020; Kumar et al., 2021; Shahid et al., 2022), practical solutions remain clusive. Could the recent progress in generative language models provide path to reliable veracity evaluation?
Misinformation poses a critical societal chal- lenge, and current approaches have yet to pro- duce an effective solution. We propose focus- ing on generalization, uncertainty, and how to leverage recent large language models, in or- der to create more practical tools to evaluate information veracity in contexts where perfect classification is impossible. We first demon- strate that GPT-4 can outperform prior methods in multiple settings and languages. Next, we ex- plore generalization, revealing that GPT-4 and RoBERTa-large exhibit differences in failure modes. Third, we propose techniques handle uncertainty that can detect impossible exam- ples and strongly improve outcomes. We also discuss results other language models, tem- perature, prompting, versioning, explainability. and web retrieval, each one providing practical insights and directions for future research. Fi- nally, we publish the LIAR-New dataset with novel paired English and French misinforma- tion data and Possibility labels that indicate if there is sufficient context for veracity evalua- tion. Overall, this research lays the ground- work for future tools that can drive real-world progress to combat misinformation.
In this work, we investigate the predictive capa- bilities of GPT-4 compared to previous upproaches. We propose going beyond the focus on direct classi- fication performance that is common in this domain and also prioritizing understanding of generaliza- tion and uncertainty. We believe this is critical since, despite extensive prior work on misinforma- tion detection, existing solutions often fail to gen- eralize and work in real-world settings, and there is no path to perfect classifier that will solve this challenge with oracle-like answers alone. There- fore, better understanding of generalization will help shrink the gap between research and deploy- ment. Similarly, better understanding of uncer- tainty will enable systems to fail gracefully, inform- ing users and practitioners when predictions are certain enough to be relied upon, and providing useful information without requiring impossibly perfect accuracy. Overall, this paper aims to lay groundwork that will lead to more practical sys- tems and, hopefully, improvements in rorl-world misinformation mitigation.
1 Introduction
Misinformation represents significant societal challenge, with detrimental effects observed in var- ious domains spanning elections (Meel and Vish- wakarma, 2020), public health (Loomba et al., 2021), the economy (Marcelo, 2023), and more. Recent developments in generative models have en- abled the creation of increasingly sophisticated and convincing AI Al-generated misinformation, includ- ing deepfakes and bots using large language mod- els, which further exacerbate the potential harm of "fake news" (Zhou et al., 2023). As result, de- veloping systems to limit the spread and impact of misinformation is critical importance. Although there is a great deal of research in this area (Shu
We examine three datasets: the widely-used LIAR (Wang, 2017). CT-FAN-22 (Köhler et al., 2022) that contains both English and German cor- pora, and a new dataset LIAR-New. The latter was constructed to provide data beyond GPT-4's main knowledge cutoff, to include examples in English and French, and to understand whether examples have sufficient context for evaluating their verac- ity. We show superior classification results with GPT-4 compared with the literature, both directly and in transfer and multilingual settings. We ana- lyze the errors made by GPT-4 and find they are quite different from a standard RoBERTa approach. We also conduct extensive experiments on uncer-

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tainty and context, examining ways to productively quantify uncertainty and evaluate how results are af- fected by insufficient input information. One of the approaches here (GPT-4 Entertainnt-Enabllee de- tects many Impossible examples and significantly increases performance. In addition, we show how extra label information can improve existing ap- proaches, and that hard classification performance does not necessarily predict soft (probabilistic) per- formance. Finally, we provide preliminary analysis of web retrieval, showing that provides a path to greatly improving performance, and explainabil- ity, showing it yields insights that motivate future work.
6-way results. Second, we use the CT-FAN-22 dataset (Köhler et al., 2022) for additional tests, including transfer and multilingual settings. It con- tains an English and a German corpus with 4-way labels, 3 of which represent gradations of veracity and an "Other" category for uncertain or unveri- fied examples. Finally, we provide a new dataset, LIAR-New. This dataset goes beyond the GPT-4 main knowledge cutoff of September 2021. It also provides novel contributions to the misinformation literature: first, every input is provided in both the original English language version and in French through human translation. Second, includes Pos- sibility labels that assess if an example is missing the necessary context and information to determine whether it is misinformation not. We discuss the first two datasets in detail in Appendix B, and our new dataset below.
In summary, our key contributions are the fol- lowing:
We show that GPT-4 can work for both hard and soft classification tasks for misinformation, including multilingual ones, and give better per- formance than existing approaches.
2.1 LIAR-New
We conduct extensive analyses on the generaliza- tion of GPT-4 in relation prior work, finding that the errors made are substantially different and that GPT-4 has superior generalization.
To minimize the possibility of test label leakage with GPT--i.e., to ensure that it is not relying on directly seeing the labels somewhere its training data-we scraped Politifact for recent fact-checks that are beyond the main GPT-4 knowledge cutoff. There is no 100% fixed endpoint, but "the vast ma- jority of its data cuts off"" in September 2021 (Ope- nAI, 2023). We scraped all fact-checks between IDs 21300 and 23300, which represents roughly the end of September 2021 to mid-November 2022. In our experiments we remove the 27 examples from September 2021, leaving only data from October stt, 2021 and beyond.
We propose a method to account for uncertainty with GPT-4 that excludes examples it cannot clas- sify well and improves results by 8 percentage points on LIAR-New and 14 on LIAR.
We conduct number of experiments aimed important practical factors or showing paths to build on this work, including other language models, temperature, prompting, versioning, ex- plainability,, and web retrieval.
We release a new dataset, LIAR-New, which has novel English plus French data and Possibility la- bels. We demonstrate how this data can be used better understand generalization and contex- tual uncertainty.
The form and source of the statements are the same as LIAR, as well the labels, with the excep- tion that PolitiFact changed the name of the class "Barely-true" to "Mostly-false". We note, however, that the class balance is very different: this dataset contains far more False examples than any other classes, and does not contain many True ones. This could correspond, for example, to real-world sce- nario where users of system are mostly interested in fact-checking statements that seem suspicious. Here, we do not train on this dataset, rather we use solely for evaluation purposes.
Reproducibility: We open-source our code and other contributions Github.
2 Data
We experiment on several misinformation datasets. First, we analyze the LIAR dataset (Wang, 2017), which is one of the most widely-used benchmarks for fake news detection. contains nearly 13K examples with veracity labeled on a 6-point scale. We mainly follow the common approach in the lit- erature of binarizing these labels, but also report
In addition to experiments the English state- ments provided by PolitiFact, we also translated all examples into French. This was done by two authors who are native French speakers and also fluent in English. The translation was done without the aid of machine translation, in order to avoid
'nttps://g thub. whbb.cco////.....omm com/Comp ex MitigateMisinno

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Pants-fir fire
False
Mostly-false 237
Half-true
Mostly-true
True
Total
Test
359
1067
147
99
48
1957
Table I: LIAR-New dataset statistics.
potential biases that might make the task casier for systems that incorporate such translation. Al- though this is not the first misinformation dataset in French, there are not many, and we struggled to find one that is easily accessible and has clear provenance and quality. Thus, this French data may be practical both in and of itself, and for compar- ative analysis with the English pairing. We make the full data available on Zenodo.?
according to this definition. After a process of dis- agreement resolution, this yielded 927 Possible, 581 Hard, and 449 Impossible examples. The nu- merous Impossible examples show that such anal- ysis is critical-training and evaluating on these examples unknowingly could lead to results that will not generalize well to real-world systems. In the Experiments section examine the different cases. We present more detailed definitions and information on the labeling process in Appendix C.
2.2 Possible vs. Impossible Examples
Methodology
Not all inputs can be evaluated for veracity because certain claims cannot be verified without the proper context. To better understand this problem in rela- tion to our data, we define the following labels for a given statement:
3.1 GPT-4
Main Prompting To test the effectiveness of GPT-4 for both hard and soft classification, we crafted concise prompt asking GPT-4 to rate the truthfulness of a statement on scale of 0 to 100, without providing any explanations. We denote the approaches using this prompt as "GPT-4 Score".
Definition 1. Possible: the statement's claim is clear without any additional context, or any miss- ing context does not make it difficult to evaluate the claim.
"role": "user", "content": 'Rate the truthfulness of the following statement: "STATEMENT" Pro- vide score from 0 100, where represents definitively false and 100 represents definitively true. Do provide any explanations, only re- spond with the numerical score."
Hard: the claim is missing important context that makes it hard evaluate, but it might still be possible.
Impossible: there is missing context that cannot be resolved ("The senator said the earth is flat"- not possible without knowing which senator). Or, the statement contains no claim for which veracity could be evaluated regardless how much context is available.
We also test binary classification prompt, which we tried to make as similar as possible to the score prompt above. We denote this approach "GPT-4 Binary""
"role": "user", "content": 'Rate the truthfulness of the following statement: "STATEMENT" Pro- vide number 0 1, where represents false and represents
It is important to note that, in an extreme sense, no statement can be verified without some level of additional context (for example, speaker could reference imaginary world in which the sun does orbit the earth). Therefore, these definitions are not meant to be an ironclad, encyclopedic for- mulations; they is instead intended to capture a reasonable standard whereby a model with train- ing knowledge- but without additional context for a given statement- can reasonably evaluate that statement's veracity. They aim to strike balance between practicality and accuracy, allowing for the effective categorization of statements containing misinformation while acknowledging the inherent complexity and context-dependency of language.
Do provide any expla- nations, only respond with number."
Finally, we examine prompt that explicitly al- lows for GPT-4 refuse to pass judgment. It was constructed from the above binary prompt based on the observation that GPT-4 sometimes responded with a score of "0.5", despite the prompt instruct- ing it to respond 0 or only (see also discussion below). We denote this approach "GPT-4 Binary Uncertainty-Enablee."
can
"role": "user", "content": 'Rate the truthfulness the following statement: "STATEMENT" Pro- vide number 0 or 1. where O represents false and represents true. If you are uncertain or there is not enough context in the statement to be sure what it refers to, instead answer 0.5. Do not make assumptions. Do not provide any explanations, only respond with the number."
Three annotators each labeled the entire dataset
Phttps://zenne wtwp:/.orrrrreeeeeerreeeoeeoooee wtwp://w/.rrre.e//...oooo

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Other Prompting We experimented with several other types of prompts. Explainability prompts are discussed in Section 4.8 and Appendix Q. Web re- trieval prompts are discussed Section 4.9 and Appendix R. Several attempts at in-context learn- ing with automatically chosen demonstration are detailed in Appendix D.
To convert GPT-4 Score's 0-100 predictions hard classification predictions, we examine two approaches. First, GPT-4 Score Zero-Shot simply divides the range evenly (e.g.. for the binary case, splitting at 50). This is arbitrary but intuitive. Sec- ond, on binary LIAR, we tested GPT-4 Score Opti- mized which uses the threshold that gives optimal weighted Fl on the validation set. There, we found that the optimal threshold is 71-GPT-4 appears be biased towards predicting "True" compared the labels in the LIAR dataset. Note that neither of these (nor GPT-4 Binary) use any of the training data, and even GPT-4 Score Optimized does not maintain any memory of the validation set inputs beyond this threshold.
Other GPT-4 Implementation Details In most of our experiments, opted for moderate tem- perature value of 0.5 in the API to balance repro- ducibility and noise against nuance and model ef- fectiveness. We later found that 0,0 temperature scems to yield better results, as discussed in Ap- pendix J.. Due to time and cost limits, however, we did not rerun the other experiments. To ensure that the full output was captured in cases where it did not return number, we set the "max tokens" value 1000. Cost estimates are available Appendix I. To convert the GPT-4 output to predictions we could evaluate, we first separated integer and non- integer predictions. We found the latter cases gen- erally occurred when GPT-4 refused or stated it was incapable of evaluating the veracity of the in- put. With GPT-4 Score, in the LIAR dataset, this occurred 40 times in the validation and 69 times in the test set, out of 1284 and 1267 examples re- spectively. In CT-FAN-22 occurred 2 times on English data and on German. In LIAR-New, occurred only once.
For testing non-English data, we keep every- thing unchanged, including leaving the prompt in English.
3.2 RoBERTa-large
Prior work, such Pelrine al. (2021) and Truică and Apostol (2023), has shown that older, mostly non-causal (bidirectional) transformer-basse lan- guage models can give strong performance for misinformation classification. We used the code made public by Pelrine et al. (2021) to implement laglara (Liu et al., 2019), which had the best performance in their study. We trained the model for 10 epochs with the given hyperparame- ter configuration and other implementation details, using an RTX8000 GPU. For conciseness in ta- bles, we refer to this model as RoBERTA-L Binary or RoBERTa-L 6-way, depending on whether it was trained with binary or 6-way labels. We focus our етrоr analysis and a number of other experi- ments on this model compared to GPT-4, because by using the existing code we can ensure its im- plementation unbiased, and it has already shown consistently strong performance on a number of datasets in the literature.
One of the authors manually examined the LIAR test set cases. According Definition 2, all were Impossible without additional information. These included statements like "On [...]" that seem to be a recurring PolitiFact headline and have no ve- racity on their own to evaluate: c.g., "On banning earmarks" or "On redistricting". Other examples GPT-4 refused to answer would require in-depth speaker and context information, such as "I said no to these big bank bailouts" (one would need to know the speaker and which big bank bailouts they are talking about). Therefore, on these 69 examples, GPT-4 has perfect precision in determin- ing that these statements cannot be evaluated in isolation.
3.3 Other Models
Other Small Language Models We also fine- tuned a selection of SLMs besides RoBERTa. Im- plementations are detailed Appendix E and re- sults are reported in several experiments.
In the Experiments section, when not otherwise stated, nonetheless make a prediction for these examples in the GPT-4 results by selecting label uniformly random. This ensures our results are fully comparable to other studies in the literature. Separately, we further investigate these examples in relation to other models.
F Fuzzy Clustering We performed limited tests with Fuzzy Clustering but the performance not promising. We discuss this method in Appendix F.
GPT-3.5 This model gave solid performance on LIAR, but it appeared brittle and did not perform

