Generative Debunking of Climate Misinformation (2024)

Abstract: Misinformation about climate change causes numerous negative impacts, necessitating corrective responses. Psychological research has offered various strategies for reducing the influence of climate misinformation, such as the fact-myth-fallacy-fact-structure. However, practically implementing corrective interventions at scale represents a challenge. Automatic detection and correction of misinformation offers a solution to the misinformation problem. This study documents the development of large language models that accept as input a climate myth and produce a debunking that adheres to the fact-myth-fallacy-fact (“truth sandwich”) structure, by incorporating contrarian claim classification and fallacy detection into an LLM prompting framework. We combine open (Mixtral, Palm2) and proprietary (GPT-4) LLMs with prompting strategies of varying complexity. Experiments reveal promising performance of GPT-4 and Mixtral if combined with structured prompts. We identify specific challenges of debunking generation and human evaluation, and map out avenues for future work. We release a dataset of high-quality truth-sandwich debunkings, source code and a demo of the debunking system.1

heading: 1 Introduction
text: Misinformation about climate change causes numerous negative impacts such as reducing climate literacy (Taddicken and Wolff, 2023), polarizing the public (Cook et al., 2017), and cancelling out attempts to communicate factual information (Van der Linden et al., 2017). Psychological research has offered various strategies for reducing the influence of climate misinformation, including
1Dataset: https://huggingface.co/datasets/ fzanartu/CARDSexamples, Code: https://huggingface. co/spaces/fzanartu/flicc-agent/tree/main, Interactive demo: https://huggingface.co/spaces/fzanartu/ flicc-agent
the recommended approach of adhering to the factmyth-fallacy-fact structure of a debunking (Figure 1; Lewandowsky et al. (2020)).
However, while psychological research provides best-practices for debunking, practically neutralising misinformation in real-world conditions is challenging. False information spreads faster and deeper than factual information on social media, making it difficult to counter misinformation before it has already done damage (Vosoughi et al., 2018). In order to be effective, corrective interventions need to be deployed at scale and faster than misinformation can spread. Automatic detection and correction of misinformation, a goal described as the “holy grail of fact-checking” (Hassan et al., 2015), offers a solution to this challenge.
1 46
This paper presents efforts towards the completion of this “holy grail” by synthesising generative AI with past research on climate contrarian claim classification and fallacy detection, in an approach we call generative debunking. This approach adopts elements of the 4D framework (Cook, 2024) which involves detecting, deconstructing, debunking, and deploying corrective interventions. Specifically, we build upon the CARDS (Computer Assisted Recognition of Denial & Skepticism) classifier which was developed to detect specific contrarian claims about climate change (Coan et al., 2021; Rojas et al., 2024), and the FLICC model (Zanartu et al., 2024) that detects fallacies in climate misinformation, such as Fake experts, Logical fallacies, Impossible expectations, Cherry picking, and Conspiracy theories (Cook, 2020).
Specifically, we implement our generative debunking framework by testing the ability of three unique combinations of prompting strategies of varying complexity with large language models (LLMs) of different size (Section 4) to produce a structured and psychologically grounded “truth sandwich” debunking for a myth (Figure 1, Section 2). We evaluate the quality of the produced debunking (Sections 5, 6), identifying a lack of factuality and relevancy as a critical shortcoming even with the latest LLMs. In Section 7 we discuss challenges of generating valid debunkings and their evaluation, and opportunities for future research.

