ICL Markup: Structuring In-Context Learning using Soft-Token Tags

Large pretrained language models (LLMs) can be rapidly adapted to a wide variety of tasks via a text-to-text approach, where the instruction and input are fed to the model in natural language. Combined with in-context learning (ICL), this paradigm is impressively flexible and powerful. However, it also burdens users with an overwhelming number of choices, many of them arbitrary. Inspired by markup languages like HTML, we contribute a method of using soft-token tags to compose prompt templates. This approach reduces arbitrary decisions and streamlines the application of ICL. Our method is a form of meta-learning for ICL; it learns these tags in advance during a parameter-efficient fine-tuning ``warm-up'' process. The tags can subsequently be used in templates for ICL on new, unseen tasks without any additional fine-tuning. Our experiments with this approach yield promising initial results, improving LLM performance on important enterprise applications such as few-shot and open-world intent detection, as well as text classification in news and legal domains.

Sparsify-then-Classify: From Internal Neurons of Large Language Models To Efficient Text Classifiers

Among the many tasks that Large Language Models (LLMs) have revolutionized is text classification. However, existing approaches for applying pretrained LLMs to text classification predominantly rely on using single token outputs from only the last layer of hidden states. As a result, they suffer from limitations in efficiency, task-specificity, and interpretability. In our work, we contribute an approach that uses all internal representations by employing multiple pooling strategies on all activation and hidden states. Our novel lightweight strategy, Sparsify-then-Classify (STC) first sparsifies task-specific features layer-by-layer, then aggregates across layers for text classification. STC can be applied as a seamless plug-and-play module on top of existing LLMs. Our experiments on a comprehensive set of models and datasets demonstrate that STC not only consistently improves the classification performance of pretrained and fine-tuned models, but is also more efficient for both training and inference, and is more intrinsically interpretable.

Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science

Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks. Here, we tackle a pervasive problem in synthetic data generation: its generative distribution often differs from the distribution of real-world data researchers care about (in other words, it is unfaithful). In a case study on sarcasm detection, we study three strategies to increase the faithfulness of synthetic data: grounding, filtering, and taxonomy-based generation. We evaluate these strategies using the performance of classifiers trained with generated synthetic data on real-world data. While all three strategies improve the performance of classifiers, we find that grounding works best for the task at hand. As synthetic data generation plays an ever-increasing role in NLP research, we expect this work to be a stepping stone in improving its utility. We conclude this paper with some recommendations on how to generate high(er)-fidelity synthetic data for specific tasks.

Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess

The advent of machine learning models that surpass human decision-making ability in complex domains has initiated a movement towards building AI systems that interact with humans. Many building blocks are essential for this activity, with a central one being the algorithmic characterization of human behavior. While much of the existing work focuses on aggregate human behavior, an important long-range goal is to develop behavioral models that specialize to individual people and can differentiate among them. To formalize this process, we study the problem of behavioral stylometry, in which the task is to identify a decision-maker from their decisions alone. We present a transformer-based approach to behavioral stylometry in the context of chess, where one attempts to identify the player who played a set of games. Our method operates in a few-shot classification framework, and can correctly identify a player from among thousands of candidate players with 98% accuracy given only 100 labeled games. Even when trained on amateur play, our method generalises to out-of-distribution samples of Grandmaster players, despite the dramatic differences between amateur and world-class players. Finally, we consider more broadly what our resulting embeddings reveal about human style in chess, as well as the potential ethical implications of powerful methods for identifying individuals from behavioral data.

Mimetic Models: Ethical Implications of AI that Acts Like You

An emerging theme in artificial intelligence research is the creation of models to simulate the decisions and behavior of specific people, in domains including game-playing, text generation, and artistic expression. These models go beyond earlier approaches in the way they are tailored to individuals, and the way they are designed for interaction rather than simply the reproduction of fixed, pre-computed behaviors. We refer to these as mimetic models, and in this paper we develop a framework for characterizing the ethical and social issues raised by their growing availability. Our framework includes a number of distinct scenarios for the use of such models, and considers the impacts on a range of different participants, including the target being modeled, the operator who deploys the model, and the entities that interact with it.