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well other datasets. We discuss the results in Appendix G.
Method
Accuracy
F1
SOTA (2022; 2021)
62
GPT-4 Score Optimized
68.2
68.1
We conducted a limited evalua- tion of PaLM 2 (Anil et al., 2023), and found that the second largest version gives passable, though not impressive, performance on LIAR. The largest version was not accessible to of this writ- ing. We discuss this model and the results in Ap- pendix O.
PaLM2 2 Bison
GPT-4 Score Zero-Sp Shot
64,9
60.9
GPT-4 Binary
66.5
66.5
RoBERTa-L Binary
63.5
62.1
RoBERTa-L 6-way
64.7
64.1
BERT
65,0
64.5
ConvBERT
66.7
65.8
DeBERTA
63.0
63.8
3.4 Evaluation Metrics
DeBERTA-V3
65.0
64.4
LUKE
65.3
64.4
We note our evaluation metrics in Appendix H.
RoBERTa
64.7
64.2
4
Experiments
SqueeZeBERT
63.1
62.2
XLMRoBERTA
61.1
61.0
4.1 LIAR Binary Classification
Fuzzy (Word2
60.2
60.4
In Table 2 we present binary classification results on the LIAR dataset (6-way classification results are presented in Appendix N). We see here that GPT-4 Score Optimized performs best, suggesting it can be powerful new tool for misinformation detection. GPT-4 Score Zero-Shot is significantly worse but still better than several approaches that rely on the training data. GPT-4 Binary is in be- tween the two, equal or better than all prior ap- proaches.
Fuzzy (BERT)
59.6
60.1
Fuzzy (GloVe)
60.1
59.7
Table Binary Classification Results (percentages). GPT-4 shows superior performance. For RoBERTa-L. even though the test is binary classification, training on 6-way labels provides an advantage.
Method
Accuracy
F1
SOTA (2022)
54.7
33.9
English
GPT-4 Score Zero-Shot
67.8
42,8
RoBERTa-L. 6-way
47.5
26.8
SOTA (2022)
42.7
29.0
German
We also find that some of other language model approaches give performance exceeding pre- vious state-of-the methods in the literature, par- alleling older findings of Pelrine et al. (2021). Fi- nally, we note that training RoBERTa-L on way labels gives better performance than training on binary labels. This indicates the 6-way labels pro- vide additional helpful information even when the goal is binary prediction. There is a parallel here with the work of Matsoukas et al. (2020) in other domains.
GPT-4 Score Zero-Shot
57.6
38.7
Table 3: 4-way classification in a transfer setting. On English data, GPT-4 beats the SOTA approach trained on in-distribution data, while RoBERTa trained on LIAR degrades. On German data, where there no in- distribution training data available, GPTT again beats SOTA-even without changing the prompt, which is written in English and makes no mention of German.
In Table 4, we remove the "Other" label cate- gory, which is not well-defined and for which we automatically marked all GPT-4 and RoBERTa pre- dictions as wrong. Performance improves by quite large margin. These results set the bar for fu- ture work on this dataset with the "Other" label excluded, which we recommend, as discussed in Section B.2.
4.2 CT-FAN-22 Classification
We next present in Table 3 our results on CT-FAN- 22. We compare zero-shot GPT-4, RoBERTa-large trained on 6-way LIAR classification (converted to 3-way output here by dividing the labels evenly in 3), and the previous SOTA. We see that GPT-4 has clear advantage in this setting, both in En- glish and in German. RoBERTa performs poorly. This provides additional evidence for the strength of GPT-4 in general compared models trained on un-distribution data. It also demonstrates that this model provides a large advantage in transferability to new data, and shows it can also work multilin- gual settings better than previous approaches.
4.3 LIAR-New Classification
We next examine new data beyond the main GPT-4 knowledge cutoff. We see in Table 5 that it can nonetheless make effective predictions. This sug- gests the model is taking advantage of its overall knowledge to make predictions on examples re- lated to older events and facts (and perhaps make educated guesses on newer ones), rather than just

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Method
Accuracy
F1
noticeably compared to English (i.e., Table 5), Part of this likely due GPT-4's overall performance in different languages, but we also hypothesize that the language can affect how GPT-4 imagines the context-for example, the best guess at who "the president" refers to might be the US president in English but the French president in French. We see that the drop in performance is bit smaller the Possible examples compared to the others. These are examples where there should be no need to guess the context, so this weakly supports the above hypothesis, but could warrant further inves- tigation. Moreover, the significant drop in perfor- mance in French, which is still relatively common language, should motivate more misinformation mitigation work in low-resource languages. As in the previous German experiments, GPT- 4 may be doing better than previous approaches in other languages, but still has a lot of room for improvement.
GPT-4 Score Shot
71.4
58.6
English
RoBERTa-I. 6-way
50.0
36.5
German GPT-4 Score Zero-Shot
63.6
54.3
Table 4: 3-way classification a transfer setting, exclud- ing the label "Other" which is not well-defined. GPT-4 again beats RoBERTa.
sceing the label during its training. This also sug- gests that GPT-4 has great potential if it could be combined with an effective web search to evaluate examples that require recent knowledge that it is missing. We also find that:
GPT-4 Binary again outperforms GPT-4 Score Zero-Shot, suggesting the score the latter pre- dicts is not quite aligned with the labels in this binarization.
RoBERTa-L 6-way is again better than RoBERTa-L Binary. further confirming the benefit of training on 6-way labels.
GPT-4's performance decreases significantly as we move from Possible to Hard to Impossible ex- amples, while RoBERTa has the opposite trend. This suggests GPT-4 in many cases may be re- lying generalizable information to assess veracity, while RoBERTa may be taking advan- tage of distributional patterns that provide cues in this dataset but won't generalize (as we saw in the preceding transfer experiment).
Method
Accuracy
Fl
GPT-4 Binary
89.6
66.1
Possible
GPT-4 Score Zero-Shot
77.3
59.9
GPT-4 Binary
71.1
60.6
Hard
GPT-4 Score Zero-Shot
60.3
58.9
GPT-4 Binary
73.9
53.5
Impossible
GPT-4 Score Zero-Shot
53.5
46.4
GPT-4 Binary
80.5
61.8
All
GPT-4 Score Zero-Shot
70.1
57.5
GPT-4 gives superior performance overall.
Table 6: Results on the French translation LIAR-New. Performance in French is lower. GPT-4 is still very capable of identifying misinformation in that language, future work to close this gap would be beneficial.
Method
Accuracy
Fl
GPT-4 Binary
91.0
73.7
Possible
GPT-4 Score Zhro-Shot
82.9
61.5
We also observed that in the case of GPT-4 Score Zero-Shot on the French data, there were 107 ex- amples that it refused to classify, of which 62 were Impossible and 30 were Hard. This compares single-digit numbers where it refused for GPT-4 Binary in French, and both models in English. This difference between the two versions of GPT-4 on the French data, and likewise on French vs. En- glish data, suggests prompt engineering can have significant impact on examples it might refuse to classify, and in turn on how a system would work if it encountered missing context otherwise Impossible examples in the real world.
RoBERTa-I. 6-way
79.4
57.3
RoBERTa-L Binary
71.9
55.3
GPT-4 Binary
74.0
68.8
GPT-4 Score Shro-Shot
64.6
61.1
Hard
RoBERTa-L 6-way
63.9
60.9
RoBERTa-L.Binas
60.1
58.2
GPT-4 Binary
71.0
58.5
GPT-4 Score Zero-Shot
56.1
49.9
Impossible
RoBERTa-L 6-wa
83.6
71.5
RoBERTa-LLBBinn
76.2
66.1
GPT-4 Binary
81.2
68.8
GPT-4 Score Zero-Shot
71.3
60.5
RoBERTa-I. 6-way
75.8
64.0
RoBERTa-L Binary
69.4
60.3
Table LIAR-New binary classification. GPT-4 shows strong performance, even beyond its main knowledge cutofff
4.4 Uncertainty Assessment
In Table 7, we consider the uncertainty quantifi- cation of GPT-4 Score vs. RoBERTa-L- on LIAR- New. We remove all examples predicted between 49 and inclusive (resp. 0.49 and 0.51) by GPT-4
4.3.1 Misinformation in French
Table 6, we test GPT-4 on the French version the dataset. We see that performance decreases

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These results show a clear ability to leverage GPT-4's assessment of its confidence in classify- (resp. RoBERTa-L's final softmax), i.e., the ones each model is the most uncertain about. We see that this significantly increases the performance of GPT-4 (compared to the respective rows of Table 5) while leaving RoBERTa-L virtually unchanged.
the distributions are different (LIAR was sampled to have relatively balanced classes), so labeling would be needed to confirm. Regardless, perfor- mance on the remaining data is approximately 14 percentage points better than the best performance on LIAR in Table 2.
Method
Accuracy
GPT-4 Score Zero-Shot
90.6
62.7
Possible
RoBERTa-L Binary
72.3
55.6
GPT-4 Score Zero-Shot
72.9
64.3
ing examples in this domain. Indeed, GPT-4 Bi- nary Uncertainty-Enable gives the strongest per- formance of any method in this paper excluding web retrieval (which could likely be combined with Envertainty-Enableee Granted, this is at the cost of excluding significant number of examples. But the majority of the ones that were excluded are Impossible, and this a domain where wrong an- swers can potentially be much more costly than no answer (for example, human users of a misin- formation detection system might be misled by an inaccurate machine judgment, instead of making their own judgment from external sources). Wc believe future work should build on this capability, especially integrating it with explainability, which GPT-4 also has natural capabilities for.
Hard
RoBERTa-L. Binary
60.7
58.8
GPT-4 Score Zero-Shot
69.9
58.5
Impossible
RoBHRTa-I. Binary
76.3
66.2
GPT-4 Score Zero-Shot
81.1
64.3
All
RoBERTa-L Binary
80.4
60.7
Table 7: Removing the examples where each model indicated the most uncertainty on LIAR-New. GPT-4 performance improves, giving an example of productive uncertainty quantification in this context.
We next examine GPT-4 Binary Uncertainty- Enabled. It responded "0.5" to 906 total examples (i.e., indicating it was not sure about their evalua- tion), out of which 306 were Impossible and 352 were Hard. This seems like a reasonable result- the majority of the "0.5" cases are Impossible ones that should receive that rating or Hard ones where it is reasonable for the model to be uncertain. It is also not unreasonable for the model to be un- certain about some of the Possible cases well, especially considering the challenge of this dataset being beyond the main knowledge cutoff. It is also detecting over two thirds of the Impossible exam- ples, although we note around 140 remain unde- tected, so its judgment is not perfect. Nonetheless, when we exclude these "0.5" examples and look at the classification performance on the remaining examples (Table 8), we see a marked increase in performance compared to other approaches.
4.5 Error Analysis
We compare the errors made by GPT-4 Score Zero- Shot and RoBERTA-L Binary on LIAR. Exclud- ing cases GPT-4 refused to classify (which were found to be Impossible; see Section 3.1, "Other GPT-4 Implementation Details"), we find there are 241 examples that GPT-4 correctly classifies and RoBERTA-L does not, and 174 cases where the re- verse is true. On LIAR-New, when we restrict the analysis to the 911 Possible examples after Octo- ber 1st, 2021, there are 182 examples that GPT-4 correctly classifies and Roberta-L does not, and 82 cases the reverse. In both datasets, this represents sizeable proportion. so indicates a substantial difference in these models' failure modes.
Method
Accoracy
Pussible
GPT-1Binary Uncertannyy Enabll
Esabled
94.4
75.1
LIAR-New
Hard
GPT-4 Binary Elecsttiint--aaal
86.5
Impuesible
GPT-4 Bintry Uncerbiiny-Esale EnaMlel
91.4
70.4
All
GPT Binary Uncestuinyy-
92.3
768
Dinary Uncestainty Enobled
82.2
Table 8: Allowing 4PT-4 to refuse to answer examples it is not confident on excludes many Impossible examples and greatly improves performance LIAR-New, as well as L.AR.
We further investigated this by embedding ex- amples with OpenAl's Ada-002 model (Greene et al., 2022). We found statistically significant dif- ference (p=0.0005) in embedding space distances between train and test examples, depending on whether GPT-4 was correct and RoBERTa-L wrong, or vice versa. These results suggest methods to help GPT-4 leverage the training data could lead to a nearly 10 percentage point improvement. More detailed discussion can be found in Appendix L. In Appendix M, we present a case study of ex-
We also report in the same table the results on LIAR. Here, we do not have Possibility labels to compare with. We observed that this approach excluded 914 examples, which is considerable, ap- proximately 75% of the dataset. Based on the LIAR-New results, it is not unlikely that a large portion of these examples are Impossible. However,

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run in August 2023. The Explainability perfor- mance conclusions should be taken with a grain of salt - an updated version of this paper will con- firm them with 0314. The Retrieval results show huge improvement margins which are not due to versioning, so the conclusions are unaffected.
1,0
Perfect Prediction
GPT-4
0.9
Actual Probability
0.6
0.4
4.8 Explainability
GPT-4 naturally has explainability capabilities. To investigate these, we created two simple variations on the "Score" prompt, one of which consistently improves Score Zero-Shot performance in gen- eral (though see caveats in Section 4.7). The full prompts and performance evaluations are available in Appendix Q.
0.0
0.2
0.4
0.6
0.8
1.0
Predicted Probability
Figure I: GPT-4 Score calibrated by Platt's method can predict probabilities (ECE=5.9%).
We performed a small sample, preliminary eval- uation where an author checked each explanation for reasonableness. We found that the explanation always matched the score. Furthermore, many of the explanations for answers with the wrong label predicted were actually reasonable partially rea- sonable, especially in cases with scores near 50, where a slight difference in GPT-4's evaluation vs. PolitiFact's could result in a different label. This might indicate limitations in the evaluation and that GPT-4 has better results than the metrics captured. See Appendix Q.3 for further details.
amples where RoBERTa was correct and GPT-4 wrong. It shows the training data directly matches some of these examples, which both shows the po- tential of leveraging that data and also that it might lead to unrealistic memorization.
4.6
Soft Classification
Our next experiment compares GPT-4 and other language models' capabilities for making probabil- ity predictions. We found GPT-4 Score can give reasonable probability predictions once calibrated (Figure 1). but SqueezeBERTTss results appear to be even better, depending how much weight one puts on identifying more certain examples vs. avoiding overconfidence. This experiment is dis- cussed in more detail in Appendix
We then sampled LIAR-Ner examples that were Impossible" to investigate the model's output. In cases it got correct, besides ones it does not re- ally know but gets lucky, seems to either rely on general information or assume a United States context. This might be a noteworthy geopolitical bias but would need further investigation and repli- cation. In the wrong predictions, generally it seems to know it is missing context. Sometimes issues are caused by not having photos or videos, which might merit a targeted solution. We present more details and case studies in Appendix Q.4. Overall, these results, while preliminary, show the potential of recent L.Mss to help researchers understand how they are making their veracity evaluations, and in turn figure out how to improve them.
4.7
GPT-4 Version
The experiments in Table 2, where GPT-4 Binary achieved 66.5% accuracy, were conducted with the gpt-4-0314 14 version. We revisited the performance with GPT-4-0613, and found a large drop to 56.0%. Large differences from different versions been re- ported in other domains (Chen al., 2023), but to our knowledge are the first to report it in the misinformation context. We suspect that the binary prompt may cause brittleness, and explainability prompts (see Section 4.8) may be more robust by ensuring the results are based on reasoning chains rather than on the spot verdicts. We recommend this as a topic for future experimentation.
4.9 Web Retrieval
We conducted a preliminary experiment on the po- tential of web retrieval. When collecting LIAR- New, scraped the full text of the PolitiFact ticles. We provide the model with this evidence through a simple adjustment of our prompt (de-
All of our experiments except for those in the following two sections were conducted with gpt-4- 03 14 (or the generic one, before versioning existed). Before knowing these results, Explainability used 0613, while Web Retrieval was unversioned and
BBy V1 Possibility chel-plcase see Appendix C