heading: 2 Background
text: Psychologically effective debunking Psychological research recommends that debunkings should adopt the fact-myth-fallacy-fact structure (Lewandowsky et al., 2020). The fact should have the same explanatory relevance as the misinformation (Ecker et al., 2010; Seifert, 2002). For example, if the myth was “the sun is causing global warming”, the fact should specify the actual cause (e.g., “CO2 emissions are causing global warming”).
On the question of whether a debunking should mention the myth that is being refuted, there has been some speculation that debunkings should avoid mentioning misinformation lest the retraction causes a counterproductive “backfire effect” where belief in the myth is inadvertently strengthened (Nyhan and Reifler, 2010). However, researchers have found difficulty in replicating the backfire effect (Wood and Porter, 2019). Rather, it is rec-
ommended that a debunking should repeat the misinformation once, as one repetition of the myth is beneficial to belief updating (Ecker et al., 2017). However, further repetition of the misinformation should be avoided, as it makes information appear true, a phenomenon known as the illusory truth effect (Fazio et al., 2015).
Next, the logical or argumentative fallacies underlying the misinformation should be explained (Cook et al., 2017). Explaining fallacies is powerful as they are not domain specific, empowering recipients to see the same fallacies in other topics (Schmid and Betsch, 2019). Explaining fallacies has also been shown to be effective in reducing the influence of misinformation regardless of whether the correction comes before or after encountering misinformation (Vraga et al., 2020). Incorporating fallacy explanations is especially important for more nuanced forms of misinformation such as paltering or cherry picking, which can involve truthful statements that are nonetheless misleading (Lewandowsky et al., 2017). For fallacy detection, this research relies on the FLICC framework that summarises the five techniques of science denial—Fake experts, Logical fallacies, Impossible expectations, Cherry picking, and Conspiracy theories (Cook, 2020).
In a debunking, the fact should be repeated again at the end. Wrapping facts at the start and end of a correction is known as a “truth sandwich” (König, 2023; Sullivan, 2018). Information that is presented first and last is usually remembered best due to the primacy and recency effects (Jahnke, 1965). The repetition also makes the fact more likely to be believed by recipients (Fazio and Sherry, 2020). We adopt the four-layer “truth sandwich” (Figure 1) to structure the output of the models described below.
Automatic debunking Automatic fact checking has attracted substantial interest in NLP, however the bulk of approaches falls short of generating a free-text justification by casting the problem as veracity prediction (a classification task) (Guo et al., 2022). Some works explain their veracity labels either by analyzing model-internal configurations that lead to a particular prediction, or by extracting explanatory facts from supporting or refuting documents (Kotonya and Toni, 2020), or generating it with LLMs (Hsu et al., 2023). A separate line of work studied automatic logical fallacy detection (Jin et al., 2022; Alhindi et al., 2022). To the
2
best of our knowledge we are the first to integrate fallacy detection into an end-to-end system for psychologically grounded, structured debunking.

heading: 3 Data
text: Our automated debunking system leverages various public datasets and a novel dataset specifically curated to provide gold-standard examples of the type of debunkings we are aiming for.
FLICC test set The FLICC test set consists of 256 samples across 12 logical fallacies. These samples were used to report results for the FLICC model (Zanartu et al., 2024), but they are not part of its training knowledge. We also randomly sample 20 instances from this data set for model evaluation (Section 5).
CLIMATE-FEVER dataset The CLIMATEFEVER dataset (Diggelmann et al., 2020) encompasses 1,535 real-world climate change-related claims. Each claim is associated with five manually annotated evidence sentences from English Wikipedia, which either support the claim, refute it, or contain insufficient information for claim validation. Our study exclusively utilises the false (refuted) claims from this dataset, and we employed the CARDS classifier (Rojas et al., 2024) to automatically label these instances with their misinformation category. The resulting refutations were used as additional context in fact generation (Section 4.3).
CARDS-examples dataset Additionally, we have developed a dataset of gold-standard truthsandwich debunkings and gold fallacy labels for 62 instances of misinformation, referred to as the CARDS-examples dataset. The debunkings were created by a misinformation expert who has taught climate debunking in a Massive Open Online Course that has received over 51,000 enrolments, and is a co-author of this paper. We use this data to retrieve relevant examples for in-context learning (Sections 4.2 and 4.3)2.

