15 research outputs found
Pareto Principles in Infinite Ethics
It is possible that the world contains infinitely many agents that have positive and negative levels of well-being. Theories have been developed to ethically rank such worlds based on the well-being levels of the agents in those worlds or other qualitative properties of the worlds in question, such as the distribution of agents across spacetime. In this thesis I argue that such ethical rankings ought to be consistent with the Pareto principle, which says that if two worlds contain the same agents and some agents are better off in the first world than they are in the second and no agents are worse off than they are in the second, then the first world is better than the second. I show that if we accept four axioms – the Pareto principle, transitivity, an axiom stating that populations of worlds can be permuted, and the claim that if the ‘at least as good as’ relation holds between two worlds then it holds between qualitative duplicates of this world pair – then we must conclude that there is ubiquitous incomparability between infinite worlds. I show that this is true even if the populations of infinite worlds are disjoint or overlapping, and that we cannot use any qualitative properties of world pairs to rank these worlds. Finally, I argue that this incomparability result generates puzzles for both consequentialist and non-consequentialist theories of objective and subjective permissibility
Towards Understanding Sycophancy in Language Models
Human feedback is commonly utilized to finetune AI assistants. But human
feedback may also encourage model responses that match user beliefs over
truthful ones, a behaviour known as sycophancy. We investigate the prevalence
of sycophancy in models whose finetuning procedure made use of human feedback,
and the potential role of human preference judgments in such behavior. We first
demonstrate that five state-of-the-art AI assistants consistently exhibit
sycophancy across four varied free-form text-generation tasks. To understand if
human preferences drive this broadly observed behavior, we analyze existing
human preference data. We find that when a response matches a user's views, it
is more likely to be preferred. Moreover, both humans and preference models
(PMs) prefer convincingly-written sycophantic responses over correct ones a
non-negligible fraction of the time. Optimizing model outputs against PMs also
sometimes sacrifices truthfulness in favor of sycophancy. Overall, our results
indicate that sycophancy is a general behavior of state-of-the-art AI
assistants, likely driven in part by human preference judgments favoring
sycophantic responses.Comment: 32 pages, 20 figure
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
We describe our early efforts to red team language models in order to
simultaneously discover, measure, and attempt to reduce their potentially
harmful outputs. We make three main contributions. First, we investigate
scaling behaviors for red teaming across 3 model sizes (2.7B, 13B, and 52B
parameters) and 4 model types: a plain language model (LM); an LM prompted to
be helpful, honest, and harmless; an LM with rejection sampling; and a model
trained to be helpful and harmless using reinforcement learning from human
feedback (RLHF). We find that the RLHF models are increasingly difficult to red
team as they scale, and we find a flat trend with scale for the other model
types. Second, we release our dataset of 38,961 red team attacks for others to
analyze and learn from. We provide our own analysis of the data and find a
variety of harmful outputs, which range from offensive language to more subtly
harmful non-violent unethical outputs. Third, we exhaustively describe our
instructions, processes, statistical methodologies, and uncertainty about red
teaming. We hope that this transparency accelerates our ability to work
together as a community in order to develop shared norms, practices, and
technical standards for how to red team language models
Language Models (Mostly) Know What They Know
We study whether language models can evaluate the validity of their own
claims and predict which questions they will be able to answer correctly. We
first show that larger models are well-calibrated on diverse multiple choice
and true/false questions when they are provided in the right format. Thus we
can approach self-evaluation on open-ended sampling tasks by asking models to
first propose answers, and then to evaluate the probability "P(True)" that
their answers are correct. We find encouraging performance, calibration, and
scaling for P(True) on a diverse array of tasks. Performance at self-evaluation
further improves when we allow models to consider many of their own samples
before predicting the validity of one specific possibility. Next, we
investigate whether models can be trained to predict "P(IK)", the probability
that "I know" the answer to a question, without reference to any particular
proposed answer. Models perform well at predicting P(IK) and partially
generalize across tasks, though they struggle with calibration of P(IK) on new
tasks. The predicted P(IK) probabilities also increase appropriately in the
presence of relevant source materials in the context, and in the presence of
hints towards the solution of mathematical word problems. We hope these
observations lay the groundwork for training more honest models, and for
investigating how honesty generalizes to cases where models are trained on
objectives other than the imitation of human writing.Comment: 23+17 pages; refs added, typos fixe
Specific versus General Principles for Constitutional AI
Human feedback can prevent overtly harmful utterances in conversational
models, but may not automatically mitigate subtle problematic behaviors such as
a stated desire for self-preservation or power. Constitutional AI offers an
alternative, replacing human feedback with feedback from AI models conditioned
only on a list of written principles. We find this approach effectively
prevents the expression of such behaviors. The success of simple principles
motivates us to ask: can models learn general ethical behaviors from only a
single written principle? To test this, we run experiments using a principle
roughly stated as "do what's best for humanity". We find that the largest
dialogue models can generalize from this short constitution, resulting in
harmless assistants with no stated interest in specific motivations like power.
A general principle may thus partially avoid the need for a long list of
constitutions targeting potentially harmful behaviors. However, more detailed
constitutions still improve fine-grained control over specific types of harms.
This suggests both general and specific principles have value for steering AI
safely
The moral inefficacy of carbon offsetting
Many real-world agents recognise that they impose harms by choosing to emit carbon, e.g., by flying. Yet many do so anyway, and then attempt to make things right by offsetting those harms. Such offsetters typically believe that, by offsetting, they change the deontic status of their behaviour, making an otherwise impermissible action permissible. Do they succeed in practice? Some philosophers have argued that they do, since their offsets appear to reverse the adverse effects of their emissions. But we show that they do not. In practice, standard carbon offsetting does not reverse the harms of the original action, nor does it even benefit the same group as was harmed. Standard moral theories hence deny that such offsetting succeeds. Indeed, we show that any moral theory that allows offsetting in this setting faces a dilemma between allowing any wrong to be offset, no matter how grievous, and recognising an implausibly sharp discontinuity between offsettable actions and non-offsettable actions. The most plausible response is to accept that carbon offsetting fails to right our climate wrongs