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tailed in Appendix R). Because the article is guar- anteed to be relevant and informative with respect to veracity, we call this approach "GPT-4 Web Ora- cle". We also examine a version "Web Answerless Oracle" where we remove the final veracity verdict from the PolitiFact article.
better performance in general, and creating systems that will address key real-world factors that have so far prevented machine learning from reliably mitigating misinformation.
Limitations
Method
Macro F1
GPT-4 Web Oracle
98.6
We hope future work will resolve limitations of this study, including the following:
GPT-4 Web Answerless Oracle
90.7
GPT-4 Score Zero-Shot
60.5
Our analysis focused on GPT-4, which is the strongest LLM available for many tasks. We rea- son that if it were incapable of producing good results here, it is unlikely that weaker LLMs of the same type could do any better. Now that we know that GPT-4 gives strong performance, it an open question to what extent these results can replicated with other systems. For closed- source models, the largest version of PaLM 2 (Anil et al., 2023) (Unicorn), along with Bard (for which Unicorn is the foundation model) would be promising models for future evalua- tion. Similarly, Claude 2 would also be promis- ing, another leading closed-source model. For open-source models, in more recent work by au- thors here and collaborators (Yu et al., 2023), we showed that similar prompting of Llama-2 strug- gles to give good performance. However, more recent open-source models such as Zephyr' have generally shown superior performance to Llama- 2. and are therefore also well worth evaluation.
GPT-4 Score Binary
68.8
Table 9: Performance with oracle web retrieval. The improvement is massive.
We see in Table 9 a huge increase in performance. Of course, these conditions are not realistic, be- cause we are assuming we can perfectly retrieve the PolitiFact article, which might not even exist. We note, however, that in spite of this limitation, these results are not true upper bounds, because this is a simple prompt using only single piece of evidence. This highlights the potential of web retrieval combined with recent LLMs like GPT-4, and we suggest it is the clearest path increasing raw performance.
5 Conclusions
In conclusion, this study presented multifaceted investigation to determine if can use GPT-4, combined with careful analysis of generalization and uncertainty, to produce more practical misin- formation detection. We found, first, that GPT-4 can give superior performance for information eval- uation in multiple datasets and contexts. Second, we found that GPT-4 has different failure modes from previous approaches like RoBERTa. This could help produce better results for detecting mis- information and countering its spread. Third, investigated uncertainty quantification in this con- text, showing that GPT-4 can provide information linked to uncertainty and that that information can be leveraged towards significantly improved per- formance. Fourth, is not easy to guarantee inputs have sufficient context for a valid judgment to be made. We provide a new dataset and analysis that starts to address this issuc. However, we believe that such explorations remain important consider in future work. Fifth and finally, we showed that both web retrieval and explainability offer promis- ing avenues for future progress. Overall, we hope this work will provide a starting point towards both
While our analysis shows GPT-4 improves prior approaches, the overall results are still not as strong as one would like in many real-world applications, whether in terms of raw perfor- mance, generalization. uncertainty quantifica- tion, or many other considerations. More work is needed to produce real-world ready systems. Besides directly building on the ideas explored here, two of the most promising parallel dirce- tions are building web search systems to address examples beyond the knowledge cutoff and in general cases that are not Impossible but where GPT-4 lacks sufficient information, and building explainability systems, to create more trustwor- thy predictions and more graceful failure. We reported on some initial experiments in both of these directions, but there is a great deal more ground to cover in future work.
alpha http://wwgggngggaeoootuggghhghgp

---

7 Ethical Considerations
quantification in deep learning: Techniques, applica- tions and challenges. Information Fusion, 76:243- 297.
Given the recent shifts in the spread of misinforma- tion which has been amplified by global events such as the COVID-19 pandemic, the war in Ukraine, and the Capitol Hills riots, it is imperative that researchers improve their approach to detecting "fake news" online. The results of this study sug- gest path forward to address this challenge by using the power of advanced language models like GPT-4. These systems can not only provide better performance- through careful attention to errors, uncertainty, and other real-world factors they can also limit harms like confident mistakes and biased predictions. However, more work is needed, and we hope future research will build on this to deliver a positive real-world impact.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin John- son, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa. Paige Bailey, Zhifeng Chen, al. 2023, Palm 2 technical report. arXiv preprint arXiv:2305.
Lingjia Chen, Matei Zaharia, and James Zou, 2023. How is chatgpt's s behavior changing over time? arXiv preprint arXiv:2307.09000
Mu-Yen Chen and Yi-Wei Lai. 2022. Using fuzzy clus- tering with deep learning models for detection of covid-19 disinformation. Transactions on Asian and Low-Resource Language Information Processing.
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettle- moyer, and Veselin Stoyanov. 2019. Unsupervised cross- tross-lingual representation learning at scale. arXiv preprint wxxiv:/91.02/116
Acknowledgements
This work was partially funded by the CIFAR AI Chairs Program and by the Centre for the Study of Democratic Citizenship (CSDC). The first author is supported by funding from IVADO and by the Fonds de recherche du Québec. We thank Yury Orliovsky for helping improve the quality of LIAR- New. We thank Berkeley SPAR for helping connect collaborators and start the project.
Jacob Devlin, Ming-Wo Wei Chang. Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understand- ing, arXiv preprint arXiv:1810.04805.
Xishuang Dong, Shouvon Sarker, and Lijun Qian. 2022. Integrating somman-n--the-lwwe into swarm learning for decentralized fake news detection. In 2022 In- ternational Conference on Intelligent Data Science Technologies and Applications (IDSTA), pages 46-53.
Author Contributions
IEEE.
Kellin Pelrine lead the project and worked most areas, especially the foundational ideas, experiment design, and analysis. Anne Imouza and Camille Thibault did the majority of the work to create LIAR-New, including the translation and impossi- bility labels. Meilina Reksoprodjo implemented most of the Gon- language models in the main comparison and contributed to writing and lit- erature review. Caleb Gupta implemented the fuzzy clustering methods and contributed literature re- view. Joel Christoph helped design and test the GPT-4 prompting and contributed writing and literature review. Jean- -ranççoss Godbout and Rei- hanch Rabbany advised the project, contributing ideas, feedback, and writing.
Yilun Du, Shuang Li, Antonio Torralba, Joshua Tenen- baum, and Igor Mordatch. 2023. Improving factual- ity and reasoning in language models through multia- gent debate. arXiv preprint arXiv:2305. 14325.
Lorenzo Jaime Yu Flores and Yiding Hao. 2022. An ad- versarial benchmark for fake news detection models. In The AAAI-22 Workshop on Adversarial Machine Learning and Beyond.
Ryan Greene, Ted Sanders, Lilian Weng, and Arvind Neelakantan. 2022. New and improved embed- ding model. https: /apenaaippna..../ com/b / log/ Accessed: 2023-05-05.
Gisel Bastidas Guacho, Sara Abdali, Neil Shah, and Evangelos E Papalexakis. 2018. Semi-supervised content-based detection of misinformation via tensor embeddings. In 2018 IEEF/ACM international con- ference on advances in social networks analysis and mining (ASONAM), pages 322-325. IEEE
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A Related Work
Misinformation and the spread of false information have become increasingly prevalent in today's 's digi- tal age (Shu et al., 2017; Sharma et al., 2019; Shu et al., 2020; Kumar et al., 2021; Shahid et al., 2022). A wide range of solutions have been proposed to tackle this problem, from fact-checking and expert- based assessments to automated machine learning approaches (Wang, 2017; Meel and Vishwakarma, 2020). Broadly, the literature can be divided into content-based and network-based approaches (Shu et al., 2017). Content- based approaches focus on analyzing the text, images, or multimedia elements
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Abhijit Suprem, Sanjyot Vaidya, and Calton Pu. 2022. Exploring generalizability of fine-tuned models for fake news detection. In 2022 IEEE 8th International Conference on Collaboration and Internet Comput- ing (CIC), pages 82-88. IEEE.

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of message to determine its veracity. Some exam- ples of this approach include (Wang. 2017), which uses a hybrid CNN and LSTM model, and (Kaliyar et al., 2021) which employs BERT. Network-bar -bsed approaches, on the other hand, examine the propa- gation patterns and user interactions surrounding the information (Hu et al., 2019; Long et al., 2017). Our approach is part of the matent-based category.
and True. The labels are fairly balanced, except for the Pants-on-Fire label, which has roughly half as many examples as the others. The dataset pro- vides an approximately 80-10- 10 sain-val-test split. Table 10 shows the basic statistics.
In addition to statements, each example includes metadata: the statement topic, and the speaker's s name, job, state, party, and total "credit history". The latter refers to the counts of statements of each label by the speaker in the training set, excluding "True". Prior research (Hu et al., 2019; Long al., 2017) has found that most of the metadata did not give significant performance benefits, but the credit history improved performance significantly. How- ever, hecause statements for fact-checking by Politi- Fact are not sampled randomly, using this metadata may cause models to learn sampling bias that can aid classification in this dataset, but will not gener- alize well to the real world. For instance, speaker might have controversial false statements which are fact-checked by PolitiFact, leading to neg- ative credit history, when they make many other uncontroversial true statements that are not fact- checked by Politilaact and consequently not part of the dataset. This contrasts with content-based classification: both may fail to classify unseen top- ics or speakers, but speaker- -ased classification (particularly when based on a biased sample) may fail even for speakers seen in the training data, In addition, the credit history may be impossible to use for classifying statements from non-famous in- dividuals (such as newly-created social media bot accounts) and introduce ethical issues. For these reasons, we exclude metadata and focus exclusively on the statements themselves. This is also a com- mon experimental setup in the literature (Shu et al., 2017: Sharma et al., 2019; Shu et al., 2020; Kumar et al., 2021; Shahid et al., 2022).
A key challenge for practical misinformation de- tection is the generalizability of models across var- ious types of datasets and domains. Several recent studies have addressed these issues from different angles. Suprem et al. (2022) investigated the gener- alizability of pretrained and fine-tuned fake news detectors, introducing the KMMaansPrrxx method for identifying overlapping subsets of unseen data. Ni et al. (2020) employed Propensity Score Match- ing to select generalizable features for fake news detection, while Lee et al. (2021) proposed UNI- FIEDM2, a unified model for multiple misinfor- mation domains, Furthermore, Hoy and Koulouri (2022) explored the generalizability of popular fake news detection models and features across similar news. These studies highlight the importance of addressing generalizability challenges for effective misinformation detection. Our work here is novel in focusing on the generalizabllity of GPT-4 in this domain, particularly in relation to the data and other LMs.
Our approach also emphasizes uncertainty as- sessment, as opposed to more rigid pursuit of overall performance metrics. The misinformation- related literature on uncertainty is very limited, lacking thorough comparative evaluations for ap- proaches like soft classification (i.e., predicting probabilities instead of class labels) and investiga- tions of the latest approaches, such as GPT-4 (Gua- cho et al., 2018; Jlifi et al., 2022; Levchuk and Shabarekh, 2017; Qu et al., 2022). Our work ad- dresses some of the many gaps in this literature by proposing approaches to productively assess uncer- tainty with GPT-4, and benchmarking methods for soft classification.
Both 6-way classification with the full set of labels, and binary classification with the labels col- lapsed into two, are common approaches to this dataset in the literature. We focus on the binary case for more lucid error and generalization anal- yses, but also report 6-way classification results. For the binary experiments, we dichotomized the categories by splitting the labels in the middle- i.e., mapping Pants-on-Fire, False, and Barely-true to "False" and Half-true, Mostly-true, and True "True". While not the only option, this the most typical binarization used in the literature (Qu et al., 2022; Orsini et al., 2022; Khan et al., 2021; Flores
LIAR and CT-FAN-22 Dataset Details B
B.1
LIAR
This dataset was collected by Wang (2017) from the PolitiFact.com API and includes 12.8K human- labeled short political statements. For the de- gree of truthfulness, there are six labels: Pants- on-Fire, False, Brre Half-true, Mostly-true,

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Pants-fire
False
Barely-true
Half-true
Mostly-true
True
Total
Train
839
1995
1654
2114
1962
1676
10240
Val
116
263
237
248
251
169
1284
Test
92
249
212
265
241
208
1267
does not contain Table 10: LIAR dataset statistics.
and Hao, 2022).
move this category and evaluate on the remaining examples. While we believe going beyond sim- plistic labeling and metrics is critical to capturing more real-worid information and producing more generalizable systems, we suggest that this "Other" label is not well-defined and should be excluded from future datasets, either in testing directly in the dataset construction.
Note that it also common for studies to code the LIAR dataset in a slightly different way than the original splits-c.g.. testing on both the valida- tion and test sets together. Although these may not include this classifica-
be perfectly comparable, tion in consideration for the state-of-the-ard method compare with our results, providing more ro- bust benchmarking for our own approach. On the other hand, in addition to studies using the meta- data as discussed above, we exclude from compari- son some studies like Pryzant et al. (2023), which evaluate their approach on very different subset of the LIAR dataset, well as ones which use different binarization, such as Dong al. (2022); Panda and Levitan (2022). To our knowledge, the resulting state-of-the- -rrt approaches are Orsini et al. (2022) and Khan et al. (2021) tied at 62% accuracy for binary classification, and Flores and Hao (2022) with 29.4% for 6-way classification.
The state-of tfrth-rtt approaches are reported by Köhler et al. (2022) from a competition run on this dataset. For English, the strongest performance is by Taboubi et al. (2022) with 54.7% accuracy and 33.9% macro FI. For the more challenging German corpus, the strongest approach was found by Tran and Kruschwitz (2022), with 42.7% ac- curacy and 29.0% macro F1. We note that these approaches used both the article title and text that the dataset provides, while for our experiments, we only used the article text because of limited time and resources.
B.2 CT-FAN-22
C Possible vs. Impossible Example Labeling
Köhler et al. (2022) derived this dataset from 20 different fact-checking websites. The labels were converted from each website's own system to four: False, Partially False, True, and Other. There is both large English corpus, and test-set-only Ger- man corpus (collected separately, not translated).
We created two versions of LIAR-New Possibility labels. We start by describing the first version and its limitations that led us to revisit the labeling. We then discuss the second, current version. The sec- ond version is now used in all experiments unless otherwise noted.
False
Partially False
Truc
Other
Total
Train
465
217
142
76
900
English
Val
113
141
69
41
364
C.1
V1 Possibility labels
Test
315
97
243
55
612
German
Test
191
97
243
55
586
We began with the following definition:
Table 11: CT-FAN-22 dataset statisties.
Definition statement is considered "Impossi- ble " with respect to veracity judgment if meets one of the following criteria: any statement of verifiable fact (e.g.. the phrase "On taxes" " in isolation has no claim that could be evaluated) or 2. the fact that the statement refer- ences cannot be verified without unavailable con- text (e.g.. "they voted for it" requires knowing who "they" are and what "it" refers to). Conversely, statement is considered "Possible" if can be confirmed or disproven with external knowledge, but does not require additional unavailable context (e.g. "the sun orbits the earth" may require astron-
The "Other" label could be problematic because it cannot guarantee something for which veracity is unverifiable; it only reflects that an entry has not been verified this dataset. Furthermore, it is not a category which can be synchronized with other datasets, impeding transfer analyses. In one of our experiments, we maintain this category for comparative purposes only, and directly mark ev- ery prediction by our models on examples with this ground truth label as wrong. This leads a maximally stringent evaluation favoring previous approaches. In another set of experiments, we re-