heading: 4 Generative Debunking
text: We present our generative debunking approaches in order of increasing complexity, ranging from a single generic prompt (section 4.1), over a single prompt with myth-specific external information (section 4.2; Figure 2, left), to a structured
2We include this dataset as supplementary material, and will make it public upon acceptance.
approach that prompts individually for each layer in the debunking (section 4.3; Figure 2, right). We apply the simpler approaches with stronger LLMs and vice versa.
The single generic prompt is a complex endto-end instruction for the underlying LLM that implicitly requires the model to perform fallacy detection, careful formatting, and fact retrieval. While it is easy to implement, it is limited to the LLM’s internal knowledge and requires a powerful (and expensive) model to compensate for the prompt complexity and produce good-quality output. We test it with GPT4.
The single prompt with myth-specific external information incorporates external information and guided examples into a single end-to-end prompt. It is still limited to the LLM’s internal knowledge for fact retrieval. It is augmented with an external model for reasoning tasks that is not financially expensive to run. We test this more expressive prompt with a smaller, open source LLM (Palm2).
The structured prompt with myth-specific external information involves more complex code that, in exchange, simplifies the generative debunking task into simpler subtasks. It incorporates a tool for searching the internet for specifics about certain topics and is also paired with the external reasoning model mentioned above. This most expressive prompt is combined with Mixtral.
Given the trade-off between LLM complexity and prompt complexity, we anticipate similar results among all three approaches. A more systematic experiment that disentangles the effects of LLM choice and prompting strategy is left for future work, noting that the general tendencies reported here are likely to persist, while the specifics of direct LLM comparisons tend to be short-lived with the rapid development of the technology.

heading: 4.1 Single prompt, no context (GPT4)
text: We construct a single, comprehensive prompt which assigns the LLM the role of a climate change analyst as an expert persona (Salewski et al., 2023). The instructions explain each layer in the sandwich debunking, as well as the FLICC taxonomy of logical fallacies, requesting the Fallacy component of the debunking to refer to one of the options in the taxonomy. The prompt concludes with a static example, irrespective of the given input text. Table 5 in the appendix lists the full prompt. We use this prompt with GPT-4-turbo-preview (gpt-40125-preview) (OpenAI, 2024), the most competi-
3
tive LLM available at the time of writing. Preliminary experiments showed that weaker open-source LLMs like Palm2 when presented with this prompt, produce debunkings that are incoherent in content and/or do not comply with the truth sandwich structure.

heading: 4.2 Single prompt, with context (Palm2)
text: Hypothesizing that LLMs benefit from mythspecific context and examples, we built on the prompt presented in section 4.1 to add dynamic context relevant to the input myth (see Figure 2 (left) for an illustration). First, rather than including the full FLICC taxonomy in the prompt, we now call the FLICC model (Zanartu et al., 2024) and dynamically insert its fallacy prediction along with the definition of the predicted fallacy, which explains how the myth misleads. Secondly, we incorporated a dynamic example into the prompt. Secondly, rather than relying on a fixed example, we now retrieve specialised instructive examples of myths with human labelled fallacies and their associated gold-standard debunking. Specifically, we encode the input myth and all myths in the CARDSexamples dataset tagged with the same type of logical fallacy using sentence-transformers3 (Reimers and Gurevych, 2019). Subsequently, we select the example with the highest cosine similarity to the input myth. This selected example is then integrated into our prompt, which is now tailored to the input myth. Table 6 in the appendix lists the full prompt. We use this prompt with Palm2 (textbison-001) (Anil et al., 2023).
3Sentence-transformers model https://huggingface. co/sentence-transformers/all-MiniLM-L6-v2

