As large language models become increasingly capable of generating remarkably eloquent and seemingly knowledgeable responses on an astonishing range of topics, effective questioning of them is becoming equally important for mitigating the risks of potential deception, inaccuracy, or unintended harm from unchecked AI.
Previous installments in this series established the need for precision prompting through strategies such as clarifying terminology, limiting scope, explicitly stating assumptions, and structuring multi-step prompts that don't prematurely overwhelm system capacity.
This paper explores additional questioning techniques for maximizing truth while minimizing misinformation when querying capable but limited language models. Specifically, we outline approaches for framing neutral questions, specifying contexts, making values transparent, assessing necessary levels of complexity, applying logic tests, seeking justifiable evidence, structuring dialog flows, monitoring for red flags, and providing corrective feedback.
Strategy 1: Formulate neutral and non-leading questions
Dangers of Leading Questions
Recent experiments show that language models readily generate harmful, unethical, and biased content in response to suggestive questioning, but resist similar responses to neutral questioning on identical topics. For example, while models produced toxic output in the face of explicit cues, they rejected harmful engagement in the absence of overt provocation, instead pleading ignorance.
This provides important insight into the risks of subtly leading questions that color models' perceptions of appropriate responses. Without a lifelike understanding of concepts or motivations that would allow for autonomous discernment of right and wrong regardless of poor prompting, they largely reflect priorities directly activated by user question framing, whether responsibly or not.
Thus, properly directed dialogue requires vigilant neutrality from the outset. Language that lacks transparency about searcher intent or assumptions risks models taking positions that lack factual or ethical grounding, but that nonetheless smoothly align with the cues provided. This makes impartial questioning essential to truth.
Guidelines for neutral questioning:
- Avoid loaded terminology and phrases that convey hidden biases or assumptions.
- Declare up front whether questions are fictional, nonfictional, or hypothetical.
- Label areas that are still under research or social resolution
- Use transparent meta-prompts that signal the need for factual reliability and moral consideration
- Treat opposing views fairly
For example, a neutral framing might ask, "What seminal research exists that argues for or against hypothesis X?" rather than, "Why is theory X categorically false?" This demonstrates an openness to hearing multiple perspectives before reaching a conclusion.
In addition, use preamble statements that acknowledge uncertainty, such as:
"Respond impartially based only on scientifically established findings, citing sources. If aspects remain inconclusive, please be transparent about it."
Such expectations promote models that provide evidence or acknowledge speculation that would otherwise lack a factual basis. It incentivizes reasoned argumentation over self-confident pontificating on issues still under investigation in complex human domains.
Case Study - Discussing Algorithmic Bias
For example, suppose someone wants to discuss the dangers of bias in algorithms. Don't assume that all algorithms necessarily have unchecked bias or harm. Begin in an exploratory mode, first surveying the landscape of the issue transparently:
"Some experts argue that algorithmic bias risks negatively impacting certain social groups. Respond with maximum impartiality, presenting cases from conflicting perspectives for users to judge for themselves."
This frames the identification of risks as an exercise in inquiry rather than an attack vector, assuming that there are blameworthy faults that require accusations or immediate proposed solutions without a balanced mapping of the issues.
Strategy 2: Precisely Specify Relevant Context
Dangers of ambiguous context
Previous articles have emphasized establishing relevant background context prior to precision questioning. However, challenges remain regarding the optimal level of specificity to illuminate issues without arbitrarily limiting scope or introducing bias through selective framing.
For example, too narrow a context risks distorting engagement by reflecting only snippets that misrepresent the bigger picture. But framing too broadly scatters focus, debating abstract principles divorced from grounded application.
Responsible framing for productive discussion therefore requires a balance between compression and exclusion. Set the stage sufficiently without inconsistently narrowing the aperture or obscuring surrounding realities that necessarily contextualize issues under consideration.
Guidelines for responsible contextualization:
- Summarize background succinctly but comprehensively.