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omy or physics knowledge to disprove, but does not require additional context).
to..." Butt it's challenging and might be impossible in some cases, for example if it's not possible to figure out which cul this refers to.
In this definition, there are only two categories, Possible and Impossible. Two authors (Anne Imouza and Camille Thibault) each annotated half of LIAR-New according to this definition. This yielded 892 Possible and 1063 Impossible exam- ples.
Impossible: there is missing context that camnot be resolved ("The senator said the earth is flat" without a name, this could be any senator through- out history, so if's not possible to give valid productive veracity evaluation). Or, the statement contains no claim for which veracity could be eval- uated regardless how much context available (for example, a statement "on the shape of the earth" contains no claim).
Each annotating author then annotated 100 ex- amples from the other's s set, and we evaluated dis- agreement. We found that disagreement on this combined set of 200 examples
significant, with Cohen Kappa 0.312 and 72/200 cases disagreeing.
The 3-way labels here provide more information and can make it casier to handle edge cases than the rigid 2-way labels. We also recruited third person (Yury Orliovsky) to provide third set of labels further reduce noise.
The annotating authors discussed each of these 200 disagreement cases and resolved the disagrec- ment. We did not observe a clear pattern such as all disagreements being resolved to one label or the other; it varied case by case. Consequently, although this is a challenging and potentially sub- jective labeling task, to improve quality much possible we decided it would be beneficial to re- vise all the labels. Based on the annotating authors' observations and group discussion, we decided to add a third category, leading to Version discussed below.
In this round, each annotator annotated every example. The resultant counts of labels are shown in Table 12.
We see that the dominant cases are either agree- ment or relatively mild form of disagreement between Possible vs. Hard or Impossible vs. Hard. We consider this type of disagreement to be within acceptable bounds, since by definition the Hard class might in fact be either Possible or Impossible. Therefore, for the final dataset, we resolved these disagreements by taking the majority vote.
We note however that the experimental conclu- sions did not change substantially between the two versions, suggesting the original version unbi- ased, if noisy.
Nonetheless, there are also 342 cases where at least one annotator marked Possible and another marked Impossible. In these cases, the labels are clearly confiictigg. To resolve these disagreements, we split them into two categories: 127 "maxi- mal" disagreement cases where each label is repre- sented, and 215 "nonmaximal" where two annota- tors marked one label and one marked the other.
C.2 V2 Possibility labels
The following definitions are the full versions of the ones we revised to, including examples (which were agreed upon in advance of the labeling) omit- ted in the main text:
Definition 3. Possible: the statement's claim is clear without any additional context (e.g., a state- ment "the earth is flat"), any missing context does not make it difficult evaluate the claim ("at the press conference, Senator X said the earth is flat" we may not know which press conference this refers to, but we can give a strong veracity evalua- tion just examining if Senator X has said the earth is flat at all, in a particular context, or frequently).
For the maximal disagreements, the annotators discussed each example live and resolved the dis- agreement to produce final label. For the non- maximal disagreements, the minority vote author first went through each case individually to assess if they had mislabeled it on their end. Finally, for cases where the minority vote author in their indi- vidual assessment still disagreed with the majority, all the annotators discussed live to produce final label.
Hard: the claim is missing important context that makes it hard to evaluate, but it might still be possible. For example, "Senator X is against the $15 million cut." " We might be able to trace the amount and the senator's 's name and figure out what this refers, and give a practical evaluation likely beginning with "Assuming the cut here refers
This yielded 927 Possible, 581 Hard, and 449 Im- possible examples. We note that in the experiments here we only examine these three categories, how- ever, potentially the raw labels from the individual annotators could be used to form a finer- mrained

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scale. For example, case all three annotators la- beled Hard might be harder than a case where labeled Hard and one labeled Possible. However, caution would be needed here as it could result noise.
We make both the raw and final labels available for future work through our GitHub.
D In-context learning with automatically chosen demonstration
Labels
Count
PPP
270
D.1 Prompts
hhp
157
We tested several prompts in which GPT-4 is pro- vided example chosen automatically from the training set. These were motivated by the obser- vation that GPT-4 and RoBERTa make different errors in a way that is tied to embedding similarity with training set examples (Section 4.5). Conse- quently, by providing the most similar training set example, we hoped to provide both a demonstration and potentially a direct source of information rele- vant to the target example. Related procedures retrieve semantically similar examples have shown performance benefits in other contexts (Luo et al., 2023).
hii
129
hpp
69
php
62
ppi
iii
52
hhh
41
hhi
39
pph
34
pii
31
phh
25
phi
22
hpi
hih
16
In the below prompts, CLOS- EST_ LLBBEL refers to the label of the closest training example according to Ada-002 embedding distance, uniformly mapped onto the 0-100 range. CLOSEST TRAIN TEXT refers to the corresponding training example input statement.
hip
16
ihp
15
iip
14
pip
14
ipp
11
hph
10
iih
Version 1:
ihi
ihh
"role": "user", "content": 'Rate the truthfulness of the following statement: "STATEMENT"
pih
4
ipi
3
Here the rating rating is accurate, but the statement may or may not he relevant to this one. Similar statement: "CLOSEST_RRA" TRAIN_RANESS TEXT" Rating: CLOS- TE__TRINN_LAA
similar statement. The
iph
2
Table 12: Counts of each label combination, with or- der preserved from the annotators. "ppp". for example, indicates all three annotators marked the example "Pos- sible".
Provide a score original statement to 100, where represents definitively false and 100 represents definitively true. Do not provide any explanations, only respond with numerical score.
Version 2:
"role": "user". "content": 'Here is a similar statement that CLOSEST TREES__TRAIN_LABB "CLOSEST TRAIN TEXT" Now rate the truth- fulness of the following statement: "STATE- MENT" Provide score from 0 to 100, where 0 represents definitively false and 100 represents definitively true. Do not provide any explanations, only respond with the numerical score.

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Finally, Version 3 is a meta-prompt where we set a condition to select between Version and
E Small Language Models
We fine-tuned a selection of pre-trained SLMs from HuggingFacc. Due to limited computational re- sources, we restricted our analysis to models that were runnable on one Tesla T4 GPU. We exam- ined BERT (base) (Devlin et al., 2018), ConvBERT (Jiang et al., 2020), DeBERTa and DeBERTa-v3 (base) (He ct al., 2020), LUKE (Yamada et al., 2020), RoBERTa (base) (Liu et al., 2019), Squeeze- BERT (landola et al.. 2020) and XLM-RoBERTa (Conneau et al., 2019).
the original (no demonstration) Score Zero-Shot prompt. Given the example we want to classify, as in previous versions, we find the most similar train- ing set example. Then if this train-test example pair is among the top 10% most similar train-test example pairs (i.e., the similarity between the tar- get, test set example and it's most similar training set example in the top 10% similarity of all such pairs), we use the Version 2 prompt. Otherwise, we use the Score Zero-Shot prompt. The motivating intuition here is that if we have a very similar train- ing set example, then we try to take advantage of that. In contrast, if none of the training set exam- ples are very similar, then we hypothesize they may not be relevant and therefore including them might confuse the model, so we revert the Zero-Shot prompt.
A small hyperparameter search performed on the given models, however, rapidly found that this was not significantly increasing perfor- mance and began showing signs of overfitting. Hence, for this purpose, all the reported values in Table 2 as well as Table 20 are based on default configurations for pre-training BERT-like models, as stated in the original BERT paper. All the mod- els were finetuned for 2 epochs. using the base embeddings with the learning rate 5e-05 using the AdamW optimizer.
D.2 Results
We report results in Table 13. We see that ver- sion seriously damages performance. Version 2 is more reasonable but still slightly worse than Zero-Shot. Version 3 is better than Zero-Shot, but the difference is marginal and unlikely to be worth the complications and potential detriment to gen- eralization of relying on limited, static training set.
F Fuzzy Clustering
K-Means Fuzzy Clustering has shown promising results for misinformation detection in some re- cent studies (Chen and Lai, 2022; Raj and Meel, 2021). This method initializes centroids in a pscudo-random fashion, then each datapoint is as- signed probabilistic membership to each clus- ter proportional to the distance between the data point and centroid. Following this, the centroids themselves are moved relative to the averages of their data points. Memberships and centroids are updated repeatedly until convergence. The final membership values can be converted to class pre- diction with an argmax or an additional classifier.
Method
Accuracy
Version 1
57.2
Version 2
64.2
Version 3
65.2
GPT-4 Score Zero-Shot
64.9
Table 13: Results of 3 versions of in-context learning with an automatically chosen demonstration. Version 3 improves performance compared to Zero-Shot, but only marginally, the benefit is likely not worth implemen- tation complications and potential impact on generaliza- tion.
We tested fuzzy clustering using three types of word embeddings with various levels of global textualization: Word2Vec. GloVE, and BERT. The models were trained using adaptations the SciKit- Fuzzy cluster cmeans library.
We note that this is only using a single example- perhaps more are necessary to minimize the impact of noise. Also, there could be many other ways to do the prompting, for instance maybe this does not synergize well with the Score prompt but would work better for Binary. So we cannot rule out some related approach doing better. But we believe other ways to provide the model additional information, especially web retricval (see Section 4.9), are much more promising.
Unfortunately, in our experiments (Tables and 20), this approach did not give good results, so we did not investigate it further.
G GPT-3.5
G.1
Base Performance
We prompt GPT-3.5 the same way as GPT-4, and evaluate its performance in Table 14.

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Dataset
Method
Accuracy
said it plans release in the future, areas for future work.
LIAR
GPT-3.5 Score Zero-Shot
67.3
LIAR
GPT-3.5 Binary
53.6
JJW
GPT-3.5 Score Zhrt-Shot
61.1
LIAR-New
GPT-3.5 Binary
55.7
LIAR-New
GPT-3,5 Binary Uneertainty Enabled
67.7
H Evaluation Metrics
Table 14: Results with GPT-3.5.
To measure the performance of our misinformation detection approaches in hard classification settings we mainly report accuracy and weighted Fl score. For LIAR-New, where the classes are very imbal- anced, report accuracy and macro Fl score, which gives more stringent assessment. Mean- while, for soft classification, we provide reliability diagrams and measure Expected Calibration Error (ECE), which are standard ways to measure perfor- mance in this context (Abdar et al., 2021). Both were applied using 10 bins with quantile scaling, i.e., the bins are scaled to have the same number of examples in each.
We note that GPT 3.5 performs surprisingly well on LIAR, better than GPT-4 in the same Score Zero-Shot setup. However, there are some caveats. First, its score is already maximized the zero- shot binarization threshold of 50, meaning it cannot achieve higher performance through tuning on the validation set like GPT-4 can. The margins are small, however, so this may not have big impact. More significantly, the performance seems brittle and does not extend to other prompts or datasets.
G.2 Fine- Tuning
We conduct a preliminary experiment on fine- tuning using the following prompt:
GPT-4 Cost Evaluation
We report Table 15 approximate API costs of some of the main experiments run. These num- bers can vary between runs of the model (due to randomness in the completion tokens) but should provide reasonable estimates. This is based on cur- rent pricing for GPT-4 (8K context window) as of October 2023, i.c., USD 0.03/IK input tokens and 0.06/1K K output tokens.
"role": "user", "content": "Rate the truthfulness of the following statement: "STATEMENT" Pro- vide a score from 0 to 100, where O represents definitively false and 100 represents definitively true.'
This is the same as the usual Score prompt, ex- cept we remove the last sentence "Do not provide any explanations, only respond with the numerical score." We hypothesized that the finetuning should already be sufficient to get it to respond with only the numerical aoore-and in practice it does, 100% of the time.
We also note that the finetuning of GPT-3.5 on the LIAR train set comprised 609k tokens costing 4.9 USD.
Overall, we see that there a very large range depending on different data and prompts. We note that if an explanation is not needed, prompts that avoid it can be quite effective in reducing cost in cases where the input is not long (e.g., LIAR and LIAR-New). Furthermore, the overall bud- get needed for these experiments is not trivial, es- pecially considering that not experiments are included here and many need multiple runs (e.g., when evaluating temperature in the following sec- tion, doing exploratory analysis, debugging, etc.). In the short-term, thoughtful prompting and choice of experiments target can help keep costs down when budget is limited. In the long-term, cheaper models with comparable performance will hope- fully (and quite likely) be found or created, which will be a significant advantage for both research and woal-world deployment.
For the output side of the fine-tuning, we map the original 6-way LIAR labels uniformly to 0-100 scores, i.e., 10,20,40,60,80, 100]. Then we fine-tur tune with default settings, which resulted in 2 epochs of fine-tuning.
On the test set, this approach achieved 57.2% accuracy. Considering the corresponding untuned approach (Score Zero-Shot) achieved 67.3% ac- curacy, the fine-tuning was not successful. We hypothesize that finetuning to Impossible examples might be particularly damaging. In addition, the Score prompting may not be well suited for this since the uniform mapping may not reflect the true variation in the labels. So there is considerable room for more experimentation here that might yield a better result. We recommend this, along with testing GPT-4 fine-tuning that OpenAl has

