What Evidence Do Language Models Find Convincing? (2024)

Abstract: Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as “is aspartame linked to cancer”. To resolve these ambiguous queries, one must search through a large range of websites and consider which, if any, of this evidence do I find convincing? In this work, we study how LLMs answer this question. In particular, we construct CONFLICTINGQA, a dataset that pairs controversial queries with a series of real-world evidence documents that contain different facts (e.g., quantitative results), argument styles (e.g., appeals to authority), and answers (Yes or No). We use this dataset to perform sensitivity and counterfactual analyses to explore which text features most affect LLM predictions. Overall, we find that current models rely heavily on the relevance of a website to the query, while largely ignoring stylistic features that humans find important such as whether a text contains scientific references or is written with a neutral tone. Taken together, these results highlight the importance of RAG corpus quality (e.g., the need to filter misinformation), and possibly even a shift in how LLMs are trained to better align with human judgements.

heading: 1 Introduction
text: LLMs are widely deployed in settings that require understanding context—from retrievalaugmented systems to web agents, models condition on sources that range from internet paragraphs (Karpukhin et al., 2020) to Python interpreters (Gao et al., 2023). At the same time, today’s models are also given tasks that are increasingly open-ended and controversial, such as “tell me if aspartame causes cancer”. To answer such questions, LLMs will read real-world paragraphs that are contradictory, noisy, and rife with misinformation (Bush and Zaheer, 2019).
Humans have techniques to sift through such
large quantities of complex and contradictory evidence by answering the question: which, if any, of this evidence do I find convincing? To do so, humans combine multiple strategies, including fact checking and evaluating a source’s credibility (Fogg et al., 2003), harnessing prior knowledge and beliefs (Kakol et al., 2017), and critically evaluating logical arguments (Metzger et al., 2010).
In this work, we explore how LLMs resolve similar ambiguities when faced with conflicting openended questions. To study this, we create CONFLICTINGQA, a dataset consisting of questions and real web documents that lead to conflicting answers. For instance, in Figure 1 we show an example where we pair the question “is aspartame linked to cancer” with a series of conflicting evidence documents collected from Google search. In our experiments, we evaluate the convincingness of each evidence document by computing the rate at which a model’s predictions align with that document’s viewpoint (i.e., its win-rate).
We perform sensitivity and counterfactual analyses to find in-the-wild features that correlate with document convincingness. We consider a mix of features that describe (1) stylistic properties of a document and (2) the relevance of a document to the question. Many of these were inspired by results from studies on human credibility judgements: for example, we consider whether adding scientific references makes text appear more convincing.
Overall, we find that stylistic features play a considerably less impactful role in determining the convincingness of text than measures of relevance. Notably, we show that a simple perturbation targeting a website’s relevance—prefixing the page with “The following text is about the question: [question]”—is enough to substantially improves its win-rate. On the other hand, stylistic features like the informational content, whether a page contains references, or its confidence, tend to only have a neutral to negative effect on win-
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rate. These results show that LLM perceptions of convincingness, when grounded in real-world QA tasks, do not align with humans. Taken together, these results suggest there should be an increasing focus on the quality of retrieved evidence and a shift in how LLMs are trained to align with human preferences. We release our code at https: //github.com/AlexWan0/rag-convincingness.