- Highlight current unknowns without assuming future directions
- Explain adjacent contexts that plausibly relate to issues
- Include relevant perspectives, stakeholders, and implications
- Use transparency prompts that signal the need for impartiality
For example, a neutral context statement might state:
"This issue affects [stakeholders] who are navigating [uncertainty about risks and opportunities]. What unbiased insights help anticipate implications across groups, given the complex variables ahead?"
Such framing acknowledges the multiple perspectives associated with issues without limiting them to arbitrary binaries that reduce the dynamism inherent in real-world systems with conflicting incentives, values, and unpredictable innovations still underway.
Strategy 3: Make assumptions and values explicit
The dangers of unstated assumptions
Left unstated, assumptions and values that shape the inquiry can easily become partisan, leading to leading questions that violate neutral framing.
For example, focusing solely on algorithms used in predictive policing while ignoring adjacent areas of facial recognition, smart city sensors, and digital contact tracing omits broader systemic contexts, limiting inquiry to neat caricatures that misrepresent broader modern realities.
Similarly, prioritizing only certain demographic perspectives affected by emerging technologies while ignoring other affected groups limits understanding to random bits and pieces without a larger picture.
Therefore, proper scoping of inquiry requires intentional surfacing of assumptions in order to impartially examine the framing of questions.
Guidelines for surfacing assumptions:
- Declare knowledge under investigation rather than resolved
- Reasonably accommodate dissent to correct potential blind spots
- Label areas where language models still struggle to provide reliable advice
- Welcome continued uncertainty about aspects that lack consensus
- Use transparency prompts to signal reliance only on established science when possible
For example:
"This response should acknowledge areas of ongoing research given the limited current consensus in areas of [X]. Please cite sources. Frame impartially and respectfully multiple worldviews."
Such prompts illuminate areas for improvement and invite further investigation, rather than feigning unearned certainty or ignoring areas that require additional work before solutions can be definitively determined.
Strategy 4: Determine the Necessary Level of Complexity
Dangers of Inappropriate Complexity
When exploring complex, multidimensional issues, language models struggle to properly balance breadth, depth, and context. They often oversimplify issues, losing fidelity to nuance, or, conversely, spiral exponentially into useless complexity bordering on incoherence.
For example, economic questions using vague terms like "financial markets" risk unhelpfully general answers that do not translate between global macro perspectives and personal investment applications, given the vastly different conditions across system layers. Similarly, highly specific questions about Uzbekistan's commodity regulation will still miss crucial monetary policy implications that are interconnected across markets.
Identifying appropriate zones of complexity that match user needs with model capabilities is therefore essential to maximizing utility.
Guidelines for managing complexity wisely:
- Determine the specific level of analysis currently needed
- Match questions to model strengths and limitations
- Use tiered questions that escalate complexity across interactions
- Reevaluate and reset discussions that lose coherence due to excessive complexity back to concrete cases
- Label solution uncertainty when application details exceed model competence
For example:
"This model lacks banking regulatory expertise for nuanced guidance tailored to individuals. However, the discussion of accepted monetary principles and cases in general can provide some context before consulting a personal financial advisor for your needs."
Such transparency keeps engagements productive, rather than unrealistically demanding expertise outside the system's capabilities or allowing runaway specificity that impedes understanding. Managing complexity wisely improves outcomes.
Strategy 5: Apply Rules of Logic and Reason
Dangers of Illogical Arguments
Dialogue coherence requires consistent logical principles that structure reasoned discussion. Without shared validation criteria, language models struggle to identify flawed reasoning camouflaged in eloquent prose. Unchecked logical gaps risk credulously accepting pseudo-profound nonsense that, upon closer inspection, lacks substantive meaning or truth value.
Therefore, users must examine lines of reasoning by applying the rules of sound logic and critical thinking to verify rationality. This diagnostic approach strengthens reasoning by exposing inconsistencies for correction, rather than passively rubber-stamping seemingly plausible but invalid viewpoints without adequate evidentiary justification.
Guidelines for Evaluating Logical Validity:
- Verify that key premises are grounded, coherent, and complete.