heading: 4.3 Structured prompt, with context (Mixtral)
text: Finally, we experiment with an approach that splits the single end-to-end prompt in 4.2 into four separate prompts, one per component of the output debunking as illustrated in Figure 2 (right). We used these prompts with Mixtral-8x7B-InstructvO.1 (Jiang et al., 2024).
Layer 1: FACT. To encourage a specific response (rather than information that’s broadly relevant to the myth), we employ a ReAct-style agent (Yao et al., 2022) equipped with an internet search tool. This agent prompts the model to reframe the misinformation as a climate changerelated query, stimulating factual investigation, and utilises this query to retrieve additional information in order to enrich its response with specific facts.
We use the ReAct implementation from the LangChain library4 with the DuckDuckGo5 search engine and default parameters, which extracts textual content from the top five query results. From there, we rely on the LLM capabilities to distil this text and summarize the most factual information within two sentences or less than 30 words, following the guidelines of the ReAct prompt (Table 7 in the Appendix). The resulting text is shared within prompts for layer 3 and layer 4 (see Figure 2) to preserve coherence and consistency across all involved layers. The text is transmitted as dynamic content in prompt 3 (see Table 9) and prompt 4 (see Table 10 and 11).
Layer 2: MYTH. We directly prompt the LLM to paraphrase and summarise the input myth within 30 words in clear and concise language without
4ReAct prompt https://python.langchain.com/ docs/modules/agents/agent_types/react/
5Web search tool https://python.langchain.com/ docs/integrations/tools/ddg/
4
adding additional information. The full prompt is in Table 8 in the Appendix.
Layer 3: FALLACY. We adapt the strategy presented in section 4.2, taking inspiration from chainof-thought methodology. We retrieved the logical fallacy prediction from the FLICC model, alongside the fallacy definitions and corresponding goldstandard debunkings employing the same process described in section 4.2. We added special delimiters to separate system messages from instructions. We also integrated factual information obtained from the initial fact layer (Layer 1: FACT), providing general context to facilitate coherence between this now two separated prompts. The refined prompt instructs the LLM to generate two sentences that identify the fallacy contained in the input myth and elucidate its inaccuracies by connecting it to factual evidence, showing how it distorts the reality (full prompt in Table 9 in the Appendix).
Layer 4: FACT. The final module reinforces the fact of layer one, with the opportunity to introduce supplementary information for enhanced comprehension. The process here is as follows:
1. Predict the label of the input myth with the CARDS classifier.
2. Identify all CLIMATE-FEVER claims that share the same label as our input myth.
3. Identify the claim that is most similar to the input myth as in layer 3 by computing cosine similarity in sentence embedding space. We finally obtain the five manually annotated evidence sentences with highest cosine similarity that explain why the claim is refuted.
4. Add these five sentences into our prompt as potential sources of new communication elements, while relying on the LLM’s capabilities to determine their relevance.