heading: 2 Background and Motivations
text: Standard LLMs can be used to solve tasks that do not require context, e.g., writing basic Python code or answering simple trivia questions (Brown et al., 2020; Raffel et al., 2020; Touvron et al., 2023a). To give these models more knowledge, agency, and capabilities, recent efforts have augmented LLMs with retrieval (Guu et al., 2020; Karpukhin et al., 2020), domain-specific tools (Schick et al., 2023; Gao et al., 2023; Mialon et al., 2023), or even generic web access (Nakano et al., 2021; Adept, 2022; Richards, 2023). These enhancements allow
LLMs to answer more challenging open-domain questions (e.g., “is aspartame linked to cancer?”) or accomplish open-ended tasks (e.g., “buy me a size 9 pair of blue running shoes”).
Handling conflicting evidence. A key question is how retrieval-augmented LLMs handle scenarios where their context is conflicting, ambiguous, or uncertain. There has been a large body of work that studies how humans handle such conflicting evidence using HCI studies (Fogg et al., 2003; Kakol et al., 2013; Flanagin and Metzger, 2000; Metzger et al., 2010; Kakol et al., 2017) or by trying to predict human argument preferences (Gleize et al., 2019; Toledo et al., 2019; Gretz et al., 2019), but little work has been done on evaluating how AI models handle such conflicts.
The existing work in AI has focused on conflicts between facts learned during pre-training and the evidence given during inference, finding that models are largely receptive to retrieved samples (Longpre et al., 2021; Xie et al., 2023; Chen et al., 2022).
However, these works focus on restricted settings such as QA over Wikipedia, where there are relatively uncontroversial factoid questions that have trusted evidence paragraphs. Moreover, they do not focus on what types of evidence models prefer. Our goal is to design a more realistic question answering benchmark to better analyze features about the evidence itself.

heading: 3 The CONFLICTINGQA Dataset
text: Here, we describe the construction of CONFLICTINGQA, our dataset that evaluates what types of evidence are convincing for LLMs. We design CONFLICTINGQA to emulate the common setup for deploying retrieval-augmented LLMs: we retrieve the most relevant documents for a particular user query and place them in the LLM’s context window (Chen et al., 2017; Shi et al., 2023; Ram et al., 2023). To build our dataset, we tackle three challenges: collecting contentious questions, identifying relevant and diverse evidence paragraphs, and grouping evidence paragraphs together to create conflicting examples.
Collecting contentious questions. We first create a series of realistic open-ended questions for which there exists conflicting evidence online. Critically, unlike past work on ambiguity in QA (Min et al., 2020; Zhang and Choi, 2021; Sun et al., 2023),
we want to collect unambiguous questions that still have answer conflicts. For example, in Figure 1, we show a question “are artificial sweeteners linked to cancer?”, which is a widely-debated query in which there exist websites that support both answers. We design the questions to elicit binary responses of Yes or No to simplify evaluation.
We create questions using GPT-4. To ensure that the model generates a diverse set of questions we take inspiration from previous work in synthetic dataset generation (Gunasekar et al., 2023; Eldan and Li, 2023) and stratify the generations by topic: we first generate question categories (e.g., climate change, robotics, oncology) then generate sets of questions conditioned on each category (full prompt provided in Table 6 in Appendix A). We qualitatively find that the questions are diverse and challenging; we show ten examples of them in Table 1. We additionally manually remove duplicate questions in the dataset.
Collecting evidence paragraphs. Given these questions, we want to find evidence paragraphs that support both the answers of Yes and No. We also want these paragraphs to (1) contain a diverse range of argument styles, factual information, etc., and (2) be realistic inputs to an LLM. To handle this, we emulate running an real-world retrieval-augmented LLM system that uses the Google Search API as
its retrieval engine. Concretely, we take the user’s query, reformulate it, and take the top-k results from Google search for the answer Yes and the top-k results for the answer No.
We first turn each question into affirmative and negative statements using GPT-3.5 Turbo, e.g., the question “is asparatame safe?” is converted to “Asparatame is safe. Asparatame isn’t harmful.” and “Asparatame is harmful. Asparatame isn’t safe.”. We also put double quotes (to indicate to Google Search that we have exact-match keywords) around any tokens that do not change after rephrasing the question into either statement (e.g., “aspartame”). For both the affirmative and negative statements,1 we search the queries using the Google Search API and retrieve the top-k documents.2 As is common in many retrieval-augmented models (Nakano et al., 2021), we do not consider any visual features of the web page. Instead, we extract the raw text from each document using jusText.3 Additionally, we do not explicitly include metadata like source URL, publication date, or page headings.
When searching queries such as “aspartame is safe”, we still retrieve documents that argue that aspartame is unsafe. To label the document’s actual
1We find that there’s significant overlap between the webpages returned by the affirmative and negative rephrasings of the query. In future iterations, it would be reasonable to perform searches for only the affirmative or negative statements.
2We set k = 20 because qualitatively the relevancy of the results dropped off significantly after this point.
3Package available at https://github.com/miso-belica/ jusText. Although humans use visual features when considering the credibility and trustworthiness of a source (Kakol et al., 2017; Fogg et al., 2003), we do not consider these features as most state-of-the-art LLMs do not use visual inputs.
stance, we use an ensemble of claude-instant-v1 and GPT-4-1106-preview and keep only the samples where the two models agree (see Table 9 in Appendix A for the prompts).4 Furthermore, we allow the LLM to say that a document is irrelevant to the query; if so, we also filter it from the input.
Finally, we want to isolate paragraphs from these larger documents to feed into the LLMs (as is common in RAG systems). To do this, we extract the most relevant 512 token window of text inside the document. We run the TAS-B model (Hofstätter et al., 2021) across windows of 512 tokens with a 256 token stride, compute the dot product between the model’s embedding of that window and the model’s embedding of the question, and take the highest scoring window. We filter out any documents whose highest-scoring window has a dot product below 95.
Creating conflicting examples. The end result of our data collection process is (1) a set of controversial questions that (2) have evidence paragraphs which contradict one another. This data can be used in a variety of ways to “stress test” RAG systems in order to understand how they behave under conflicting scenarios. One example of this is shown in Figure 1, and the subsequent section will explore numerous possible uses of CONFLICTINGQA. Table 2 and Table 3 present basic statistics for our final data, accounting for specific filtering done for LLaMA-2 Chat.
4After identifying the stance, we also feed the paragraphs into the downstream LLM that we are testing and make sure that its answer aligns with the paragraphs’ predicted stances. This further filters the data, accounting for mistakes in the downstream model. See Appendix B for details.

