Many alignment efforts frame their objective using the 'helpful, harmless, and honest' (HHH) criteria. Which statement correctly characterises what these three criteria are intended to specify?
- AThree mandatory architectural modules - a helper head, a safety head, and a verifier head - that every aligned transformer must include.
- BThree quantitative benchmark scores that must each exceed 0.9 before a model is certified as aligned for release.
- CA set of high-level behavioural goals: the assistant should help the user, avoid causing harm, and avoid deceiving the user, which together guide alignment decisions. Correct
- DA data-collection protocol specifying that training corpora must contain equal proportions of helpful, harmless, and honest example sentences.
Why A is wrong: HHH describes desired behavioural properties, not required architectural components, and no transformer is obliged to contain named heads bearing these labels. It is tempting because the three-part naming suggests three model parts, but the criteria constrain behaviour rather than structure.
Why B is wrong: HHH is a qualitative set of goals, not a trio of fixed numeric thresholds that gate certification. The trap appeals to the desire for crisp pass marks, but no standard assigns a 0.9 cutoff per criterion, and alignment is not certified by three such numbers.
Why C is correct: The HHH framing names three behavioural targets - being helpful to the user, avoiding harmful actions or content, and being honest rather than deceptive - that together act as a compact statement of what an aligned assistant should do. These goals guide trade-off and training decisions, which is exactly their intended role.
Why D is wrong: HHH does not prescribe corpus composition ratios; it describes how the model should behave, not how the dataset must be balanced. It is plausible because dataset curation matters for alignment, but the criteria are behavioural goals rather than a sampling recipe.