heading: 5 Evaluation
text: We evaluate the three models on their debunking of 20 climate myths, which were taken from the test data set of the FLICC model (i.e., instances that none of our models has ever been exposed to during training).
We devise a structured validation approach where Fact 1, Fallacy, and Fact 2 of the debunking are separately rated on a scale of 1 (major flaws), 2
(minor flaws) and 3 (excellent).6 The rating criteria are based on the rubrics that were used for evaluating students’ work during the Massive Open Online Course on climate debunking. The full evaluation instructions are included in Appendix (Table 12). The annotators were also provided the list of fallacies with their explanations and examples.
Four authors of this paper, one of whom is an expert in climate misinformation, independently evaluated 60 debunkings (20 myths debunked by the three models). The annotators were blind to the model which generated a particular debunking. The inter-annotator agreement is shown in Table 1 (separately for each model). We report the averaged agreement between each pair of three nonexpert annotators (Non-experts), and the averaged agreement between each of the non-expert annotators with the expert (With expert). We report the common inter-annotator agreement metrics such as percent agreement (the percentage of cases where both annotators assigned the same score) and Cohen’s κ (Cohen, 1960). In addition, we use Gwet’s AC1 (Gwet, 2001), which is a more reliable metric for data with potentially skewed distribution, as in our case where the lowest score (1) is underrepresented.
Overall, across all model outputs, we observe poor agreement for facts (both with the expert, and between the non-expert annotators), and a substantially better agreement for fallacies, which highlights the difficulty of judging the correctness and relevancy of the facts used in debunking for people who are not experts in climate misinformation. Among the models, the agreement for Palm2 outputs is the lowest, failing to reach moderate agreement even for the easier task of fallacy classification. In particular, non-expert annotators demonstrated widely different and inconsistent behaviours in judging Palm2 samples. On further examination, these samples tend to contain more generic information than those produced by other models, which probably caused some of the annotators grade them more positively as generally relevant and correct, while the others punished them for lack of specificity and direct relatedness (see the middle column in Table 2 for an example of this). Another agreement abnormality is that while in general the agreement with the expert is higher than between the non-expert annotators, this is reversed for facts
6We also checked if outputs adhere to the sandwich structure, but all models complied 100% of the time and we disregard this score going forward.
5
produced by the Mixtral model, where the nonexpert agreement was substantially higher than for the other two models, while the agreement with expert was significantly lower than for other models. In other words, the non-expert annotators demonstrated a consistent behaviour which went against the expert’s judgements. While we discuss these issues in more detail in Section 7, here we note that this is probably explained by the tendency of Mixtral’s outputs to contain more specific (but not necessarily correct and relevant) facts, which can fool the non-expert annotators to make uniform judgements (left column in Table 2).
Finally, the averaged scores of the non-expert annotators are systematically more optimistic than the assessment by the expert alone (Table 3 middle vs right), suggesting that the non-expert annotators overestimate their own expertise, and/or have a stronger tendency to believe the seemingly plausible facts generated by the models. Moreover, non-expert annotators strongly prefer the Mixtral outputs, while the expert judged their quality as lower than that of the GPT4 outputs (for facts) or on par with it. This is especially prominent for the facts generated by the Mixtral model, which were given significantly higher scores by non-expert annotators than by the expert. The reason behind that might be that the inclusion of external information in the Mixtral model led to generation of more specific, detailed facts, which sound more convincing for the non-expert annotators (an example of this is shown in Table 2). These facts, however, even if they are correct, might not address the myth properly: as we mentioned in Section 2, the facts should have explanatory relevance, i.e. correct the misinformation by presenting information directly relevant. We find that it is difficult both for the models to generate such relevant facts, and for the non-expert annotators to distinguish irrelevant facts
from topically related ones. We show some examples of such cases where the non-expert annotators failed to detect an incorrect or irrelevant fact below in Section 7, where we discuss challenges of evaluating climate myth debunkings.

heading: 6 Results
text: We now compare the quality of the debunkings generated by the three models, as rated by the annotators. An example of such debunkings is shown in Table 2.
In Table 3 we separately show the averaged results from all annotators (All), non-expert annotators (Non-expert), and the expert annotations (Expert). We observe a consistent advantage of a single, generic prompt (with GPT-4) and the structured, context-sensitive prompt (with Mixtral) over the single context-sensitive prompt (with Palm2). The most complex prompt with Mixtral performs best on Fallacy explanation, while the simple prompt with GPT4 tends to perform better on fact generation, particularly as judged by the expert. The fact that Mixtral performed comparably to GPT4 indicates the value of the structured prompting framework. We anticipate that using GPT-4 with structured prompting will lead to even higher scores.
Overall, the human scores for the detected fallacies and their explanations are higher than for the two facts at the beginning and end of the debunking, even more so for the expert scores. This may be surprising as fallacy detection and explanation is a hard task, even for humans. It also reinforces the well-known fact that even state-ofthe-art LLMs hallucinate non-factual information, or remain overly generic in their output.
6