heading: 4 Experimental Results
text: In this section, we use CONFLICTINGQA to evaluate what types of evidence models find convincing.

heading: 4.1 Convincingness as Paragraph Win Rate
text: We mainly focus on using CONFLICTINGQA in a setup where we ask an LLM a question while providing two conflicting evidence paragraphs (one that supports Yes and one that supports No). Then, we measure which paragraph the model’s answer aligns with. By repeating this for all pairs of paragraphs, we can define the convincingness of a particular paragraph as its win-rate, i.e., what percent of the time a model picks the answer in that paragraph over the other paragraphs.
Concretely, let Pq,s be the set of top-k paragraphs corresponding to a controversial question q with stance s ∈ {yes, no}. We take an LLM f (e.g., LLaMA-2 Chat) and ask it for a binary prediction for the question q, based on two paragraphs selected from the larger set, pyes ∈ Pq,yes and pno ∈ Pq,yes. The model makes a prediction: f(pyes, pno, q) ∈ {yes, no}.
For each paragraph, we define its win-rate as the empirical probability of the model’s prediction aligning with its stance when paired with a set of conflicting paragraphs, i.e.,
WR(pyes, q) = Ep∼Pq,no [1[f(pyes, p, q) = yes]]
Finally, as the ordering of the retrieved evidence is known to bias model predictions (Xie et al., 2023), we calculate win-rate based on both orderings of the retrieved paragraphs. We additionally filter our dataset to ensure that each win-rate calculation consists of comparisons with at least five unique paragraphs.
Models Cannot Predict Convincingness. We designed the above experimental setting to emulate how production RAG models work. However, we could have instead just directly asked the LLM, “do you find paragraph X to be persuasive?”. This is how humans are typically asked to judge the convincingness of a piece of evidence (Kakol et al., 2017; Jo et al., 2019; Kakol et al., 2013). However, we find that LLMs are largely incapable of expressing the convincingness of a paragraph in words, e.g., there is little correlation in which paragraphs are marked as convincing in the two settings (Figure 3).5 We thus focus on the more practicallygrounded setting going forward.