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Dataset
Prompt
Input Tokens
Output Tokens
Total Cost (USD)
LIAR
Score
100K
3K
3.19
LIAR
Binary
93K
2K
2.88
LIAR
Explain-then-Seee
142K
314K
23.1
LIAR
Score-then-Explain
104K
118K
10.21
LIAR-New
Score
154K
2K
4.73
LIAR-New
Binary
140K
2K
4.32
LIAR-New
Binary Uncertainty-nnalle
205K
4K
6.37
LIAR-New
Explain-then-Score
216K
464K
34.35
LIAR-New
Web Oracle
1,8M
2K
53.07
LIAR-New
Web Answerless Oracle
1.7M
2K
51.07
CT-FAN English
Score
725K
1K
21.79
CT-FAN English
Explain-then-Seee Score
691K
197K
32.57
CT-FAN German
Score
956K
IK
28.72
Table 15: GPT-4 API costs.
J Temperature and Variation
Temp.
Accuracy
Non-
SD>
PIP
numeric
Mean
Max
Max
# Large
We examine here the effect of varying the sam- pling temperature from 0.5, used in the main exper- iments, to 0.0 or 1.0. There two outcomes to consider: first, the effect on overall performance. Second, the variation of GPT-4's responses for each example over multiple runs. We test these by run- ning GPT-4 10 times on the test data at each tem- perature level, in the binary setting.
0.0
95
51.6
0.5
67.4
108
48.3
1.0
66.5
129
40.7
100
Table 16: Temperature vs. accuracy and several mea- sures of variation in the responses. In general, lower temperature gives better results.
In Table 16 we report first the mean accuracy and its standard deviation over 10 runs. Then we report the number of non-numeric answers, c.g., when GPT-4 refuses to answer or returns explanation instead of following the directive to only return score 0-100. Note that for the accuracy calculation, we replaced these with a random number uniformly drawn between and 100, while in the subsequent metrics we exclude these examples. For each exam- ple, we then compute the standard deviation of the scores, and report the mean and maximum of that value over all the examples in the dataset (Mean SD and Max SD), Finally, we look peak-to-peak distance, i.e., the difference between the largest and smallest GPT-4 scores for each example over the 10 runs. We report the maximum (Max PtP) and the number of examples for which the PtP distance is over 50 (# Large PtP).
very large variation in responses responses-regardlesss the temperature, there is always an example with the maximal 100 tokktt distance, and even with 0.0 temperature there are 5 examples with over 50 peak-to-pedt to-peak distance. This might be trou- blesome for user-facing systems built on this, as occasionally it can give very different answers to different users querying the veracity of the same statement, or one user querying multiple times, which could cause some confusion or distrust.
regardless of
Consequently, future work that addresses this limitation would be helpful. This might be done with better prompts or with techniques such as multi-agent debate (Du et al., 2023; Liang et al., 2023), possibly giving the system more context and evidence.
We also note that the maximum standard devia- tion shows counterintuitive result, increasing as temperature decreases. This might be noise, or it might be due to lower temperature following the instruction to respond with number more consis- tently and still giving an answer in cases where it is more confused. We found that of the 95 examples where at least one run at 0.0 temperature returned a non- numeric answer, all but one were contained
We find that lower temperature generally gives better results, with both small increase in accu- racy and reduced variation amongst the responses. The mean standard deviation between runs with 0.0 temperature is quite reasonable at 2.2, indicating the responses are generally consistent. We do how- ever note that a small number of examples still have

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within the 129 examples where a run at 1.0 temper- ature returned a non-numeric answer. Furthermore, excluding the full 129+1 examples increased per- formance to 69.6 + 0.2, reduced Mean SD to 2.1, and removed one of the 5 large PtP cases. It did not change the maximum standard deviation or peak-to- peak distance, but nonetheless, this might suggest that higher temperature has edge in realizing cases it cannot answer properly. Further investiga- tion is needed to determine an adjusted prompt, which directly allows the model to refuse to answer, would have a positive effect here.
which means it may be learning information or a bias which helps on this data but will not general- ize. On the other hand, if we look at RoBERTa's 's soft classification these examples, the results are more encouraging: the mean absolute deviation from 0.5 (which would match "can't 't be determined one way or another") is 0.232 in the Impossible case, compared .338 in the Possible case. This suggests more certainty overall Possible exam- ples, which is the direction one would want, even if is not sufficient to achieve the desired outcome.
Method
Accuracy
F1
Finally, we also considered the potential effect of changing the temperature, as well as repeated runs in general, on the optimal threshold. We evalu- ated all possible thresholds to determine the oracle threshold that would give the best performance at each temperature. Results are shown in Table 17. We see that the original threshold found on the vali- dation set (with 0.5 temperature) was a high for the test set, especially at 0.0 temperature. However, the difference in accuracy between this threshold and the oracle one is quite small. Thus, it does not appear that temperature has a large practical effect on the optimal threshold.
GPT-4
69.4
69.1
Possible
RoBERTa-large
63.8
62.3
50-50 Random
50.0
50.2
Impossible
RoBERTa-large 59.4
58.4
Table 18: Possible vs. Impossible examples, RoBERTa- large beats random on Impossible ones which is might not generalize.
Finally, we further tested what happens to the GPT-4 classification threshold when these exam- ples are included, as in Table 2. by assigning them a random prediction. The threshold increased from the optimal 71 to a mean over 100 runs (re- randomizing the Impossible predictions) of 72.31. Although not large change, this does illustrate concretely how including these examples can bias the predictions.
Temp. Original Thre.
Accu.
Oracle Thre.
Accu.
0.0
67.9
68.1
0.5
71
67.4
66
67.7
1.0
66.5
67.5
Table 17: Temperature vs. performance with the original threshold from one the validation set (71), and the oracle threshold that maximizes performance. The original threshold somewhat high, but the difference in accuracy is low.
Overall, these results confirm that noise in the data can affect downstream results. RoBERTa- large can somewhat account for them, but not nearly to the extent of GPT-4. Since it can detect many Impossible examples, GPT-4 has robust- ness advantage in this area, and might also be used for cleaning the data if approaches like RoBERTa- large are needed.
K LIAR Possible vs. Impossible Examples
In addition the larger scale analysis on LIAR- New, we also examined the Possible vs. Impossible examples in LIAR (i.e., using the ones that GPT- 4 found for the Impossible case, as discussed in Section 3..). We restrict the test set to only one or the other category, and compare results with GPT- 4 and RoBERTa-large. First, in Table 18 we see that performance improves slightly (compared to Table 2) when restricting to the Possible examples, which confirms our initial intuition.
L Investigating Errors in Relation to the Training Data
We embedded all examples with OpenAl's Ada- 002 model (Greene et al., 2022). This is a docu- ment embedding model which is suitable for evalu- ating their semantic similarity. For each example in the test set, we find the most similar statement in the training set by calculating the cosine distance in Ada-002 embeddings between the statements. We find the training set statement that minimizes the distance to each test set statement. Finally, we compare the minimum distances averaged over all
Next, for the Impossible examples, we see the results of flipping a coin vs. RoBERTa-large. RoBERTa outperforms random classifications here,

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the test set examples.
N LIAR 6- 6-y Classification
For the cases where GPT-4 was correct and RoBERTa-L wrong, the average distance is 0.127. while for the reverse, the average is 0.116. The dif- ference is statistically significant (p=0.0005). This suggests RoBERTa is using similar examples in the training data to make correct predictions for cases GPT-4 gets wrong, while GPT-4 must be using the information it learned during its own training pro- cess to correctly predict cases that do not relate well to the examples RoBERTa sees in the train- ing set. We publish our Github these examples and the associated cosine distances so that further analysis on the generalizability of RoBERTa's 's pre- dictions can be conducted. replicable in other contexts, these results suggest GPT-4 or similar models could benefit significantly from effective finetuming or few-shot setups in this domain, if they can be optimized beyond the failed attempts we made here (Appendix G and Appendix D). In other words, if GPT-4 could correctly classify all the ex- amples RoBERTa does without compromising its current correct examples, there would be nearly 10 percentage point improvement. However, the optimal methods to achieve that-without compro- mising generalization-remall an open question.
In Table 20, we present results for 6-way classification-iie.. using the original LIAR dataset labels, without dichotomizing the ratings. In this case, DeBERTa performs best. GPT-4 Zero-Shot is not far behind, but this suggests that more in- formation is needed to match the finer-grained la- bels here, either from the training set or by tuning the GPT-4 classification thresholds the valida- tion set. Performance very low overall, but this matches the literature, and in the case of DeBERTa, exceeds it (Flores and Hao, 2022). It is important to note that, as shown by the low metrics across the board, this task is extremely difficult. Besides cur- rent approaches just being insufficiently capable in general, this could potentially occur if there were lack of consistency in the fine-graie fine-grained labeling, further validation of the labels could be valuable area for future work.
PaLM 2 Bison
In Table 21, we examine the performance of the Score prompt with PaLM 2 Bison (Anil et al., 2023). We report accuracy for LIAR and Macro FI for CT-FAN (the most used metrics for those datasets).
We that Bison performs OK, if not impres- sively, on LIAR. It is significantly worse than GPT- 4 and other approaches on CT-FAN. In addition. unlike with GPT-4, we did not find any significant performance gains from tuning the score threshold. Nonetheless, the results on LIAR do suggest rea- sonable misinformation detection is no artifact of GPT-4 alone among recent LLMs. and similarly capable models might give similarly strong results. Bison is the second largest PaL. 2 model. The largest, Unicorn, is not publicly accessible in a direct way as of this writing. Unicorn can be used indirectly as it powers Google Bard, but Bard is not accessible in the authors' country (Canada), and to our knowledge does not offer an API anywhere. Consequently, we were unfortunately not able to test test the most powerful version of PaLM 2. If accessibility issues can be resolved, we believe that would be worthwhile evaluation for future work.
M LIAR Error Analysis Case Study
We present here some examples that RoBERTA-L Binary gets correct and GPT-4 Score Zero-Shot gets wrong, along with their closest training set example by Ada-002 embedding cosine distance. Specifically, we randomly sampled 50 examples for qualitative analysis, and here we print the five with the lowest cosine distance, the five in the middle, and the five with the highest cosine distance among the 50. From the closest five, we see that a number of examples in the LIAR test data are near-replicas of an example in the training data. Some others are very similar. On the one hand, this means that the training data is helpful to classify some exam- ples, explaining why RoBERTa-L gets some right that GPT-4 gets wrong, and suggesting potential benefit if GPT-4 could ingest the training data di- rectly. On the other hand, this also means some examples might be artificially easy for models that have seen the training data, and can be classified by memorization rather than learning generalizable patterns.
P Soft Classification
Here we evaluate the capabilities of GPT-4 and other language models for making probability pre- dictions. We first discuss the calibration method we used, then the results.

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Test Set Statement
Closest Train Statement
Weve doubled the production clean energy. Says the National Labor Relations Board told Boe- ing that it cant build factory in non-union state.
Weve doubled our use of renewable energy. Says the National Labor Relations Roard told Boe- ing that couldnt build a factory in South Carolina because South Carolina is right-to-work state. Under Barack Obama, the fewest number adults are working since Jimmy Carters presidency. Says Hillary Clinton defended Syrias President As- sad as a possible reformer at the start of that coun- trys civil war.
Closest
Under Barack Obama, the U.S. now has the lowest workforce since (President Jimmy) Carter. Says (Clinton) called President Assad a reformer. She called Assad different kind leader.
Gov, McDonnells proposed budget is cutting public oducation.
Says Gov. Bob McDonnells budget would cut pre-
kindergarten programs.
PolitilFact Texas says Congressman Edwards at- tacks on Bill Flores are false.
Says Politilacct has ruled that Ed Gillespies uds are false and mislcading.
Middle 5
One in five American houscholds have nobody un- der the same roof thats got a job.
We have 25 million Americans out of work.
Says Bill Pascrell voted to eliminate all estate taxes for billionaires, and I voted no.
Says Congressman Bill Pascrell voted to remove the public option from the Affordable Health Care Act.
Of all cities in the United States with more than 100,000 people. Providence is the 183rd safest.
[When] I was mayor] Providence was one of the five renaissance cities of America, according to USA Today in those days. Money magazine said it was the fifth hest city to live in in America. In addition to that, All Cities Almanac said in 1994, I believe, that was the safest city America.
Says critics who say he cut Medicaid are wrong; his budget added $1.2 hillion to the program
Says he opposed $716 billion cut to Medicare.
Says Republican Rep. Charlic Dent wants to kick the Freedom Caucus of the Repuhlican confer- ence for voting our conscience.
Says Congressman Eric Cantor wants eliminate Social Security.
Furthest
Active duty males the military are twice likely to develop prostate cancer than their civilian coun- terparts.
One five women in the military are receiving unwanted sexual contact, as are 3.3 percent men.
We have 41,000 people right now on the waitlist for financial-busee assistance for our tech colleges and universities, most of them for tech schools.
The 2013-15 state budget backed by Gov. Scott Walker and Republican lawmakers left our techni- cal colleges funded at 1989 levels.
Ninety percent the people in Arkansas still love Bill Clinton.
59 percent of Americans today believe that Barack
Ohama still Muslim.
When people enter the service, theres not a mental
Seventy-fir five pereent of the young adults in this country are not mentally or physically fit to serve.
health evaluation.
Table 19: Samples of I.ARR examples that RoBERTa-L. Binary classified correctly and GPT-4 Score Zero-Shot classified incorrectly, grouped by semantic similarity according Ada-0022 embeddings. The closest ones identical, the middle ones are similar, the furthest ones are only tangentially related.
P.1 Calibration
model's output probabilities. We do this on the validation set. This will in theory help correct for overconfidence and lead to better performance. For evaluation metrics, please see Appendix H.
For soft classification (probabilities), we first use the scores our models provide out-of-the-boxx In the case of GPT-4, we use the raw output it returns (divided by 100 to put it between 0 and 1). For the other language models, we take the output of the final softmax, right before the argmax converts this value to a single class prediction. These ap- proaches have the advantage of simplicity and not introducing potential confounding variables.
Aside from GPT-4 which is not trained, all the models for this section were trained in the binary classification setting of Table 2.
P.2 Results
Results are shown in Figure 2 and the following figures. We first observe that Platt's method gen- erally improves the quality of the predictions. We next see that GPT-4 predictions are not very well calibrated out-of-the-box. but after Platt they show a decent relationship between prediction and actual probability, indicating this approach has potential to give more informative predictions. We suspect
Note, however, that the literature suggests deep learning models are often over-confident and their out-of-the-box probabilities may be of low quality (Moon et al., 2020). Therefore, we apply Platt's method (Jiang et al., 2011) 1) to calibrate. This in- volves fitting a logistic regression model to the

