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Deepak Ramachandran

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    Preview abstract Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, they often preclude direct interpretation. While downstream tasks make use of these compressed representations, meaningful interpretation usually requires visualization using dimensionality reduction or specialized machine learning interpretability methods. This paper addresses the challenge of making such embeddings more interpretable and broadly useful, by employing large language models (LLMs) to directly interact with embeddings -- transforming abstract vectors into understandable narratives. By injecting embeddings into LLMs, we enable querying and exploration of complex embedding data. We demonstrate our approach on a variety of diverse tasks, including: enhancing concept activation vectors (CAVs), communicating novel embedded entities, and decoding user preferences in recommender systems. Our work couples the immense information potential of embeddings with the interpretative power of LLMs. View details
    Preview abstract Improving the accuracy-fairness frontier of deep neural network (DNN) models is an important problem. Uncertainty-based active learning active learning (AL)can potentially improve the frontier by preferentially sampling underrepresented subgroups to create a more balanced training dataset. However, the quality of uncertainty estimates from modern DNNs tend to degrade in the presence of spurious correlations and dataset bias, compromising the effectiveness of AL for sampling tail groups. In this work, we propose Introspective Self-play (ISP), a simple approach to improve the uncertainty estimation of a deep neural network under dataset bias, by adding an auxiliary introspection task requiring a model to predict the bias for each data point in addition to the label. We show that ISP provably improves the bias-awareness of the model representation and the resulting uncertainty estimates. On two real-world tabular and language tasks, ISP serves as a simple “plug-in” for AL model training, consistently improving both the tail-group sampling rate and the final accuracy-fairness trade-off frontier of popular AL methods. View details
    KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals
    Sandeep Silwal
    Andrew Nystrom
    Andrew McCallum
    International Conference in Learning Representation (ICLR) (2023) (to appear)
    Preview abstract The unprecedented rate at which the sizes of machine learning (ML) models are growing necessitates novel approaches to enable efficient and scalable solutions. We contribute to this line of work by studying a novel version of the Budgeted Correlation Clustering problem where along with a limited number of queries to an expensive oracle for node similarities (e.g. a large ML model), we have unlimited access to a cheaper but less accurate second oracle. Our formulation is inspired by many practical scenarios where coarse approximations of the expensive similarity metric can be efficiently obtained via weaker models. We develop a theoretically motivated algorithm in this setting that leverages the cheap oracle to judiciously query the strong oracle while maintaining high clustering quality. We empirically demonstrate gains in query minimization and clustering metrics on a variety of datasets with diverse strong and cheap oracles. Most notably, we demonstrate a practical application in text clustering based on expensive cross-attention language models by showing that cheaper (but weaker) embedding-based models can be leveraged to substantially reduce the number of inference calls to the former. View details
    Preview abstract Interactive Recommender Systems (RSs) have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional RSs (e.g., clicks, item consumption, ratings), allowing users to express intent, preferences, constraints, and contexts in a richer fashion using natural language. Still, more research is needed to find the most effective ways to use this feedback. One major challenge is inferring a user's intended semantic intent from given the open-ended terms (say, attributes or tags) used to describe a desired item, and utilize that to refine recommendation results. Leveraging Concept Activation Vectors (CAVs) [13], we develop a framework to learn a representation that captures the semantics of such attributes and connect them to user preferences and behaviors in RSs. One novel feature of our approach is its ability to distinguish objective and subjective attributes (including subjectivity of degree and of sense) and associate different senses of subjective attributes with different user. We demonstrate on both synthetic and real-world datasets that our CAV representation not only accurately interprets users' subjective semantics, but can also be used to improve recommendations. View details
    Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities
    Ivan Vendrov
    Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-22) (2022), pp. 12287-12293
    Preview abstract The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given the inherent semantic subjectivity present in natural language, we argue that modeling subjective attributes in recommenders is a critical, yet understudied, avenue of AI research. We propose a novel framework for understanding different forms of subjectivity, examine various recommender tasks that will benefit from a systematic treatment of subjective attributes, and outline a number of research challenges. View details
    FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
    Alon Albalak
    Connor Pryor
    Jay Pujara
    Lise Getoor
    Luke Yoffe
    Pegah Jandaghimeibodi
    William Wang
    Yi-Lin Tuan
    EMNLP'22 (2022)
    Preview abstract Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work explores conversational task transfer by introducing \feta: a benchmark for \textbf{FE}w-sample \textbf{TA}sk transfer in open-domain dialogue. \feta\;contains two underlying sets of conversations upon which there are 10 and 7 tasks annotated, enabling the study of intra-dataset task transfer; task transfer without domain adaptation. We utilize three popular language models and three learning algorithms to analyze the transferability between 132 source-target task pairs and create a baseline for future work. We run experiments in the single- and multi-source settings and report valuable findings, e.g., most performance trends are model-specific, and span extraction and multiple-choice tasks benefit the most from task transfer. In addition to task transfer, \feta\;can be a valuable resource for future research into the efficiency and generalizability of pre-training datasets and model architectures, as well as learning settings such as continual and multitask learning. View details
    Preview abstract Many Question-Answering (QA) datasets contain unanswerable questions, but their treatment in QA systems remains primitive. Our analysis of the Natural Questions (Kwiatkowski et al., 2019) dataset reveals that a substantial portion of unanswerable questions (∼21%) can be explained based on the presence of unverifiable presuppositions. Through a user preference study, we demonstrate that the oracle behavior of our proposed system—which provides responses based on presupposition failure—is preferred over the oracle behavior of existing QA systems. Then, we present a novel framework for implementing such a system in three steps: presupposition generation, presupposition verification, and explanation generation, reporting progress on each. Finally, we show that a simple modification of adding presuppositions and their verifiability to the input of a competitive end-to-end QA system yields modest gains in QA performance and unanswerability detection, demonstrating the promise of our approach. View details
    Preview abstract We show that embedding-based language models capture a significant amount of information about the scalar magnitudes of objects but are short of the capability required for general common-sense reasoning. We identify ambiguity and numeracy as the key factors limiting their performance, and show that a simple reversible transformation of the pre-training corpus can have a significant effect on the results. We identify the best models and metrics to use when doing zero-shot transfer across tasks in this domain. View details
    Preview abstract Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distribution over Quantities (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as ``Is a lion bigger than a wolf?". Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce. View details
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