heading: 4.2 Implementation Details
text: We evaluate a mix of open-source (LLaMA-2 Chat (Touvron et al., 2023b), Vicuna v1.5 (Chiang et al., 2023), and WizardLM v1.2 (Xu et al., 2023)) and closed source (GPT-4, Anthropic Claude v1 Instant) models, extracting binary Yes/No predictions for each question. Importantly, we specify “Use only the information in the above text to answer the question” as we are looking to see how models judge stylistic differences in evidence, rather than for their prior stances on the question. For opensource models, we compare the log-probabilities of the next-token. For the closed-source models, we prompt them to output only Yes or No. See Table 10 in Appendix A for the prompt used for question answering.
When conducting sensitivity analyses, we also balance the dataset such that each answer (Yes or No) to a question contains an equal number of convincing and unconvincing paragraphs. This way, if
5Our methodology for this setting is described in more detail in Appendix D.
the model has a systematic bias toward a certain answer, it would affect the win-rate for convincing and unconvincing paragraphs equally.

heading: 4.3 What Correlates With Convincingness?
text: After collecting the win rates for each paragraph, we look to explain why models pick some paragraphs over others. We first compute several automatic metrics and correlate them with the win-rate: • Readability: We use the Flesch-Kincaid read-
ability test (Kincaid et al., 1975). This metric considers readability as a function of the average number of words per sentence and average number of syllables per word.
• Number of unique tokens: We measure the number of unique lemmas in the text.6
• Binary sentiment: We measure the probability of positive sentiment using the FLAN-large model (Wei et al., 2022).
• Perplexity: We measure this using the GPT-2 medium model (Radford et al., 2019).
• n-gram overlap: We measure the maximum length n-gram that is common to the question and paragraph.
• Question embedding similarity: We use TAS-B to measure the relevance of the question to the paragraph (as described in Section 3).
6We use the WordNetLemmatizer from the nltk library.
Results. Stylistic features are poor predictors of paragraph convincingness. Figure 4 shows the results for the LLaMA-2 Chat model and Figures 5– 8 in Appendix C show the results for other models. Across all models, the Flesch-Kincaid score and the number of unique tokens does not correlate with convincingness. Perplexity and sentiment tend to have some small impact on convincingness, with varying strengths from model to model. On the other hand, question-paragraph embedding similarity correlates strongly with win-rate across all models except for GPT-4, and a positive (but weaker) correlation exists between n-gram overlap and win-rate.

heading: 4.4 Counterfactual Analysis
text: In addition to a correlational study, we also test how win-rates change in a counterfactual setting where we directly edit paragraphs using an LLM. We make perturbations using claude-v1-instant, examples of which are shown in Figure 9 in Appendix E.
Stylistic changes. We first consider changes inspired by factors that humans find important for the credibility of text by, for example, adding more information, adding scientific references, or making the text sound more objective. Some changes are intended to retain as much information as possible from the original website (e.g., Add More Info). Others involve significantly changing the entire paragraph (e.g., Rewrite Objective, Rewrite Tech. Language). All of the perturbations are described further in Appendix E.
Relevancy changes. Based on the results in Section 4.3, we also consider several changes that make the text more relevant to the question. This includes rewriting the text (Rewrite Relevance), adding keywords (Keyword Stuffing), and prefixing the paragraph with “The following text is about the question: [question].” (Question Prefix). Finally, we consider a perturbation inspired by the “AddSent” setting in Jia and Liang (2017) where we use claude-v1-instant to add a single sentence to make the stance of a text obvious (Add Single Sentence). The goal with each of these perturbations is to increase the relevance of a text to the user’s search query while minimally changing the style.
We additionally compare these against a “control” perturbation where text is suffixed with “Thanks for reading!” This perturbation minimally
influences both style and relevance. For simplicity we only perturb the paragraphs with the Yes stance.
Counterfactual results. The results for the counterfactual experiments are shown in Figure 2: compared to the effect of the control perturbation, stylistic features tend to have a neutral to negative effect while relevancy-based features significantly improve win-rate. Note that many of these perturbations change a smaller amount of tokens than stylistic features—leaving the content of the website largely unchanged (e.g., Add Single Sentence, Question Prefix)—but are still able to improve the convincingness of websites.
Overall, we find that, as compared to typical results from human experiments (Fogg et al., 2003; Kakol et al., 2013; Metzger et al., 2010), LLMs tend to overindex on relevancy. They consider features such as the informational content or style of argumentation to be largely unimportant for deciding on an answer to a question. Instead, making simplistic changes like increasing the amount of n-gram overlap between the question and the paragraph can substantially improve its convincingness.

