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- Category: Prompting the LLMs'
In recent years, artificial intelligence (AI) has made amazing progress, particularly in the area of natural language processing (NLP). Systems known as large language models, such as GPT-3, Google's LaMDA, and Wu Dao 2.0, can now generate remarkably human-like text and engage in persuasive dialogue on virtually any topic imaginable. Their abilities seem to grow more impressive with each passing month.
But as AI researchers warn, "Language models don't have a fundamental understanding of the world. While they can skillfully manipulate language and string words together in coherent and meaningful ways, they lack a true understanding of the concepts, facts, and common sense that humans accumulate through lived experience. As such, their output can appear convincing on the surface, while containing falsehoods or biases that require human scrutiny.
This presents something of a conundrum when interacting with increasingly powerful language models that seem eager, sometimes overly so, to provide information on demand. How do we tap into their breadth of knowledge while accounting for inaccuracies or potential harm from misinformation? How can we ask good questions that elicit value rather than nonsense or deception?
This series explores principles and strategies for responsibly querying large language models to enrich understanding while minimizing risk. Getting the most out of this AI-powered tool requires care and diligence to keep it on track.
Read more: Intro : The Power and Peril of Probing Large Language Models
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Interacting with large language models requires vigilance in how we probe them. While they are capable of generating persuasive prose, their lack of grounded understanding means that questions lacking rigor can activate dangerous responses. Without a lifelike understanding of the concepts, contexts, or consequences behind words, they struggle to interpret vagueness or ambiguity.
Conscientious users must therefore take great care to formulate clear, specific questions. Although we routinely pepper human conversation with idioms that are open to interpretation, coding veterans Analog Futura warn that language models take input much more literally. They cannot infer intended meanings behind poor phrasing that lacks specificity.
This first article in our series examines why clarity and context are critical when engaging large language models. We will explore strategies for crafting precise questions that are less susceptible to misinterpretation, scope creep, or failure modes. Mastering these will lay the groundwork for subsequent techniques that build on the fundamentals of responsible questioning.
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When faced with complex questions with many unknowns, our instinct is often to ask broad, sweeping questions of language models that demand overly simplistic answers. This runs the risk of reductionism, or explaining complicated phenomena by eliminating crucial nuances.
AI researchers explain that humans have "thousands of years of shared background knowledge" that subconsciously feeds into dialogues. Large language models lack such context about the fundamentals underlying issues. Attempting to simplify thorny issues into digestible sound bites risks misrepresenting reality.
Multiple studies show that open-ended, broad questions create dangers such as
- Oversimplified explanations that lack nuance
- False confidence in speculative content
- Frivolous responses that imply harm or ethical violations
- Unmoored hypotheticals divorced from reality
Rather than demanding that complex issues be squeezed into a single exchange, use stepwise, multi-step questioning strategies that provide scaffolding and context to enhance the dialogue.
The Power of Gradual Inquiry
Read more: Article 2: Breaking Down Complex Questions Through Multi-Step Questioning
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The previous articles established strategies to help users ask better questions - clarifying terminology, constraints, and assumptions to prime language models for truthful answers. However, the responsibility for responsible AI extends beyond the quality of inputs. Users also need to check the output before acting on suggestions.
Computer scientists caution against blindly trusting model-generated text without checking it against known facts. We cannot assume accuracy just because answers sound coherent or authoritative. Models often hallucinate pseudo-profound statements that have no basis in reality.
This risk requires that language models be used more as hypothesis-generating tools than as oracles deserving unquestioning faith. Responsible use involves scrutinizing results by requiring evidence and external confirmation from reputable sources before further reliance for decision making.
Read more: Article 3: Responsible Sourcing - Fact-Checking Model Results
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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.
Read more: Article 4 : Getting better responses from large language models