Deepak Ramachandran
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Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Christina Göpfert
Alex Haig
Ivan Vendrov
Tyler Lu
Hubert Pham
Mohammad Ghavamzadeh
ACM Transactions on Recommender Systems (2024)
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Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e.g., clicks, item consumption, ratings). They allow users to express intent, preferences, constraints, and contexts in a richer fashion, often using natural language (including faceted search and dialogue).
Yet more research is needed to find the most effective ways to use this feedback. One challenge is inferring a user's semantic intent
from the open-ended terms or attributes often used to describe a desired item,
and using it to refine recommendation results.
Leveraging concept activation vectors (CAVs) (Kim, et al., 2018)
a recently developed approach for model interpretability in machine learning,
we develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in recommender systems. One novel feature of our approach is its ability to distinguish objective and subjective attributes (both subjectivity of degree and of sense), and associate different senses of subjective attributes with different users.
We demonstrate on both synthetic and real-world data sets that our CAV representation not only accurately interprets users' subjective semantics, but can also be used to improve recommendations through interactive item critiquing.
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Structured Complex Task Decomposition (SCTD) is the problem of breaking down a complex real-world task (such as planning a wedding) into a directed acyclic graph over individual steps that contribute to achieving the task, with edges specifying temporal dependencies between them. SCTD is an important component of assistive planning tools, and a challenge for commonsense reasoning systems. We probe how accurately SCTD can be done with the knowledge extracted from Large Language Models (LLMs). We introduce a high-quality human-annotated dataset for this problem and novel metrics to fairly assess performance of LLMs against several baselines. Our experiments reveal that LLMs are able to decompose complex tasks into individual steps effectively, with a relative improvement of 15% to 280% over the best baseline. We also propose a number of approaches to further improve their performance, with a relative improvement of 7% to 37% over the base model. However, we find that LLMs still struggle to predict pairwise temporal dependencies, which reveals a gap in their understanding of complex tasks.
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Demystifying Embedding Spaces using Large Language Models
Jihwan Jeong
Lior Shani
Martin Mladenov
The Twelfth International Conference on Learning Representations (2024)
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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.
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Tackling Provably Hard Representative Selection via Graph Neural Networks
Transactions on Machine Learning Research (2023)
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Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset. In this paper, we study RS for attributed graphs, and focus on finding representative nodes that optimize the accuracy of a model trained on the selected representatives. Theoretically, we establish a new hardness result for RS (in the absence of a graph structure) by proving that a particular, highly practical variant of it (RS for Learning) is hard to approximate in polynomial time within any reasonable factor, which implies a significant potential gap between the optimum solution of widely-used surrogate functions and the actual accuracy of the model. We then study the setting where a (homophilous) graph structure is available, or can be constructed, between the data points. We show that with an appropriate modeling approach, the presence of such a structure can turn a hard RS (for learning) problem into one that can be effectively solved. To this end, we develop RS-GNN, a representation learning-based RS model based on Graph Neural Networks. Empirically, we demonstrate the effectiveness of RS-GNN on problems with predefined graph structures as well as problems with graphs induced from node feature similarities, by showing that RS-GNN achieves significant improvements over established baselines on a suite of eight benchmarks.
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Remarkable progress has been made on automated reasoning with natural text, by using Language Models (LMs) and methods such as Chain-of-Thought and Selection-Inference. These techniques search for proofs in the forward direction from axioms to the conclusion, which suffers from a combinatorial explosion of the search space, and thus high failure rates for problems requiring longer chains of reasoning. The classical automated reasoning literature has shown that reasoning in the backward direction (i.e. from the intended conclusion to supporting axioms) is significantly more efficient at proof-finding. Importing this intuition into the LM setting, we develop a Backward Chaining algorithm, called LAMBADA, that decomposes reasoning into four sub-modules. These sub-modules are simply implemented by few-shot prompted LM inference. We show that LAMBADA achieves sizable accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets, particularly when deep and accurate proof chains are required.
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Pushing the Accuracy-Group Robustness Tradeoff Frontier with Introspective Self-play
Dj Dvijotham
Jihyeon Lee
Martin Strobel
Quan Yuan
ICLR'23 (2023) (to appear)
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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.
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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)
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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.
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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)
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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.
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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
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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.
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Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Christina Göpfert
Ivan Vendrov
Tyler Lu
WWW22: The Web Conference 2022, Lyon, France, pp. 2411-2421
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.
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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.
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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.
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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.
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