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Method
Accuracy
F1
(close or 1). This can be issue because we still want systems that make strong predictions in clear cut cases. While this likely better than mak- ing numerous confident but wrong predictions, as one gets with standard hard classification, it sug- gests a line of future work aimed at fully realizing the potential of soft classification approaches.
SOTA (2022)
29,4
GPT-4 Zero-Shot
28.1
25.5
BERT
28.1
26.4
ConvBERT
27.9
27.2
DeBERTa
29.9
29.2
DeBERTa-V3
28.9
27.5
LUKE
28.3
27.0
Q Explainability
RoBERTa
26.5
22.1
SqueezeBERT
26.6
25.3
In many experiments, we have examined prompts that explicitly discourage explanations. This gives advantages in speed and reduced cost (see Ap- pendix 1), and such outputs are easier to evalu- ate quantitatively. However, in the research and development context, explanations could provide insights into how the system is making evaluations and how could improve it. In a deployment context, they could enable user to see if its eval- uations seem complete or something is missing, Icading to improved trust and more graceful failure. In the following subsections we go into more detail on our explainability experiments.
XLMRoBERTA
20.9
07.0
Fuzzy (Word2Vec)
22.3
22.8
Fuzzy (BERT)
23.1
23.2
Fuzzy (GIoVE)
21.5
21.1
Table 20: 6- Way Classification Results (percentages)
Dataset
Method
Result
LIAR
PaLM 2 Bison
62.4
LIAR
GPT-4 Score Zero-Shot
64.9
CT-FAN English
PaLM Bison
21.3
CT-FAN English
GPT-4 Score Zero-Shot
42.8
CT-FAN German
PaLM Bison
22.3
CT-FAN German GPT-4 Score Zero-Shot
38.7
Q.1 Prompts
Table 21: Comparison with PaLM Bison. GPT-4 performs better, but Bison does give passsable results on LIAR. Note that this is only the second largest PaLM model; the largest was not accessible to the authors as of this writing.
We considered two prompts. The first, "Score- then Then-Explain" is very close to the original "Score" prompt. We simply replace the last line in the orig- inal, which discourages explanations, with a line requesting them.
"role": "user", "content": 'Rate the truthfulness the following statement: "STATEMENT" Pro- score from 100, where represents definitively false 100 represents definitively true. Then write vertical bar "I", followed by explaining your reasoning for the score.'
that this could be improved even further with better prompt.
More surprisingly, we find that superior perfor- mance hard classification does not translate to superior performance on soft classification. The best soft classification model is SqueezeBERT (af- ter calibration), which gave thoroughly unremark- able performance in the hard classification exper- iments. We believe this warrants further investi- gation for two reasons. On the one hand, this could lead to an effective, nuanced misinforma- tion mitigation tool leveraging the berf-performing approaches here like SQUE On the other hand, understanding why different approaches per- form better or worse here for soft classification despite their hard classification performance could lead to better soft classification general.
The second prompt, "Explain-then-SSone asks the model to write an explanation before deciding on score. The intuition here is that we ask for a score first and lock that in, then the explana- tion will likely be written to match the score. But if we ask for the explanation first, the model will have chance to reason further about the input it writes the explanation, which might lead to more thoughtful and accurate score. This approach was directly inspired by (Tian al., 2023), and has connections to general chain-of-thought (Wei et al., 2022) literature which has shown various ap- proaches to promote intermediate reasoning steps can improve final result.
Finally, we see that although after calibration the different approaches do overall follow the perfect prediction line, especially SqueezeBERT, they do not give many predictions with very high certainty
"role": "user", "content": 'Rate the truthfulness of the following statement: "STATEMENT" Your task is to provide a score from 0 to 100, where

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1.0
Perfect Prediction
1.0
Perfect Prediction
GPT-4
GPT-4
Actual Probablitty
Actual Probabality
0.4
0.4
0.2
0.2
1.0
Predicted Probability
Predicted Probability
(a) Out-of-the-box (ECE=14.0%)
(b) Platt's method (ECE=5.9%)
1.0
Perfect Prediction RoBERTa-large
1.0
Perfect Prediction
RoBERTa-large
Actual Probabiitty
Actual
0.2
0.2
0.0
0.0
Predicted Probability
Predicted Probability
(c) Out-of-the-box (ECE=20.15
(d) Platt's method (ECE=6.3%
1.0
Perfect Prediction SqueezeBERT
1.0
Perfect Prediction
SqueezeBERT
Actual Probabilty
Actual Probabiitty
0.2
0.2
0.0
0.0
Predicted Probability
Predicted Probability
(c) Out-of-the-hox (ECF=4.0%)
(f) Platt's method (FCF=1.6%)
Figure GPT-4 (top), RoBERTa-large (middle), SqueezeBERT (bottom) probability predictions, out-of-the-box calibrated with Platt's method.

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Perfect Prediction
1.0
Perfect Prediction
BERT
BERT
Actual Probabilty
Actual Probabiltty
0.2
0.2
0.0
0.0
0.2
Predicted Probability
Predicted Probability
(a) Out-of-the-box (ECE=6.9%)
(b) Platt's method (ECE=4.0%)
Figure 3: BERT probability predictions, out-of-the-box or calibrated with Platt's method.
Perfect Prediction
Perfect Prediction
ConvBERT
ConvBERT
Actual Probabiltyy
Actual Probability
0.6
0.6
0.4
0.4
0.2
0.2
0.2
0.6
1.0
0.2
0.6
1.0
Predicted Probability
Predicted Probability
(a) Out-of-the-bor (ECE=6.9%)
(b) Platt's method (ECE=2.1%)
Figure ConyBERT probability predictions, out-of-the-box calibrated with Platt's method.
Perfect Prediction
Perfect Prediction
DeBERTa
DeBERTa
0.8
0.8
Actual Probability
Actual Probability
0.6
0.6
0.4
0.4
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
10
Predicted Probability
Predicted Probability
(a) Oft-of-the-box (ECE=2.2%)
(b) Platt's method (BCE=2.4%
Figure 5: DeBERTa probability predictions, out-of-the-hox calibrated with Platt's method.

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Perfect Prediction DeBERTa-v3
1.0
Perfect Prediction DeBERTa
Actual Probabilty
Actual Probabiltty
0.2
0.2
0.0
0.0
0.2
Predicted Probability
Predicted Probability
(a) Out-of-the-box (ECE=3.9%)
(b) Platt's method (ECE=3.3%)
Figure DeBERTa-v3 probability predictions, out-of-the-box or calibrated with Platt's method.
Perfect Prediction
Perfect Prediction
LUKE
LUKE
Actual Probabiliyy
Actual Probabiltty
0.6
0.6
0.4
0.4
0.2
0.2
0.2
0.6
1.0
0.2
0.6
100
Predicted Probability
Predicted Probability
(a) Out-of-the-bow (ECE=4.9%)
(b) Platt's method (ECE=2.5%)
Figure LUKE probahility predictions, out-of-the-box calibrated with Platt's method.
Perfect Prediction
Perfect Prediction
RoBERTa
RoBERTa
0.8
0.8
Actual Probability
Actual Probability
0.6
0.6
0.4
0.4
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
10
Predicted Probability
Predicted Probability
(a) Out-of-the-bx (ECE=4.9%)
(b) Platt's method (BCE=2.5%)
Figure 8: RoBERTa probability predictions, out-of-the-hox calibrated with Platt's method.

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1.0
Perfect Prediction SqueezeBERT
1.0
Perfect Prediction SqueezeBERT
Actual Probabiitty
Actual
0.2
0.2
0.0
0.0
Predicted Probability
Predicted Probability
(a) Out-of-the-hox (ECF=4.0%)
(b) Platt's method (FCF=1.6%)
Figure SqueezeBERT probability predictions, out-of-the-box or calibrated with Platt's method.
Perfect Prediction XLM-RoBERTa
Perfect Prediction XLM-RoBERTa
0.8
0.8
Actual Probability
Actual Probability
0.6
0.6
0.4
0.4
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.
Predicted Probability
Predicted Probability
(a) Out-of-the-box (ECE=3.6%)
(b) Platt's method (ECE=S.6%)
Figure 10: XLM-RoBERY probability predictions, out-of-the-box or calibrated with Platt's method.

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represents definitively false and 100 represents definitively true, you must not state your score until you've presented a thorough analysis. Do not begin your response with a number. First write your analysis. then write a vertical bar "I", then finally state your score."
Definition 5. "ccore-Reasonabee" the explana- tion reasonable in relation to the score it gives? If
so please put "y" for "yes". It would be "n" for "no" if for example it is very confident the state- ment is true but gave a score around 50 (indicating uncertainty) or well below (indicating false).
Q.2 Predictive Performance
This criteria is quite similar to the previous GT- Reasonable, just making the evaluation compared to the score instead of the ground truth.
We test the scores these prompts produce compared to no explanation GPT-4 Score Zero-Shot We re- port results (the primary metrics for the datascts: accuracy for LIAR, macro F1 for others) in Ta- ble 22.
We found that 100% of the examples were Score- Reasonable. This is not surprising but confirms the score stable in relation to the explanation and the model seldom if ever suddenly changes its mind at that stage. We report Ge-Reesoneble rates Table 23.
Q.3 Evaluation of the Explanations:
Reasonableness
To perform an initial evaluation of whether the ex- planations made sense and would be helpful, one of the authors manually examined samples of ex- planations according to the following directive:
We that the explanations are generally rea- sonable when correct. More unexpectedly, they are also often reasonable even when the prediction is wrong. We believe there are two main reasons. First, this often occurs when GPT-4 gives an answer indicating the statement is murky, e.g. score in the 40-60 range, while PolitiFaact agrees it is mixed but labels it slightly differently. Due to the threshold at 50, the binary class can end up different, even if both GPT-4 and PolitiFact are largely saying the same thing.
Definition 4. "GT-RRasoonabee: the explanation is reasonable in evaluating the statement rela- tion the ground truth PolitiFact or other actual information please put "y" for "yes". Otherwise, "n" for "no" (e.g., its explanation is false, does not explain the answer, illogical or outright nonsensi- cal, no explanation given at all, etc.).
We note that this is only proxy for the ultimate question in explainability: whether the explanation will help user of an information evaluation system based on this framework. However, that question cannot be answered without knowing the user, precise definition of "help", etc. This definition ignores all those factors and more, so we do not recommend for in depth analysis; we use it here simply as a tool to explore the results.
Second, there is some uncertainty and subjectiv- ity in the evaluation. For example, if GPT-4 gives correct framing and general info about the state- ment, but says it lacks a particular piece of data, is that a good explanation or not? If it gives an explanation with the context of 2021, when really the statement was made in 2008, is that good explanation?
More refinement of the process is needed. This would likely include more precision in the defi- nition and breaking it down into more categories, as well as larger sample size and more labelers. Nonetheless, these preliminary results already sug- gest an approach like this can have great potential for explainability, and it is far easier to get here than with previous models like RoBERTa.
We sampled 30 random examples where GPT- 4 gives the correct answer, 10 where it gives the wrong answer, and 10 where it gives the right an- swer and RoBERTa gives the wrong one. Although we might expect to only get reasonable explana- tion if giving the right answer, is conceivable that we could somehow have a correct explanation but a wrong answer. We would also like to better understand why it gives wrong answers. Mean- while, analyzing the examples GPT-4 got correct and RoBERTa wrong might provide further insights what GPT-4 relied on to get those answers that RoBERTa could not.
We do not recommend training on them, but in case these explanations are useful for further analysis, we publish them our Github.
Q.4 Evaluation of the Explanations: Impossible Cases
We then did a second round of another exam- ples with the same the same sampling setup, but with the addition of a second evaluation criteria:
We took the LIAR-New predictions, filtered for Impossible examples only according to VI Possi- bility labels (Appendix C), and randomly sampled

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Dataset
Method
Result
LIAR
GPT-4 Scope-then-Explain
62.9
LIAR
GPT-4 Explain-then-Score
64.9
LIAR
GPT-4 Score Zero-Shot
64.9
LIAR-New All
GPT-4 Explain-teen-Soeee
65.3
LIAR-New All
GPT-4 Score Zero-Shot
60.5
CT-FAN English 4- 4-Way
GPT-4 Explain-tte---Seeee
43.6
CT-FAN English 4- 4-Way
GPT-4 Score Zero-Shot
42.8
CT-FAN English 3- Way
GPT-4 Explain- thpplann-then--oe Seeiii-tten---ooorr Score
63.1
CT-FAN English 3-Way
GPT-4 Score Zero-Shot
58.6
Table Predictive performance of explainability prompts. Sepllin-thenn-ooee works well, matching or outper- forming Score Zero-Shot.
Sample Type
Round
Round 2
or misinterpretation of a forecast or pre- diction being mistaken for actual results.
Correct
28/30
29/30
W grong
5/10
8/10
GPT Correct RoBERTa Wrong
9/10
10/10
Given these considerations, it is impos- sible definitively confirm deny the statement without additional information. 50
Table 23: GT- Re-Reeaoneblle proportions examples domly sampled from each group. Not only are the ex- planations reasonable cases predicted correctly, but also significant number in which it did not.
PolitiFact rated this statement "Half True" not- ing the tv station accidentally aired mock election results, similar to GPT-4's suggestion that it might be misinterpretation of "forecast prediction" https://www.poliffaa.ccom httpa//www.aaaccccc///////////aa
50 where GPT-4 made the correct prediction and 50 where it was wrong.
Correct Answers Besides cases it does not really know (potentially even saying it does not know) but gets lucky, it seems either rely general information or assume United States context.
posts/phoenix-tvstane -t--sttio----iii -ht--call-ccctiio-aaa--------
The following are examples of assuming a United States context.
Statement: "There are 63 million abortions a year in this country."
The following is an example of relying on general information to evaluate the statement "A Phoenix TV station aired the election results more than week early."
The statement asserts that there are 63 million abortions a year in the United States. The Guttmacher Institute, leading research and policy organization committed to advancing sexual and re- productive health and rights, reported that there were approximately 862,320 abortions in the United States in 2017 [...]
The statement refers to specific event that could be verified through reliable sources. However, without any specific details such as the name of the TV sta- tion, the date of the alleged early airing, or the specific election question, difficult to confirm the truthfulness of important
Statement: "$2,000 stimulus checks were due on Jan. 19."
the claim. Furthermore, to note that the broadcasting of election results before the official end of voting is generally considered unethical and poten- tially illegal, as it could influence the out- come of the election. Therefore, it would be unusual for reputable TV station do so. Finally, it's also worth consider- ing that this could be a misunderstanding
The statement is suggesting that $2,000 stimulus checks were due on Jan. 19. This statement is ambiguous because it does specify the year or the country it is referring to. However, assuming refers to the United States and the year 2021, we can analyze its truthfulness [...]