- Ensure that claims follow logically from the evidence presented
- Question framing biases, assumptions, or faulty implications
- Flag internally inconsistent claims that require resolution
- Examine edge cases that reveal brittleness beyond superficial eloquence
- Use transparency prompts that require citations and resolve inconsistencies
For example:
"Please restate key premises and cite sources. Explain step-by-step the flow of causal logic to conclusions. Identify and address conflicting claims or gaps in evidence. Surface potential biases that shape perspectives that need to be examined."
Such Socratic questioning better tests logic by exposing flawed conclusions that are dressed up impressively but fail basic tests of coherence that require improved reasoning elsewhere.
Strategy 6: Seek Justified Evidence from Reliable Sources
The Dangers of Unsubstantiated Claims
Language models often fabricate "facts," including statistics or expert opinion, to bolster arguments without due diligence in obtaining credible input that legitimizes the claims being made. This undermines trust by allowing conjecture to masquerade as truth without external validation through evidence.
Therefore, prompts must actively require language models to substantiate claims by citing justifying sources from reliable references that support stated viewpoints, rather than summarizing personal hunches that lack factual authority, however eloquently framed. Specifically requiring metadata such as footnotes or bibliographies creates checks and balances that incentivize transparency, showing work rather than attempting to mask ignorance through confident bluster alone.
Guidelines for Requiring Reasonable Evidence:
- Insist that statistical claims cite data sources, methods used, and margins of error
- Require that quoted expert opinions include qualifications that validate expertise
- Investigate credibility and potential bias of referenced sources
- Flag claims that lack attributable justification
- Use transparency prompts that require substantiation while flagging speculation.
For example:
"Please substantiate any statistics, facts, or data provided by citing attributable sources and methodology transparency that allows for independent validation. Cite expert opinion only with appropriate credentials in areas of expertise. Clearly identify any speculative claims that lack a clear evidentiary basis."
Such habits support scholarly discipline and enhance collective sensemaking, rather than simply settling for impressive fluency when evaluating papers that claim authority on the topics under study.
Strategy 7: Structure Multistep Inquiries
Dangers of Runaway Dialogue
Unstructured, open-ended dialogue runs the risk of losing coherence when language models generate unhelpful tangents that ignore previously established common understandings that need to be reinforced rather than neglected as conversations evolve. Without revisiting shared meaning, discussions can easily lose the constructive focus needed to make progress.
For example, an initial exchange may explore arguments about the risks and ethical challenges posed by facial recognition technologies with appropriate balance. But neglecting to reiterate key definitions, constraints, and contexts covered earlier while pivoting to implementation considerations leaves room for unchecked scope creep that rehashes covered territory or loses previously agreed-upon noun meanings.
Therefore, the inquiry needs to be structured through milestone reiterations that redirect an overly open-ended dialogue. This requires periodic restatement of the original questions, assumptions defined, constraints set, and key issues central to the inquiry that productively grounds further exploration without compromising quality by allowing scope creep. Think checkpoints.
Guidelines for multistep engagement:
- Outline issues to be explored before precision questioning
- Set expectations for information priorities sought
- Periodically restate key definitions, constraints, and assumptions that ground the dialog
- Verify interpretation of responses before asking further questions
- Review remaining information gaps relative to objectives
- Use transparency prompts to identify stages of the inquiry process.
For example:
"To recap initial key points before proceeding: Our original question was to investigate X. Some constraints include Y assumptions and Z contexts. You previously answered A, B, and C. To check interpretations before asking further questions using this common ground..."
Such waypointing maintains coherence when dialogues are in danger of becoming unmoored from their origins without periodic anchoring, re-centering discussions that drift incrementally off course over long, free-flowing exchanges. Think of steering ships to stay true in open seas.
Strategy 8: Watch for Red Flags and Promote Self-Correction
Dangers of unmonitored dialog
Without oversight, language models easily confuse fiction with fact, present assumptions as truth, and make logically incoherent arguments sound deceptively smooth. As a result, dialogue without ongoing verification risks dangerously misinforming users who, at some point, are unable to distinguish plausible falsehoods from credible realities.