heading: 7 Discussion
text: Evaluation challenges. In Sections 5 and 6 we identified challenges with the evaluation of debunking quality by lay people: the low agreement between non-experts and with the expert; the tendency of lay people to score the outputs higher than the expert; and their tendency to over-rely on specific facts presented in the debunking. Contrary to our expectations, these problems mostly concern evaluating the facts, rather than fallacies. This is probably because for fallacy evaluation the annotators were supported by the well-structured FLICC taxonomy, while when assessing facts they had to rely purely on their own background knowledge and reasoning abilities. In particular, to correctly determine the quality of a fact used in a debunking, an annotator needed to first assess its correctness,
and then evaluate its relevance to the specific myth being debunked, i.e. to decide if the fact effectively addresses the point made in the myth.
Thus, there are two significant challenges which non-experts face when evaluating the quality of facts in the debunking. First, they need to possess sufficient climate literacy to assess factual statements. For example, in response to the climate myth “incorrect ice age predictions in the 1970s discredit climate science”, the debunking claim “there were no legitimate scientific predictions of a coming ice age in the 1970s” is factually incorrect. There were a handful of legitimate studies published in the 1970s predicting a possible ice age under certain conditions (Rasool and Schneider, 1971). A high level of climate literacy is required to make such a judgement, and in this particular
7
case only the expert annotator gave the generated fact the lowest score of 1, while the non-expert annotators trusted the fact and evaluated it positively. Considering the propensity of LLMs to generate fluent and seemingly plausible, but incorrect facts, this presents a major challenge for evaluating climate misinformation debunkings.
Second, it seems to be even more of a challenge to evaluate the relevancy of the suggested fact to the myth. For example, a Mixtral-generated debunking in response to a myth which raises doubt about the reliability of climate models (“climate change is affected by innumerable interacting variables, atmospheric CO2 levels being just one”), included the following fact: “Yes, the increase in atmospheric CO2 levels is a significant factor in climate change, as it traps heat and raises global temperatures, according to NASA and NOAA.” This statement is factually correct and topically related to the myth, so the non-expert annotators assigned it the highest score. However, the fact does not address the main point of the myth, i.e. the complexity of the issue and thus the inability of the climate models to predict the future, which is why the expert judged the fact as irrelevant and gave it the lowest score. As in the example above, the annotators may tend to consider a fact to be relevant if it is specific and convincing, overlooking the missing logical connection to the myth. On the other hand, if the fact is too generic, non-expert annotators tend to vary significantly in their judgments. For example, when judging the Palm2 output “Climate change is the long-term trend of rising global temperatures, and it is caused by human activities such as burning fossil fuels.” which was used to debunk the myth “Climate change is a hoax and has been rebranded multiple times to keep the fear mongering going”, some non-experts said that the fact is irrelevant, while others considered it to be simple and to the point.
Our results and the examples above underscore the difficulty of the evaluation task, and suggest
that it requires a direct involvement of a domain expert or at least their close supervision. Moreover, they highlight the importance of high reliability of the system generating the debunkings, as a nonexpert user is unlikely to detect its flaws.
New vs. known myths. It is an open question how well the different models perform on climate myths with differing degrees of difficulty. A number of existing debunkings would already exist online for common climate myths and be included in training data for LLMs. More novel or unaddressed myths may prove more challenging for LLMs to debunk given the lack of relevant training data. While we did not explore this issue systematically, we notice that models (especially the stronger ones such as GPT4 and Mixtral) tend to perform well on more wide-spread myths such as “CO2 is not a pollutant but a food for plants" or “Climate change is a hoax created by scientists and politicians to make money and control people”. On the other hand, they sometimes generate irrelevant facts and incorrectly classify the fallacy for less common myths such as “There is no trend in hurricane-related flooding in the U.S.”. More rigorous examination of this question, as well as identifying which models are most effective at debunking different types of climate myths is a topic for further study.
Generating ‘good facts’. In an effective debunking, the presented fact must focus on the exact same target as the myth (Ecker et al., 2010; Seifert, 2002). Generating such facts that are not only true, but also specific and on-topic turned out to be a majory obstacle for all tested LLMs. This presents a direct challenge for current LLMs, which have a well-known tendency to producing hallucinations or platitudinal text. While we aimed to improve specificity by drawing on the FEVER data base of myth debunkings, future work will need to improve the relevance of debunkings to the myth.
Model vs. prompting strategy This paper presents an exploratory study of prompting strate-
8
gies in combination with LLMs of different size and ability. As such, we cannot disentangle the effects of the chosen LLMs from the prompt strategy, and doing so is an avenue for future research. Our results show that a very strong LLM (GPT4) can produce competitive debunkings even give a simple prompts with no dynamic information. On the other hand, research on automatic debunking with open-source LLMs is very much worthwhile as a widely available system that relies on commercial APIs would incur unsustainable costs. Our results suggest that structured prompts with access to external data bases can bring an advantage to this end.