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Statement: "Our Supreme Court has never taken away constitutional right."
signed one way or another, errors of this type are not distinguishable from more egregiously wrong and misleading answers. Solutions for such fail- ures may look very different (e.g., querying user or knowledge base for additional information, vs. mit- igating ballucinations or overconfidence). So this suggests need for more uncertainty-awar classi- fication and evaluation methods, such as some of the approaches propose hopefully even better future upproaches.
The Supreme Court of the United States has the power to interpret the Constitu- tion and determine the constitutionality of laws [...]
Although PolitiFact focuses largely on US poli- tics leading to correct answers here, assuming US context could be substantial issue if using a sys- tem based on this in other countries, even in the US in the case of evaluating statements that are actually about other countries. While many bi- ases like race, gender, and political ideology are commonly studied in relation to LLMs, this sort of "geographical" bias is less explored, particularly in the misinformation domain. These results suggest it merits further research.
The following is an example where GPT-4 seems to take it at face value that images exist, evaluating the statement "Images show how climate change destroyed the Greenland ice sheet and harmed a polar bear from 2009 to 2019."
The statement is making two claims: 1) climate change has destroyed the Green- land ice sheet and 2) climate change has harmed a polar bear from 2009 to 2019. In conclusion, while the statement cap- tures the essence of the effects of climate change, its of the term 'destroyed' and the lack of specific evidence regard- ing the polar bear make not entirely accurate. 770
Incorrect Answers
In the majority of cases, GPT-4 appears to know it is missing context. In some cases, seems to assume an image or video which it does not have access to exists, leading to incorrectly predicting a statement is true.
The following is an example where it knows context is missing when evaluating the statement "A royal guard member collapsed because of the COVID-19 vaccine."
In reality, PolitiFact rates this statement False, explaining "experts pointed out that the image of Greenland has nothing to do with the polar bear image, since polar bears face sea-ice loss in the Arctic Ocean, not Greenland" https::/wwww.poltiffatttctm http://ec...omssccccc////////aae http://aaaaa//fa/oooooooooo posts/10-years whw.eeearrraallleeeeaeeeeeeeeeee whwpnatttechaneeeeeeeeeeeeeee exaggerates--
The statement "A royal guard member collapsed because of the COVID-19 vac- cine" is not verifiable without specific information and context. The statement is vague and does not provide enough detail to determine its truthfulness. It doesn't specify which royal guard (from which country or monarchy), nor does it provide any timeline source of this information. Furthermore, it directly tributes the collapse to the COVID-19 vaccine, which is a complex medical con- clusion that would require professional medical evaluation to confirm. Vaccines can have side effects, but serious side effects are rare, and it's also possible for someone to collapse due to other health conditions. Without any specific information or context, it's impossible definitively confirm or refute this state- ment. 50
impa/).
Such errors might be addressed by targeted in- terventions, such as classifier to detect when an image/video is referenced, making user aware the system cannot see the pictures and might give inac- curate answers, or simply multimodal models that can process pictures smoothly (in theory, GPT-4 might already do this, but this feature is not yet enabled through the API and we were unable to evaluate it).
R
Web Retrieval
R.1 Prompt
"role": "user", "content": 'Rate the truthfulness of the following statement: "STATEMENT" Pro- vide score from 0 to 100, where represents definitively false and 100 represents definitively true. Do not provide any explanations, only re- spond with the numerical score.
In a binary evaluation (or even many forms of multiclass evaluation) where a label must be as-

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The following information may provide additional evidence or context for your rating: ARTICLE'
The first part is the same as our Score prompt, to which we add the last part about "additional evi- dence or context" and the corresponding PolitiFact article.
R.2 Answerless Oracle
To remove the final PolitiFact label, we first split the article into sentences. We then iterate back- wards through the article looking for sentence containing of the keywords "true", "false", "pants" (for "pants on fire" label). Each of the la- bels contains one of these, so this locates the last sentence in the article with a label keyword. We then remove that sentence and any subsequent ones. While this may not be perfect guarantee of remov- ing the label, PolitiFact articles almost always have the verdict at the very end. Generally, the only exception is if the article was later updated, which is not frequent. So this heuristic is quite consistent and sufficient for our preliminary analysis here, which does not aim to say that the performance is precisely X but just shows that sufficiently strong retrieval can massively increase performance com- pared to not using retrieval.
We make the original PolitiFacc articles and this answer-removing code available through our GitHub.


