There and Back Again: The AI Alignment Paradox

The field of AI alignment aims to steer AI systems toward human goals, preferences, and ethical principles. Its contributions have been instrumental for improving the output quality, safety, and trustworthiness of today's AI models. This perspective article draws attention to a fundamental challenge inherent in all AI alignment endeavors, which we term the "AI alignment paradox": The better we align AI models with our values, the easier we make it for adversaries to misalign the models. We illustrate the paradox by sketching three concrete example incarnations for the case of language models, each corresponding to a distinct way in which adversaries can exploit the paradox. With AI's increasing real-world impact, it is imperative that a broad community of researchers be aware of the AI alignment paradox and work to find ways to break out of it, in order to ensure the beneficial use of AI for the good of humanity.

Fleet of Agents: Coordinated Problem Solving with Large Language Models using Genetic Particle Filtering

Large language models (LLMs) have significantly evolved, moving from simple output generation to complex reasoning and from stand-alone usage to being embedded into broader frameworks. In this paper, we introduce \emph{Fleet of Agents (FoA)}, a novel framework utilizing LLMs as agents to navigate through dynamic tree searches, employing a genetic-type particle filtering approach. FoA spawns a multitude of agents, each exploring autonomously, followed by a selection phase where resampling based on a heuristic value function optimizes the balance between exploration and exploitation. This mechanism enables dynamic branching, adapting the exploration strategy based on discovered solutions. We experimentally validate FoA using two benchmark tasks, "Game of 24" and "Mini-Crosswords". FoA outperforms the previously proposed Tree-of-Thoughts method in terms of efficacy and efficiency: it significantly decreases computational costs (by calling the value function less frequently) while preserving comparable or even superior accuracy.

Emojinize: Enriching Any Text with Emoji Translations

Emoji have become ubiquitous in written communication, on the Web and beyond. They can emphasize or clarify emotions, add details to conversations, or simply serve decorative purposes. This casual use, however, barely scratches the surface of the expressive power of emoji. To further unleash this power, we present Emojinize, a method for translating arbitrary text phrases into sequences of one or more emoji without requiring human input. By leveraging the power of large language models, Emojinize can choose appropriate emoji by disambiguating based on context (eg, cricket-bat vs bat) and can express complex concepts compositionally by combining multiple emoji (eq, "Emojinize" is translated to input-latin-letters right-arrow grinning-face). In a cloze test--based user study, we show that Emojinize's emoji translations increase the human guessability of masked words by 55%, whereas human-picked emoji translations do so by only 29%. These results suggest that emoji provide a sufficiently rich vocabulary to accurately translate a wide variety of words. Moreover, annotating words and phrases with Emojinize's emoji translations opens the door to numerous downstream applications, including children learning how to read, adults learning foreign languages, and text understanding for people with learning disabilities.

Broccoli: Sprinkling Lightweight Vocabulary Learning into Everyday Information Diets

The learning of a new language remains to this date a cognitive task that requires considerable diligence and willpower, recent advances and tools notwithstanding. In this paper, we propose Broccoli, a new paradigm aimed at reducing the required effort by seamlessly embedding vocabulary learning into users' everyday information diets. This is achieved by inconspicuously switching chosen words encountered by the user for their translation in the target language. Thus, by seeing words in context, the user can assimilate new vocabulary without much conscious effort. We validate our approach in a careful user study, finding that the efficacy of the lightweight Broccoli approach is competitive with traditional, memorization-based vocabulary learning. The low cognitive overhead is manifested in a pronounced decrease in learners' usage of mnemonic learning strategies, as compared to traditional learning. Finally, we establish that language patterns in typical information diets are compatible with spaced-repetition strategies, thus enabling an efficient use of the Broccoli paradigm. Overall, our work establishes the feasibility of a novel and powerful "install-and-forget" approach for embedded language acquisition.