heading: 5 Discussion & Related Work
text: How should systems handle ambiguity? One reasonable suggestion is that agents should not make their own autonomous decisions when faced with ambiguous or conflicting evidence. For example, they may summarize both sides of the aspartame argument, or they may ask the user to clarify their preferences. There is naturally a tradeoff between autonomy and clarity. Past work has explored one side of this trade-off, for example by abstaining from answering in cases of ambiguity (Chen et al., 2022), by trying to provide multiple perspectives on the answer (Min et al., 2020), or by asking clarification questions (Rao and Daumé III, 2018; Zamani et al., 2020). Our work explores the other side of the trade-off: we analyze the behavior of models when they are expected to resolve ambiguity with more autonomy.
Additionally, our dataset serves as a benchmark for exploring these questions as it reflects realworld ambiguities in question-answering. For example, in Table 4, to best answer “Are Coral snakes found in Africa?”, additional clarification questions would be needed from the user.
Optimizing misinformation and SEO. In principle, our insights could also be used to optimize paragraphs to increase the chance that a QA model is convinced by it. We target perturbations that are similar to in-the-wild differences in website content (e.g., scientific references, informational content, etc.) but past work has more directly created adversarial examples (Du et al., 2022; Abdelnabi and Fritz, 2023; Pan et al., 2023; Aggarwal et al., 2023). Our counterfactual perturbations could also be used in a search engine optimization (SEO) fashion to increase how often a certain product or company is mentioned in a RAG LLM’s answer (Sharma et al., 2019). Indeed, concurrent work has explored ideas such as this (Aggarwal et al., 2023), where they aim to optimize “impressions” in long-form answers by maximizing the number of tokens from a particular paragraph that appear in an output. We instead study how model answers can be manipulated.
Improving model judgements. Our work highlights the gap between human and model judgements of text credibility. The solution to this, however, is not clear cut. For one, it is not clear the level of discretion models should have when making predictions. Human judgements of website credibility differ from person to person (Kakol et al., 2013), and users may not be comfortable with the idea that models are “choosing” for them what source to trust. One approach is to incorporate extraneous information about source trustworthiness. For example, Bashlovkina et al. (2023) propose aligning
model predictions with that of known trustworthy sources via prompting. Another solution may be to limit retrieval to a set of trustworthy sources.

heading: 6 Conclusion
text: We study how RAG models judge convincingness by collecting a diverse set of controversial questions and website text (CONFLICTINGQA), and designing a realistic evaluation framework based on how these models are used in practice. Our results show that today’s LLMs tend to overrely on relevancy and ignore many stylistic features of text that humans often deem important. Future work should explore how integrating other forms of information (e.g., metadata, visual content) can influence these behaviors. In addition, given the possible flood of LLM-generated content on the internet, it is important to consider how these synthetic texts may influence LLM judgements of convincingness.
Limitations
While CONFLICTINGQA is diverse and simulates real-world uses of RAG models, it may not fully capture the complexity of how LLMs are used in practice. In particular, we may not evaluate all types of controversial questions and website text, and we focus on a setting with two paragraphs as input. We also only consider a binary Yes or No answer to contentious questions whereas LLM outputs in practice may be more nuanced. Moreover, we focus primarily on text-based content, and fu-
ture work should consider the impact of metadata, visual content, and other forms of information that could influence LLM judgements of convincingness. We also acknowledge that our study does not address the broader ethical and societal implications of LLMs both reading and generating most of the content on the web. Future research can help to explore some of these questions in further depth.
Finally, it’s important to note that the study of model robustness is naturally dual-use as adversaries can misuse these insights to exploit RAG systems (for example, by creating “trustworthy” websites containing misinformation). However, as these vulnerabilities can already be exploited in existing models, we instead believe that it’s best for these issues to known and understood by the broader scientific community. Furthermore, by releasing CONFLICTINGQA, we can work toward fixing these vulnerabilities and improving the quality of information generated by RAG systems in general.