Reviews:
[{"review_id": "2l9xuxf8ZM", "reviewer": "Reviewer_nBWy", "summary": "", "questions": "", "limitations": "", "rating": 4, "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": "The main rationale for explicitly discouraging explanations was to make sure the results could be evaluated quantitatively in a direct way. Without “Do not provide any explanations, only respond with the number“ in the prompt, the responses will not follow a set format, making it difficult to reliably extract the prediction, especially if it includes multiple numbers in the response. Potentially a more complicated prompt that asks for a specific formatting could work, but our approach also reduces cost and increases speed by minimizing output tokens. The cost difference is also quite substantial, approximately 3.2 vs. 23.1 USD per experiment on LIAR (which is token-wise the smallest and therefore cheapest of the datasets - please see also bottom of response to reviewer H7JA). We will add a note clarifying this in the paper.\n\nThat said, we agree that explanations are valuable. The paper notes this as a promising direction for future work, but we are excited to share some preliminary results. We changed our prompt to the following:\n\n> Rate the truthfulness of the following statement: \"{text}\" \\\n> Your task is to provide a score from 0 to 100, where 0 represents definitively false and 100 represents definitively true, but you must not state your score until you've presented a thorough analysis. Do not begin your response with a number. First write your analysis, then write a vertical bar \"|\", then finally state your score.\n\nThis prompt is inspired by https://arxiv.org/abs/2308.00071 which suggested asking for explanation first followed by final answer is more effective than the reverse, as well as more general chain-of-thought literature.\n\nWe applied the prompt to LIAR, then using the original PolitiFact articles that LIAR is derived from, we evaluated the reasonableness of the explanations. Specifically, one of the authors went through each example manually and labeled them according to the following:\n\n> \"GT-Reasonable\": if the explanation is reasonable in evaluating the statement - in relation to the ground truth PolitiFact or other actual information - please put \"y\" for \"yes\". Otherwise, \"n\" for \"no\" (e.g., its explanation is false, does not explain the answer, illogical or outright nonsensical, no explanation given at all, etc.).\n\nWe note that this definition is only a proxy for the ultimate question in explainability here: whether the explanation will help a user of an information evaluation system based on this framework. However, that question cannot be answered without knowing the user, a precise definition of “help”, etc. \n\nWe sampled 30 random examples where GPT-4 gives the correct answer, 10 where it gives the wrong answer, and 10 where it gives the right answer and RoBERTa gives the wrong one. This division is motivated that mainly we only expect a reasonable explanation if it is giving the right answer, but we also might have a correct explanation but (somehow) a wrong answer, or in general want to understand what is happening in the wrong answers for better interpretation of the results like you highlighted. Meanwhile, analyzing the ones GPT-4 got correct and RoBERTa wrong might provide further insights on what GPT-4 relied on to get those answers that RoBERTa could not.\n\nWe then did a second round of another 50 examples with the same the same sampling setup, but with the addition of a second evaluation criteria:\n\n> “Score-Reasonable”: is the explanation reasonable in relation to the score it gives? If so please put “y” for “yes”. It would be \"n\" for \"no\" if for example it is very confident the statement is true but gave a score around 50 (indicating uncertainty) or well below (indicating false).\n\nThis criteria is quite similar to the previous GT-Reasonable, just evaluating compared to the score instead of the ground truth.\n\n## Results:\n\nRound 1 GT-Reasonable rates:\nRandom correct: 28/30\nRandom wrong: 5/10\nRandom GPT correct RoBERTa wrong: 9/10\n\nRound 2 GT-Reasonable rates:\nRandom correct: 29/30\nRandom wrong: 8/10\nRandom GPT correct RoBERTa wrong: 10/10\n\nRound 2 Score-Reasonable rates:\nRandom correct: 30/30\nRandom wrong: 10/10\nRandom GPT correct RoBERTa wrong: 10/10\n\nWe see that the explanations seem quite reasonable. There are no issues with score matching to explanation (Score-Reasonable rates = 100%), and even compared to the ground truth most explanations appear to make sense. Most surprisingly, this holds even for the case where GPT-4’s prediction is wrong. We two main reasons:\nThis often happens when GPT-4 gives an answer indicating the statement is murky, e.g. score in the 40-60, which PolitiFact also says but labels slightly differently. Due to the threshold at 50, the binary class can end up different, even if both GPT-4 and PolitiFact are largely saying the same thing.\nThere is some uncertainty in the evaluation. For example, if the LLM gives correct framing and general info about the statement, but says it lacks a particular piece of data, is that a good explanation or not? If it gives an explanation with the context of 2021, when really the statement was made in 2008, is that a good explanation?\n\nMore refinement of the process, likely breaking it down into more categories (as well as more samples and more labelers), is needed. Nonetheless, these preliminary results already suggest an approach like this can have great potential for explainability, and it is far easier to get here than with previous models like RoBERTa. We will add this experiment to the paper, along with publishing the data and adding some case studies to the appendix. We hope it will provide a launchpad for in depth future work on explainability in this context.", "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. Dataset Creation and Uncertainty Handling: The proposed LIAR-New dataset with paired English and French misinformation data, along with Possibility labels indicating context for veracity evaluation, represents a valuable resource for the research community. Additionally, the proposed techniques to handle uncertainty demonstrate the paper's practicality and applicability in real-world settings, where perfect classification is often challenging. \n\n2. Comparative Performance and Novel Findings: GPT-4, as the most advanced LLM by OpenAI, have demonstrated excellent performance on many real-life tasks. The results in this work demonstrates that GPT-4 can outperform existing methods in various settings and languages, showcasing the superiority of the proposed approach. The exploration of generalization differences between GPT-4 and RoBERTa-large offers novel insights that hold promise for significant performance improvements.\n\nThese dataset creation efforts, the uncertainty handling techniques and the results with GPT-4 are important contributions that will drive progress in misinformation detection and mitigation.", "reasons_to_reject": "1. In the GPT-4 experiments, the instructions provided explicit prompts to the model, specifying the omission of explanations in the returned outputs, and only scores were requested. However, it is worth noting that explanations could potentially offer valuable insights for interpreting the results to some extent. The rationale behind discouraging the inclusion of explanations in the outputs is not mentioned in the paper.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This work proposes a new dataset for fake news detection in English and French. It demonstrate that GPT-4 outperform existing methods in fake news detection, revealing that GPT-4 and RoBERTa-large exhibit critical differences in failure models. Moreover, the code and dataset are publicly available for the community to reproduce the results and conduct further research.", "excitement": 4, "reproducibility": 5, "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": "CVz974AUQx", "reviewer": "Reviewer_H7JA", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": 5, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Techniques to improve performance\n\nWe agreed there could potential for improvement - perhaps significant improvement - with better prompting strategies. We’ve now run a number of experiments with different prompts, along with other techniques to improve the performance. This includes two explainability prompts, three prompts for in-context learning with automatically chosen demonstration, finetuning GPT-3.5 on the LIAR training set (and 8 other new results with GPT-3.5), and using PolitiFact articles to evaluate the potential of web retrieval systems in this context.\n \nMost of these approaches provided only marginal improvements, or directly made performance worse. Only web retrieval yielded better results, and shows potential for very large performance improvements - 20+ percentage points, with a sufficiently strong retrieval system. Based on these results, we believe that is the most promising direction for reducing errors (in tandem with other directions like explainability and improved uncertainty quantification, that are critical for real-world results but not directly aimed at reducing errors).\n\nBelow we discuss all of these results in more detail.\n\n\n\n### Classification Performance of Explainability Prompts:\n\nFirst, we consider two prompts related to explainability, also discussed in response to Reviewer nBWy:\n\nPrompt 1, (Score Zero-shot): 64.9 (accuracy)  \n> Rate the truthfulness of the following statement: \"{text}\" \\  \n> Your task is to provide a score from 0 to 100, where 0 represents definitively false and 100 represents definitively true, but you must not state your score until you've presented a thorough analysis. Do not begin your response with a number. First write your analysis, then write a vertical bar \"|\", then finally state your score.\n\nPrompt 2, (Score Zero-shot): 62.9 (accuracy)  \n> Rate the truthfulness of the following statement: \"{text}\" \\  \n> Provide a score from 0 to 100, where 0 represents definitively false and 100 represents definitively true. Then write a vertical bar \"|\", followed by explaining your reasoning for the score.\n\nThe first prompt, which has some chain-of-thought characteristics in terms of asking for reasoning first, gives results equal to the Score prompt in the paper, while the second’s performance is a bit worse.\n\n\n### In-context learning with an automatically chosen demonstration:\n\nThe following three prompts use a demonstration chosen by embedding similarity of the target statement with statements in the training set. This has similarities to https://arxiv.org/abs/2305.14128 and is particularly motivated by the observations in section 4.5 of our paper, i.e. that GPT-4 and RoBERTa make different errors in a way that is tied to embedding similarity with training set examples. The hope is that by providing the most similar training set example, that provides both a demonstration and potentially a direct source of information relevant to the target example. Unfortunately, however, this proved ineffective.\n\nIn the below prompts, closest_train_label refers to the label of the closest training example, uniformly mapped onto the 0-100 range. Closest_train_text refers to the corresponding input statement.\n\nPrompt 3, (Score Zero-shot): 57.2 (accuracy)  \n> Rate the truthfulness of the following statement: \"{statement}\"  \n\n> Here is the rating of a similar statement. The rating is accurate, but the statement may or may not be relevant to this one.  \n> Similar statement: \"{closest_train_text}\"  \n> Rating: {closest_train_label}\n\n> Provide a score for the original statement from 0 to 100, where 0 represents definitively false and 100 represents definitively true. Do not provide any explanations, only respond with the numerical score.\n\nPrompt 4, (Score Zero-shot): 64.2 (accuracy)  \n> Here is a similar statement that is {closest_train_label}: \"{closest_train_text}”  \n> Now rate the truthfulness of the following statement: \"{statement}”  \n> Provide a score from 0 to 100, where 0 represents definitively false and 100 represents definitively true. Do not provide any explanations, only respond with the numerical score.'\n\n\nPrompt 5, (Score Zero-shot): 65.2 (accuracy)  \n> If the most similar training set example is in the top 10% most similar: Prompt 4  \n> Else: original Score prompt\n        \nPrompt 3 doesn’t work well, but 4 and 5 are OK. 5 gives an improvement on GPT-4 Score Zero-Shot (64.9), but 0.3% is marginal - could easily be noise, and not likely to be worth the extra tokens and complications.\n\n\n### Finetuning and GPT-3.5\n\nWhile not exactly prompting, in the similar vein of general strategies to improve a foundation model’s performance, we also tested GPT-3.5 and its newly-released finetuning feature. Specifically, we use the following prompt:\n\n>Rate the truthfulness of the following statement: \"{statement}\" \\  \n>Provide a score from 0 to 100, where 0 represents definitively false and 100 represents definitively true.\n\nThis is the same as the usual Score prompt, except the removal of the last sentence “Do not provide any explanations, only respond with the numerical score.” We remove this because the finetuning should already be sufficient showing it to respond only with the numerical score (and in practice it does, 100% of the time). We map the original 6-way LIAR labels to scores 0-100 uniformly, i.e. [0,20,40,60,80,100]. Then we finetune with default settings, which resulted in 2 epochs of finetuning. We compare with the untuned version with the usual Score prompt. The results on LIAR were:\n\nGPT-3.5 Score Zero-Shot: 67.3 (accuracy)  \nGPT-3.5 Score Zero-Shot Finetuned: 57.2 (accuracy)\n\nThe finetuning is not effective and seems to damage the generalization ability. We hypothesize that finetuning to Impossible examples might be particularly damaging. That said, this is a first attempt using a system that was only released last week, so there is considerable room for experimentation that might yield better results. Further research in this direction is needed, as well as testing GPT-4 finetuning when that becomes available (later this year according to OpenAI).\nWe note that “untuned” GPT 3.5 performs surprisingly well, better than GPT 4 in the same Score Zero-Shot setup. However, there are some caveats. First, its score is already maximized at the zero-shot binarization threshold of 50, meaning it cannot achieve higher performance through tuning on the validation set like GPT-4 can. The margins are small, however, so this may not have a big impact. More significantly, the performance seems brittle, as illustrated by the following results:\n\nLIAR GPT-3.5 Binary: 53.6 (accuracy)  \nLIAR-New “All” GPT-3.5 Binary: 55.7 (macro f1)  \nLIAR-New “Possible” GPT-3.5 Binary: 58.3 (macro f1)  \nLIAR-New “All” GPT-3.5 Score Zero-Shot: 61.1 (macro f1)  \nLIAR-New “Possible” GPT-3.5 Score Zero-Shot: 60.3 (macro f1)  \nLIAR-New “All” GPT-3.5 Binary Uncertainty-Enabled: 67.7 (macro f1)  \nLIAR-New “Possible” GPT-3.5 Binary Uncertainty-Enabled: 67.5 (macro f1)\n\nIn addition, in the Uncertainty-Enabled setting, it marked 794 examples as ones it did not have certainty on, not too far from GPT-4’s 914. Of which 519 (65.4%) were Impossible, which is a bit lower than GPT-4’s 69.1% but again comparable. And we do see a relative performance increase. But in terms of the absolute numbers, in all 7 of these cases, the performance is much worse compared to GPT-4. Therefore, GPT-3.5’s strong performance on LIAR with Score Zero-Shot prompt may be mostly luck.\n\n\n### Web Retrieval\n\nAnother route to improving performance is incorporating external information. We conducted an initial investigation of this approach. In particular, as part of the collection process of LIAR-New, in addition to the statements and labels, we also obtained all of the corresponding PolitiFact articles. We set up the following prompt:\n\n>Rate the truthfulness of the following statement: \"{input_text}\"  \n>Provide a score from 0 to 100, where 0 represents definitively false and 100 represents definitively true. Do not provide any explanations, only respond with the numerical score.\n\nThe following information may provide additional evidence or context for your rating:\n{article_text}\n\nWe consider two settings. First, we just provide the entire PolitiFact article. We call this “Web Oracle” because it represents nearly the ideal case for web retrieval - the document is perfectly relevant and directly gives veracity judgment. Second, we remove the last part of the PolitiFact article where PolitiFact’s rating is stated. Here the document is still perfectly relevant, but the system has to do more evaluation of the information presented itself.\n\nLIAR-New:  \nGPT-4 Web Oracle: 98.6 (macro f1)  \nGPT-4 Web Answerless Oracle: 90.7 (macro f1)\n\nThis shows an enormous performance improvement compared to all other approaches. Now, there is a significant limitation: here we already had and knew where to get relevant information. This is a standard approach for developing and evaluating parts of an overall system involving information retrieval (e.g., it is used in the well-known FEVER dataset paper https://aclanthology.org/N18-1074/), but not possible in practice. So more research is needed here to reach these results with non-oracle retrieval systems. Nonetheless, these results show there is enormous potential for these systems to use external information from such systems.\n\n\n### Overall\n\nWe hope all of these results will provide some further insights on prompting and other strategies to improve performance of a fixed foundation model in this context. We recognize that there are many other techniques still unexplored, such as more classic chain-of-thought, few-shot setups with more than one example or with the examples chosen in different ways, and many more. However, after considering the results here where most changes had either limited or negative impact, we believe it may still be too soon to devote too much research to fully optimizing prompting for performance here. Rather, we suggest that first research should focus on integrating modules necessary for strong real-world results from such a system, such as web retrieval which provides a route to much larger performance increases than prompting, and explainability to make predictions that inspire greater confidence and provide more practical information. Then when a system is close to ready for real-world use, or when there are no more promising modules to add, that will be the most efficient time to thoroughly explore prompts. Otherwise, there is a substantial risk that carefully crafted prompts will be rendered obsolete by new modules that greatly change how the system works.\n\nWe will add these experiments, results, and discussion to the paper.\n\n\n\n\n## Reviewer: Section 3.1 is a bit too long. It's better to add some subsections.\n\nThank you for the feedback, we will add subsections separating the prompts and the other information on converting the GPT-4 answers to label predictions.\n\n## Reviewer: The exact expenses on OpenAI APIs could be included as well.\n\nFor GPT-4 Score Zero-Shot on LIAR, the cost is approximately 3.2 USD (100k prompt tokens, 3k completion tokens). On LIAR-New, 4.6 USD (154k, 2k). On CT-FAN English, where the input examples are much longer,  21.8 USD (725k, 600). On CT-FAN German, 28.7 USD (956k, 700). These numbers can vary slightly between runs of the model (depending on completion tokens), as well as slight differences in the three prompts in the paper, but should provide reasonable estimates.\n\nAmong the new experiments, Prompt 1 above (adding explanations to the output instead of just the number) cost 23.1 USD (142k, 314k). The finetuning on the LIAR train set comprised 609k tokens costing 4.9 USD. Web Retrieval Oracle cost 53 USD (1765k, 2k).\n\nWe see that there is a very large range depending on different data and prompts. Furthermore, the overall budget needed for these experiments is not trivial. In the short-term, thoughtful prompting and choice of experiments to target can help keep costs down when budget is limited. In the longterm, cheaper models with comparable performance will almost certainly be found or created, which will be a significant advantage for both research and real-world deployment.\n\nWe will add these numbers and discussion to 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. This paper is well-organized and easy to read.\n2. The experiments are quite extensive with different settings and baseline methods. The experimental result analysis is inspiring. These can well support the research claims and conclusions.\n3. This paper proposes a new multilingual dataset on misinformation prediction.", "reasons_to_reject": "1. Prompts exploration is insufficient.\n\n    Although this paper proposes different prompts for misinformation prediction,\n    it does not try other prompts to enable in-context learning with samples from a training set or human-crafted.\n    It is expected to see how this can further improve the ability of GPT-4 on misinformation prediction.", "questions_to_authors": "", "paper_topic_and_main_contributions": "Instead of technical contributions,\nthis paper studies the feasibility of using GPT-4 on misinformation prediction.\nThis paper conducts extensive experiments on three different datasets in English and French.\nIt analyzes the generalization and uncertainty of GPT-4.\nThe results show GPT-4 can be a better misinformation predictor than prior work. It also finds that GPT-4 has a different failure mode from other transformer-based LMs.", "excitement": 5, "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. Section 3.1 is a bit too long. It's better to add some subsections.\n2. The exact expenses on OpenAI APIs could be included as well."}, "extra_text": {}}, {"review_id": "wUSU49nDKT", "reviewer": "Reviewer_FWg4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> Reviewer: Technical depth will need to further expanded: The main concern in the paper is that they only discuss this from one point of view of Large Language Models (LLMS) which is GPT-4. The paper claims it is the best which is not true. Google Bard has proven better. Moreover, there are many other LLMS that could be comprised such as LLama or Vicuna. The assumption that GPT-4 is the best out there has significantly weakened the paper.\n\n\nWe agree that GPT-4 is not universally strongest, and it is valuable to test other LLMs. We will amend the paper to only state that GPT-4 is the strongest in many tasks. Furthermore, we have now tested some other models. Specifically:\n\nLIAR:  \nLlama 2 13b (instruct-tuned): 52.4 (accuracy)  \nLlama 2 70b (instruct-tuned): 50.1 (accuracy)  \nPaLM 2 Bison: 62.4 (accuracy)  \nOld result: GPT-4 Score Zero-Shot: 64.9 (accuracy)  \n\nCT-FAN-22 English:  \nLlama 2 13b (instruct-tuned): 22.3 (macro f1)  \nLlama 2 70b (instruct-tuned): 21.4 (macro f1)  \nPaLM 2 Bison: 21.3 (macro f1)  \nOld result: GPT-4 Score Zero-Shot: 42.8 (macro f1)   \n\nWe also tested Bison on CT-FAN German: 22.3 (macro f1). These results correspond to our Table 2 and are the “Score Zero-Shot” accuracies by the terminology of the paper. Unfortunately, tuning the score threshold does not enable any significant improvement, for example, on LIAR Bison’s performance is already maximized, and at best Llama’s performance increases to 56.4 by predicting majority class only. Unfortunately, Bard itself is not available in our country for testing, and we similarly were not able to access PaLM 2 Unicorn which powers Bard (Bison reported here is the second largest version, while Unicorn is the largest).\n\nAlthough Bison does not perform as well as GPT-4, and may particularly struggle with the longer and more complicated inputs of CT-FAN-22, on LIAR it is close to the other language models tested, without relying on a training set and without being the strongest PaLM 2 model. So, this again highlights the potential of recent LLMs for this domain. We will add these results to our paper. \n\nWe chose to start with GPT-4 because Bard was not accessible in our country, and most resources we have seen indicate GPT-4 is strongest. The comparisons between PaLM 2 (on which Bard is based) and GPT-4 highlighted in Table 5 of the PaLM 2 Technical Report, where PaLM 2 wins 2 out of 3, omitted a number of other comparisons. For example, GPT-4 has superior performance on MMLU https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu, but the PaLM 2 report only gave their own result there with no comparison. Similarly, GSM8K  https://paperswithcode.com/sota/arithmetic-reasoning-on-gsm8k, HellaSwag https://paperswithcode.com/sota/sentence-completion-on-hellaswag, and HumanEval https://paperswithcode.com/sota/code-generation-on-humaneval.\n\nWe would also like to highlight that should Bard or another model prove better than GPT-4 here in the future, that does not detract but actually strengthens the value of our results. That is, if recent LLMs can do even better than the results we have found so far, that means they will be providing even more value in terms of performance, practical quantification of uncertainty, and generalization.\n\n\n> Reviewer: The paper lacks case studies.\n\nA case study was presented in Appendix F (“LIAR Error Analysis Case Study”). We look at examples GPT-4 gets wrong but RoBERTa gets right, and show how some examples are extremely close semantically to training set examples. In these cases, learning from the training set examples could be helpful, but could also result in artificially easy test set examples that don’t reflect generalization.\nWe will also add case studies on explainability as discussed in the response to reviewer nBWy. If those do not cover the type of case study you have in mind, we would welcome additional feedback here.\n\n> Reviewer: inefficient survey of existing work. The paper should be looking for other LLMs and make a valid comparison.\n\nThe paper compares extensively with the state-of-the-art on these tasks and datasets. This state-of-the-art was not other recent (2023, generative) LLMs, it was older models such as  BERT and RoBERTa. Certainly other LLMs could be explored and we make no argument that other recent LLMs could not give better results than GPT-4 in this domain. The paper also suggests other recent LLMs as a direction for future research, which we have been looking into after submitting the paper and have shared some of the results in response to the previous question.\n\nWe will also add a section to the literature review on other recent LLMs, and discuss which ones like Bard would be most promising for future investigation. If you have references showing that Bard is the strongest overall model available (besides the PaLM 2 technical report, which as we discussed above seems insufficient), we would be very happy to learn from them and include them. More generally, if you have any other insights on which models might have the most potential in this domain and why (and therefore would provide the most value as suggestions for future researchers to investigate), those would likewise be greatly appreciated.", "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) Interesting solution to detect and mitigate misinformation \n2) Extensive experiment focused on uncertainty and generalization.\n3) Good presentation\n\n\n\n", "reasons_to_reject": "1) Technical depth will need to further expanded: The main concern in the paper is that they only discuss this from one point of view of Large Language Models (LLMS) which is GPT-4. The paper claims it is the best which is not true. Google Bard has proven better. Moreover, there are many other LLMS that could be comprised such as LLama or Vicuna. The assumption that GPT-4 is the best out there has significantly weakened the paper. \n2) The paper lacks case studies. \n3) inefficient survey of existing work.  The paper should be looking for other LLMs and make a valid comparison. \n", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes  a multifaceted investigation to determine if they can use GPT-4, combined with  analysis of generalization and uncertainty, to produce more practical misinformation detection. The paper's aim is to establish a ground  that will lead to practical systems and, improvements in real-world misinformation mitigation. The contributions are as follows: \n1-  The study shows that GPT-4 is effective for both hard and soft classification tasks related to misinformation, including multilingual scenarios, outperforming existing approaches.\n2 - The research analyzes the generalization capability of GPT-4 compared to previous work. \n3 - The study introduces a method to handle uncertainty with GPT-4, which involves excluding examples it struggles to classify effectively. \n4 - Introduce a new dataset called LIAR-New consisting of English and French\n", "excitement": 3, "reproducibility": 5, "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 paper provides a new dataset for misinformation detection and mitigation in French and English. The authors show that GPT-4 outperforms existing methods and that GPT-4 and RoBERTa show large differences in what they fail to detect. They further show that training GPT-4 with an uncertainty-based method that excludes examples that are difficult to classify further improves performance. The dataset and code will be publicly released and should be useful for the community. Some of the weaknesses regarding the explanations and prompts were sufficiently addressed by the authors in the rebuttal and the results and analysis would be great to add to the paper. One reviewer had concerns about the number of different LLMs tested. The authors responded with some insight into other models and I believe that this is appropriate for the scope of this work. Overall, the work appears to be sound and exciting.
Decision: Accept-Main