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Links for 2023-06-09

  • Bus bunching

    A fascinating queueing theory phenomenon:

    In public transport, bus bunching, clumping, convoying, piggybacking or platooning is a phenomenon whereby two or more [buses] which were scheduled at regular intervals along a common route instead bunch together and form a platoon. This occurs when leading vehicles are unable to keep their schedule and fall behind to such an extent that trailing vehicles catch up to them. […] A bus that is running slightly late will, in addition to its normal load, pick up passengers who would have taken the next bus if the first bus had not been late. These extra passengers delay the first bus even further. In contrast, the bus behind the late bus has a lighter passenger load than it otherwise would have, and may therefore run ahead of schedule.
    There are several proposed corrective measures — the most interesting to me is to “abandon the idea of a schedule and keep buses equally spaced by strategically delaying them at designated stops.” This has been implemented as a system called BusGenius, for example in Northern Arizona University —

    (tags: buses bunching clumping public-transport queue-theory busgenius)

  • [2304.11082] Fundamental Limitations of Alignment in Large Language Models

    An important aspect in developing language models that interact with humans is aligning their behavior to be useful and unharmful for their human users. This is usually achieved by tuning the model in a way that enhances desired behaviors and inhibits undesired ones, a process referred to as alignment. In this paper, we propose a theoretical approach called Behavior Expectation Bounds (BEB) which allows us to formally investigate several inherent characteristics and limitations of alignment in large language models. Importantly, we prove that for any behavior that has a finite probability of being exhibited by the model, there exist prompts that can trigger the model into outputting this behavior, with probability that increases with the length of the prompt. This implies that any alignment process that attenuates undesired behavior but does not remove it altogether, is not safe against adversarial prompting attacks. Furthermore, our framework hints at the mechanism by which leading alignment approaches such as reinforcement learning from human feedback increase the LLM’s proneness to being prompted into the undesired behaviors. Moreover, we include the notion of personas in our BEB framework, and find that behaviors which are generally very unlikely to be exhibited by the model can be brought to the front by prompting the model to behave as specific persona. This theoretical result is being experimentally demonstrated in large scale by the so called contemporary “chatGPT jailbreaks”, where adversarial users trick the LLM into breaking its alignment guardrails by triggering it into acting as a malicious persona. Our results expose fundamental limitations in alignment of LLMs and bring to the forefront the need to devise reliable mechanisms for ensuring AI safety.
    (via Remmelt Ellen)

    (tags: papers ethics llms ai ml infosec security prompt-hacking exploits alignment)

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