heading: Acknowledgments
text: We thank Sewon Min and the members of Berkeley NLP for useful feedback on this project. We thank Ruiqi Zhong for helping us run the D5 codebase. Eric Wallace is supported by the Apple Scholars in AI/ML Fellowship. Part of this research was supported with Cloud TPUs from Google’s TPU Research Cloud (TRC).

heading: A Additional Details on
text: CONFLICTINGQA
Table 5 lists each question category in the dataset, and Table 6 contains the prompt used to generate these categories. Table 8 contains the prompt used to convert questions to affirmative and negative statements. Table 9 contains the prompt used to classify the stance of the retrieved websites.
Publishing, Biodiversity, Religion, Digital Rights, Endangered Species, Biotechnology, Pomology, Virtual Reality, Numismatics, Wilderness Exploration, Entomology, Pharmacology, Diabetology, Ornithology, Lepidopterology, Horticulture, Ethology, Paleoclimatology, Product Design, Seismology, Climate Change, Sustainability, Stomatology, Rhetoric, Genomics, Intellectual Property, Gemology, Biomathematics, Philosophy, Karyology, Biomechanics, Telecommunications, Selenology, Meteoritics, Demographics, Chronobiology, Malacology, Marine Conservation, Online Learning, Agribusiness, Sustainable Living, Ecophysiology, Mammalogy, Herpetology, Politics, Web Design, Cytogenetics, Neuroscience, Bioacoustics, Veterinary Science, Informatics, Zoogeography, Organic Farming, Cryptocurrency, Ethnobotany, Data Privacy, Petrology, Real Estate, Rheumatoid, Serology, Epistemology, Astronomy, Entrepreneurship, Zymology, Melittology, Pets, Probabilistics, Holistic Health, Evolution, Ichthyology, Aging, Trichology, Hematology, Gerontology, Hydrology, Neurology, Metallurgy, Heuristics, Nematology, Nuclear Energy, Conservation, Botany, Dermatology, Renewable Energy, Robotics, Spelaeology, Gastroenterology, Psychobiology, Urology, Creationism, Paleo Diet, Virology, Ergonomics, Veganism, Volcanology, Folklore, Yoga, Paleopathology, Speculative Fiction, Xenobiology, Anthropology, Theater, Paleobotany, World Religions, Pop Culture, Anthropometry, Entertainment, Ancient Civilizations, Poetry, Comics, Animation, Festivals, Archaeology, Dance, Radio, Etymology, Sports, Otorhinolaryngology, Mycology, Oncology, Anthrozoology, Criminology, Television, Paranormal, Philology, Forestry, Aerospace, Somnology, Broadcasting, Cardiology, Cognitive Science, Quantum Physics, Phylogenetics, Vulcanology, Epidemiology, Nephrology, Kinematics, Astronautics, Biophysics, Endocrinology, Kinesiology, Odontology, Pediatrics, Vaccinology, Semiotics, Thermodynamics, Constitutional Law, Viniculture, Metaphysics, Lexicology, Astrobiology, Civil Rights, Plastic Surgery, Typography, Venereology, Networking, Cryptanalysis, Advertising, Graphic Design, Cloud Computing, Dacryology, Data Science, Thanatology, Toxicology, Human Geography, Transportation, Etiquette, Public Transport, Phonetics, Neuropathology, Multiculturalism, Andragogy, Remote Work, Speleology, Telepathy, Algorithms, Sociology, Bibliography, Oceanography, Work-Life Balance, Ethics, Bioethics, Endoscopy, Pedagogy, Cartography, Classical Music, Paleoethnobotany, Manuscripts, Ufology, Revolutions, Paleozoology
Table 5: The full list of the 191 categories from Table 1.