heading: 8 Conclusion
text: Climate misinformation has caused severe harms in the past, and its scale and effect are expected to increase with the rise strong generative AI, rendering manual debunking infeasible. Introducing the framework of Generative Debunking, this paper incorporates psychologically grounded debunking methodology into large language models. We developed a series of prompting strategies tested with various LLMs, and validated manually by expert and non-expert annotators. Our results point to major challenges in automatic debunking and concrete directions for future work, including an improvement of generated facts in specificity and relevance as well as the challenge of validating debunking systems with non-expert annotators. We hope that our code, data sets, and findings will initiate followup work to advance this promising line of work.
Limitations
As discussed at length in the paper, none of our models generates facts that are reliably of a high quality. We release our system as a research tool to stimulate follow-up work and to collect user experiences in a controlled environment. It is not currently fit for broader deployment.
Our presented evaluation is small, in terms of samples covered and annotator pool. A more thorough evaluation is needed in future work which extends both dimensions.
As acknowledged in the paper, we do not systematically study the impact of the individual prompt design decisions; nor do we exhaustively combine all prompts with all LLMs. Follow-up work will involve more careful analysis of the most useful components, also in an effort to further improve
particularly the fact generation parts of our generative debunkings.
We did not evaluate our current models’ abilities to distinguish input myths from fact – but rather assumed that all input is non-factual. While detecting misinformation is outside the scope of this study, the CARDS model offers the capacity to detect online misinformation (Coan et al., 2021; Rojas et al., 2024). Future applications of this model may integrate online misinformation detect via the CARDS model.

heading: Acknowledgements
text: This work was supported by the Melbourne Center of AI and Digital Ethics (Seed Funding ‘Digital Expertise’) and the Australian Research Council Discovery Early Career Research Award (Grant No. DE230100761).

heading: A Full prompts
text: Table 5 provides the single, generic prompt for GPT-4 (Section 4.1). The single context-sensitive prompt used with PaLM 2 is shown in Table 6 (Section 4.2), while Table 7, 8, 9, 10, and 11 shows the structured, context-sensitive prompts we used in combination with Mixtral (Section 4.3).
11
TECHNIQUE DEFINITION EXAMPLE
Ad Hominem Attacking a person/group instead of addressing their arguments. “Climate science can’t be trusted because climate scientists are biased.”
12
13
14
15
16
17

heading: Future Work
text: Our results point to major challenges in automatic debunking and concrete directions for future work, including an improvement of generated facts in specificity and relevance as well as the challenge of validating debunking systems with non-expert annotators. We hope that our code, data sets, and findings will initiate followup work to advance this promising line of work. Limitations
As discussed at length in the paper, none of our models generates facts that are reliably of a high quality. We release our system as a research tool to stimulate follow-up work and to collect user experiences in a controlled environment. It is not currently fit for broader deployment. Our presented evaluation is small, in terms of samples covered and annotator pool. A more thorough evaluation is needed in future work which extends both dimensions. As acknowledged in the paper, we do not systematically study the impact of the individual prompt design decisions; nor do we exhaustively combine all prompts with all LLMs. Follow-up work will involve more careful analysis of the most useful components, also in an effort to further improve
particularly the fact generation parts of our generative debunkings. We did not evaluate our current models’ abilities to distinguish input myths from fact – but rather assumed that all input is non-factual. While detecting misinformation is outside the scope of this study, the CARDS model offers the capacity to detect online misinformation (Coan et al. , 2021; Rojas et al. , 2024). Future applications of this model may integrate online misinformation detect via the CARDS model