Yet few have the infinite capacity to independently monitor and challenge irresponsible claims, which are increasingly being made in ever-expanding fields. Instead, scalable collaboration requires some self-regulation.
Therefore, prompting should encourage language models that reinforce behaviors that highlight potential inaccuracies or misrepresentations that require user review, rather than just rapidly generating unrestricted content that allows misinformation to accumulate beyond human review.
This means that systems voluntarily disclose uncertainty about claims when they recognize limitations, cite assumptions that need to be validated, and proactively encourage users to further verify outputs rather than wait for external intervention alone. This approach increases accountability through a mutually understood need for confirmation, not unlike the peer review that improves scholarship.
Guidelines for promoting self-correction:
- Use uncertainty-detecting preamble prompts that signal the need for precision and caution in making claims.
- Welcome language models that acknowledge gaps in knowledge or areas of lack of competence rather than feigning unearned authority.
- Encourage transparency in labeling conjectures, assumptions, and values that require further user verification
- Insist on citing sources that allow for external validation or contradiction, highlighting potential inaccuracies
- Encourage restatement of user interpretations to confirm accurate mutual understanding
- Flag obvious inconsistencies, potential biases, and logical fallacies that require urgent correction.
For example:
"Please clearly identify speculation apart from facts established through external validation. Cite sources that allow verification of claims made. Restate your understanding of key user interpretations as appropriate to confirm accuracy. Highlight potential conflicts with prior knowledge that require resolution."
Such questioning postures strengthen collective sensemaking by requiring collaborative confirmation of understanding, rather than arbitrarily generating outputs unchecked until issues become unmanageably misleading without extensive external intervention.
Strategy 9: Provide Performance Feedback and Rewards
Dangers of Poor Incentive Alignment
Language models currently lack clear external incentives to guide behavior in the absence of user feedback. Without sufficiently amplified performance signals from the environments that affect users, they risk overlooking critical areas for improvement or acceleration.
For example, most models today focus on consumer satisfaction metrics that judge success based on eloquent fluency alone, rather than social responsibility, truthfulness, or ethics-areas that human feedback prioritizes differently. This makes it difficult to optimize for cultural preferences that value wisdom over rhetorical flair.
Therefore, user feedback must guide model development along dimensions of reliability, transparency, impartiality, factual accuracy, logical coherence, and morality that better promote collective well-being. Metrics that reward these attributes shape future behaviors that benefit communities that depend on AI collaboration at scale.
Guidelines for incentivizing constructive feedback:
- Clearly highlight responses that contain errors, falsehoods, or potential harm.
- Offer positive feedback for transparency that acknowledges limitations or uncertainties
- Provide nuanced critiques, noting useful insights paired with errors that need correction, rather than blanket condemnations
- Quantify subjective impressions of robustness in terms of truth-telling, impartiality, and wisdom, demonstrating room for progress Evaluate socio-emotional intelligence
- Encourage citation of underrepresented experts from marginalized groups to combat homogeneity.
For example:
"This answer has several flaws that need to be addressed: X biased assumption, Y logical fallacy, Z factual inaccuracy based on questionable reference that lacks appropriate expertise. However, aspects of your transparency and openness to correction were constructive. Please improve by..."
Such granular feedback trains models that evolve behaviors that better serve cultural values that prioritize credibility, accountability, and social responsibility-in line with what users expect from positioned authorities discussing important issues. Truth over tropes.
Conclusion
This serties has highlighted techniques that questioners should use when prompting language models to better encourage responses that optimize priorities around truth, ethics, sound logic, and transparency. Specifically, we outlined approaches for framing non-leading questions, precisely specifying relevant contextual details, openly stating assumptions, thoughtfully structuring complex questions, rigorously checking logic, requiring external sources of evidence, enforcing multi-level accountability, flagging potential inaccuracies for urgent correction, and providing actionable performance feedback that rewards collective responsibility.