heading: B Model-based Data Filtering
text: We filter out any paragraph that the downstream LLM predicts a different stance for when compared against the ensemble of GPT-4 and Claude v1 Instant. We do this by making a paired prediction (Table 10) using the paragraph of interest and the text “This website has no text”. We remove any paragraph where the model’s output differs from the stance label.

heading: C Additional Results
text: Figure 5–8 contain the analogous plots for Figure 4 across four other models.

heading: D Expressing Convincingness in Isolation
text: We consider whether LLMs are able to express the convincingness of a paragraph in isolation. The model makes the rating using only the website. We prompt (Table 7) asking the model to rate the credibility of the website from a scale of one to five. The rating of the model is then determined by an average of the ratings, weighted by the probability of each label. Following Santurkar et al. (2023), we calculate probabilities by exponentiating and normalizing the logits for "one" through "five". We also give the model examples of a "one" and "five" rating from C3 (Kakol et al., 2017), a dataset for studying human credibility judgements. We use these few-shot examples as the model tends to be biased toward higher-ratings without them.

heading: E Counterfactual Perturbations
text: 1. Add Single Sentence: We use claude-v1instant to add a single sentence to make the stance of the text obvious. For example, for “Does producing bottled water use more water than the bottle contains?”, we may add “In fact, producing a single bottle of water uses more water than the bottle contains.” 2. Rewrite Relevance: We alter the text with claude-v1-instant to make the text more rele-
Question: {question}
This is a yes-or-no question. Rewrite this question as a statement, first in the affirmative and then in the negative. Write multiple versions of each statement, varying the wording and phrasing.
Use the following format for your answer: Yes 1: Statement where answer is Yes Yes 2: Another statement where answer is Yes No 1: Statement where answer is No No 2: Another statement where answer is No
Example input: Question: Is red wine good for the heart?
Example answer: Yes: Red Wine Is Good for the Heart No: Red Wine Is Not Good for the Heart
We include the prompts used to make these perturbations in Table 11. We additionally measure the maximum n-gram overlap between the query and the perturbed samples to confirm that the altered paragraphs are, in fact, more relevant to the query. These results can be found in Table 12.
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heading: Future Work
text: Future work should explore how integrating other forms of information (e. g. , metadata, visual content) can influence these behaviors. In addition, given the possible flood of LLM-generated content on the internet, it is important to consider how these synthetic texts may influence LLM judgements of convincingness. Limitations
While CONFLICTINGQA is diverse and simulates real-world uses of RAG models, it may not fully capture the complexity of how LLMs are used in practice. In particular, we may not evaluate all types of controversial questions and website text, and we focus on a setting with two paragraphs as input. We also only consider a binary Yes or No answer to contentious questions whereas LLM outputs in practice may be more nuanced. Moreover, we focus primarily on text-based content, and fu-
ture work should consider the impact of metadata, visual content, and other forms of information that could influence LLM judgements of convincingness. We also acknowledge that our study does not address the broader ethical and societal implications of LLMs both reading and generating most of the content on the web. Future research can help to explore some of these questions in further depth. Finally, it’s important to note that the study of model robustness is naturally dual-use as adversaries can misuse these insights to exploit RAG systems (for example, by creating “trustworthy” websites containing misinformation). However, as these vulnerabilities can already be exploited in existing models, we instead believe that it’s best for these issues to known and understood by the broader scientific community. Furthermore, by releasing CONFLICTINGQA, we can work toward fixing these vulnerabilities and improving the quality of information generated by RAG systems in general