Community embeddings reveal large-scale cultural organization of online platforms

Optimism about the Internet's potential to bring the world together has been tempered by concerns about its role in inflaming the 'culture wars'. Via mass selection into like-minded groups, online society may be becoming more fragmented and polarized, particularly with respect to partisan differences. However, our ability to measure the cultural makeup of online communities, and in turn understand the cultural structure of online platforms, is limited by the pseudonymous, unstructured, and large-scale nature of digital discussion. Here we develop a neural embedding methodology to quantify the positioning of online communities along cultural dimensions by leveraging large-scale patterns of aggregate behaviour. Applying our methodology to 4.8B Reddit comments made in 10K communities over 14 years, we find that the macro-scale community structure is organized along cultural lines, and that relationships between online cultural concepts are more complex than simply reflecting their offline analogues. Examining political content, we show Reddit underwent a significant polarization event around the 2016 U.S. presidential election, and remained highly polarized for years afterward. Contrary to conventional wisdom, however, instances of individual users becoming more polarized over time are rare; the majority of platform-level polarization is driven by the arrival of new and newly political users. Our methodology is broadly applicable to the study of online culture, and our findings have implications for the design of online platforms, understanding the cultural contexts of online content, and quantifying cultural shifts in online behaviour.

Adoption of Twitter's New Length Limit: Is 280 the New 140?

In November 2017, Twitter doubled the maximum allowed tweet length from 140 to 280 characters, a drastic switch on one of the world's most influential social media platforms. In the first long-term study of how the new length limit was adopted by Twitter users, we ask: Does the effect of the new length limit resemble that of the old one? Or did the doubling of the limit fundamentally change how Twitter is shaped by the limited length of posted content? By analyzing Twitter's publicly available 1% sample over a period of around 3 years, we find that, when the length limit was raised from 140 to 280 characters, the prevalence of tweets around 140 characters dropped immediately, while the prevalence of tweets around 280 characters rose steadily for about 6 months. Despite this rise, tweets approaching the length limit have been far less frequent after than before the switch. We find widely different adoption rates across languages and client-device types. The prevalence of tweets around 140 characters before the switch in a given language is strongly correlated with the prevalence of tweets around 280 characters after the switch in the same language, and very long tweets are vastly more popular on Web clients than on mobile clients. Moreover, tweets of around 280 characters after the switch are syntactically and semantically similar to tweets of around 140 characters before the switch, manifesting patterns of message squeezing in both cases. Taken together, these findings suggest that the new 280-character limit constitutes a new, less intrusive version of the old 140-character limit. The length limit remains an important factor that should be considered in all studies using Twitter data.

Learning Personalized Models of Human Behavior in Chess

Even when machine learning systems surpass human ability in a domain, there are many reasons why AI systems that capture human-like behavior would be desirable: humans may want to learn from them, they may need to collaborate with them, or they may expect them to serve as partners in an extended interaction. Motivated by this goal of human-like AI systems, the problem of predicting human actions -- as opposed to predicting optimal actions -- has become an increasingly useful task. We extend this line of work by developing highly accurate personalized models of human behavior in the context of chess. Chess is a rich domain for exploring these questions, since it combines a set of appealing features: AI systems have achieved superhuman performance but still interact closely with human chess players both as opponents and preparation tools, and there is an enormous amount of recorded data on individual players. Starting with an open-source version of AlphaZero trained on a population of human players, we demonstrate that we can significantly improve prediction of a particular player's moves by applying a series of fine-tuning adjustments. The differences in prediction accuracy between our personalized models and unpersonalized models are at least as large as the differences between unpersonalized models and a simple baseline. Furthermore, we can accurately perform stylometry -- predicting who made a given set of actions -- indicating that our personalized models capture human decision-making at an individual level.

De-anonymization of authors through arXiv submissions during double-blind review

In this paper, we investigate the effects of releasing arXiv preprints of papers that are undergoing a double-blind review process. In particular, we ask the following research question: What is the relation between de-anonymization of authors through arXiv preprints and acceptance of a research paper at a (nominally) double-blind venue? Under two conditions: papers that are released on arXiv before the review phase and papers that are not, we examine the correlation between the reputation of their authors with the review scores and acceptance decisions. By analyzing a dataset of ICLR 2020 and ICLR 2019 submissions (n=5050), we find statistically significant evidence of positive correlation between percentage acceptance and papers with high reputation released on arXiv. In order to understand this observed association better, we perform additional analyses based on self-specified confidence scores of reviewers and observe that less confident reviewers are more likely to assign high review scores to papers with well known authors and low review scores to papers with less known authors, where reputation is quantified in terms of number of Google Scholar citations. We emphasize upfront that our results are purely correlational and we neither can nor intend to make any causal claims. A blog post accompanying the paper and our scraping code will be linked in the project website https://sites.google.com/view/deanon-arxiv/home