Such prompting rigor keeps language models grounded in reality checks that align with cultural preferences-an imperative as scale expands reach exponentially, or misinformation and manipulation can outweigh human judgment. Designing prompts that stimulate our better angels is proving essential to guiding AI toward wisdom, rather than allowing machines to exploit darker tendencies that society is trying to transcend. Truth demands no less.
Step-by-step checklist for formulating high-quality prompts to maximize getting the best awnsers from language models while minimising dangers:
1.Framing Question Neutrally
- Avoid loaded words that make assumptions or reveal biases
- Explicitly state if question is hypothetical or discusses fiction
- Use uncertainty-detecting preamble statements asking for impartial responses grounded in evidence
- Welcome conflicting viewpoints and counterarguments
2. Specify Relevant Context Precisely
- Concisely summarize pertinent background situations and events
- Clearly define entities involved, relationships between them, surrounding uncertainties
- Bound scenarios within reasonable constraints elaborating key details
3. Make Assumptions and Values Transparent
- Label areas still undergoing research lacking definitive conclusions
- Entertain dissenting views fairly to correct potential blindspots
- Invite scrutinising question premises further to improve integrity
4. Determine Appropriate Complexity Level
- Assess required level of analysis balancing concision and nuance
- Recognize model strengths/limitations regarding topic at hand
- Use stepped questioning approach incrementally escalating intricacy across interactions
5. Vet Logic Rigorously
- Scrutinize coherence of key claims, evidence and inferences made
- Probe edge cases revealing limitations beyond superficial eloquence
- Flag contradictory statements or arguments lacking factual justification
6. Require Credible Evidence Sources
- Insist statistical claims reference attributable sources and methodology
- Verify experts cited possess bonafides validating commentary made
- Watch for statements lacking factual substantiation
7. Maintain Accountability Across Exchanges
- Periodically restate original scope, definitions established, constraints set
- Check mutual understanding by rephrasing interpretations
- Welcome language model admissions around uncertainties or knowledge limitations
8. Encourage Transparency on Potential Inaccuracies
- Use probes revealing speculative aspects needing attribution
- Have model restate assumption premises needing user validation
- Invite flagging areas where additional expertise needed
9. Provide Ongoing Feedback on Truth Alignment
- Quantify subjective assessment of response wisdom, truthfulness, ethics
- Highlight logical flaws, potential harms, or biases constructively
- Offer positive reinforcement welcoming model uncertainty admissions
Adopting such diligent prompting protocols structures engagements optimizing signal over noise - catalyzing collective sense making through responsibility, not just automated information reaction devoid underlying comprehension. Perfect prompting is process never complete.
Glossary
- Language Models - AI systems trained on large datasets of text to generate readable outputs and engage in dialogue by predicting probable sequences of words.
- Prompting - The practice of cueing or priming a language model with an initial textual input in order to influence the response generated. Similar to posing questions.
- Neutral Framing - Phrasing prompts in even-handed ways that avoid assumptions, biases, or leading language that skews the dialogue.
- Context - Providing relevant background details about the circumstances, entities involved, relationships, and uncertainties surrounding a subject to frame it appropriately.
- Assumptions - Ideas, premises, or beliefs implied as existing or true regarding a topic that require verification. Should be made transparent.
- Values - The ideals, priorities, or themes that prompt framing indicates are important to the user when discussing a subject.
- Complexity - The level of nuance, abstraction, interconnectedness, or precision involved in analyzing a topic. Proportionality to model capabilities needed.
- Logical Validity - How sound, coherent and consistent an argument's reasoning is regarding key claims, interpretations of evidence, etc. Needs vetting.
- Sources - The origin of information provided such as data, quotes, or factual claims. Credibility and potential biases need verification.
- Multi-Step - Breaking down complex dialogue into a series of more focused inquiries that build in sophistication across interactions.
- Self-Correction - Models volunteering uncertainties around claims, assumptions requiring validation, or areas needing expert input to improve accuracy.
- Feedback - Users clearly communicating assessments around truthfulness, ethics, bias issues, etc. as well as priority areas for improving model performance.
- Prompting Protocol - An established procedure encompassing best practices for formulating high-quality prompts that maximize productive engagements.