hari sundaram

We show the effectiveness of automatic differentiation in efficiently and correctly computing and controlling the spectrum of implicitly linear operators, a rich family of layer types including all standard convolutional and dense layers. We provide the first clipping method which is correct for general convolution layers, and illuminate the representational limitation that caused correctness issues in prior work. We study the effect of the batch normalization layers when concatenated with convolutional layers and show how our clipping method can be applied to their composition. By comparing the accuracy and performance of our algorithms to the state-of-the-art methods, using various experiments, we show they are more precise and efficient and lead to better generalization and adversarial robustness. We provide the code for using our methods at https://github.com/Ali-E/FastClip.

As large-scale language models become the standard for text generation, there is a greater need to tailor the generations to be more or less concise, targeted, and informative, depending on the audience/application. Existing control approaches primarily adjust the semantic (e.g., emotion, topics), structural (e.g., syntax tree, parts-of-speech), and lexical (e.g., keyword/phrase inclusion) properties of text, but are insufficient to accomplish complex objectives such as pacing which control the complexity and readability of the text. In this paper, we introduce CEV-LM - a lightweight, semi-autoregressive language model that utilizes constrained edit vectors to control three complementary metrics (speed, volume, and circuitousness) that quantify the shape of text (e.g., pacing of content). We study an extensive set of state-of-the-art CTG models and find that CEV-LM provides significantly more targeted and precise control of these three metrics while preserving semantic content, using less training data, and containing fewer parameters.

Sequential recommenders are crucial to the success of online applications, \eg e-commerce, video streaming, and social media. While model architectures continue to improve, for every new application domain, we still have to train a new model from scratch for high quality recommendations. On the other hand, pre-trained language and vision models have shown great success in zero-shot or few-shot adaptation to new application domains. Inspired by the success of pre-trained models in peer AI fields, we propose a novel pre-trained sequential recommendation framework: PrepRec. We learn universal item representations by modeling item popularity dynamics. Through extensive experiments on five real-world datasets, we show that PrepRec, without any auxiliary information, can not only zero-shot transfer to a new domain, but achieve competitive performance compared to state-of-the-art sequential recommender models with only a fraction of the model size. In addition, with a simple post-hoc interpolation, PrepRec can improve the performance of existing sequential recommenders on average by 13.8\% in Recall@10 and 29.5% in NDCG@10. We provide an anonymized implementation of PrepRec at https://anonymous.4open.science/r/PrepRec--2F60/

Existing works on outline-conditioned text generation typically aim to generate text using provided outlines as rough sketches, such as keywords and phrases. However, these approaches make it challenging to control the quality of text generation and assess consistency between outlines and generated texts due to lack of clarity and rationality of the rough outlines. In this paper, we introduce a novel text generation task called Precise Outline-conditioned Generation, which requires generating stories based on specific, sentence-level outlines. To facilitate research on this task, we construct two new datasets, WPOG and CDM. We provide strong baselines based on fine-tuning models such as BART and GPT-2, and evaluating zero-shot performance of models such as ChatGPT and Vicuna. Furthermore, we identify an issue of imbalanced utilization of the outline information in the precise outline-conditioned generation, which is ubiquitously observed across fine-tuned models and zero-shot inference models. To address this issue, we propose an explicit outline utilization control approach and a novel framework that leverages the task duality between summarization and generation. Experimental results show that the proposed approaches effectively alleviate the issue of imbalanced outline utilization and enhance the quality of precise outline-conditioned text generation for both fine-tuning and zero-shot settings.

Automatically open-ended long text generation poses significant challenges due to semantic incoherence and plot implausibility. Previous works usually alleviate this problem through outlines in the form of short phrases or abstractive signals by designing unsupervised tasks, which tend to be unstable and weakly interpretable. Assuming that a summary serves as a mature outline, we introduce a two-stage, summary-enhanced outline supervised generation framework. This framework leverages the dual characteristics of the summarization task to improve outline prediction, resulting in more explicit and plausible outlines. Furthermore, we identify an underutilization issue in outline-based generation with both standard pretrained language models (e.g., GPT-2, BART) and large language models (e.g., Vicuna, ChatGPT). To address this, we propose a novel explicit outline control method for more effective utilization of generated outlines.

Social media sites like Reddit, Discord, and Clubhouse utilize a community-reliant approach to content moderation. Under this model, volunteer moderators are tasked with setting and enforcing content rules within the platforms' sub-communities. However, few mechanisms exist to ensure that the rules set by moderators reflect the values of their community. Misalignments between users and moderators can be detrimental to community health. Yet little quantitative work has been done to evaluate the prevalence or nature of user-moderator misalignment. Through a survey of 798 users on r/ChangeMyView, we evaluate user-moderator alignment at the level of policy-awareness (does users know what the rules are?), practice-awareness (do users know how the rules are applied?) and policy-/practice-support (do users agree with the rules and how they are applied?). We find that policy-support is high, while practice …

Informed consent is a core cornerstone of ethics in human subject research. Through the informed consent process, participants learn about the study procedure, benefits, risks, and more to make an informed decision. However, recent studies showed that current practices might lead to uninformed decisions and expose participants to unknown risks, especially in online studies. Without the researcher’s presence and guidance, online participants must read a lengthy form on their own with no answers to their questions. In this paper, we examined the role of an AI-powered chatbot in improving informed consent online. By comparing the chatbot with form-based interaction, we found the chatbot improved consent form reading, promoted participants’ feelings of agency, and closed the power gap between the participant and the researcher. Our exploratory analysis further revealed the altered power dynamic might …

Modern neural collaborative filtering techniques are critical to the success of e-commerce, social media, and content-sharing platforms. However, despite technical advances -- for every new application domain, we need to train an NCF model from scratch. In contrast, pre-trained vision and language models are routinely applied to diverse applications directly (zero-shot) or with limited fine-tuning. Inspired by the impact of pre-trained models, we explore the possibility of pre-trained recommender models that support building recommender systems in new domains, with minimal or no retraining, without the use of any auxiliary user or item information. Zero-shot recommendation without auxiliary information is challenging because we cannot form associations between users and items across datasets when there are no overlapping users or items. Our fundamental insight is that the statistical characteristics of the user-item interaction matrix are universally available across different domains and datasets. Thus, we use the statistical characteristics of the user-item interaction matrix to identify dataset-independent representations for users and items. We show how to learn universal (i.e., supporting zero-shot adaptation without user or item auxiliary information) representations for nodes and edges from the bipartite user-item interaction graph. We learn representations by exploiting the statistical properties of the interaction data, including user and item marginals, and the size and density distributions of their clusters.

Peer evaluations are a well-established tool for evaluating individual and team performance in collaborative contexts, but are susceptible to social and cognitive biases. Current peer evaluation tools have also yet to address the unique opportunities that online collaborative technologies provide for addressing these biases. In this work, we explore the potential of one such opportunity for peer evaluations: data traces automatically generated by collaborative tools, which we refer to as "activity traces". We conduct a between-subjects experiment with 101 students and MTurk workers, investigating the effects of reviewing activity traces on peer evaluations of team members in an online collaborative task. Our findings show that the usage of activity traces led participants to make more and greater revisions to their evaluations compared to a control condition. These revisions also increased the consistency and participants …

In many online markets we “shop alone” — there is no way for us to know the prices other consumers paid for the same goods. Could this lack of price transparency lead to differential pricing? To answer this question, we present a generalized framework to audit online markets for differential pricing using automated agents. Consensus is a key idea in our work: for a successful black-box audit, both the experimenter and seller must agree on the agents’ attributes. We audit two competitive online travel markets on kayak.com (flight and hotel markets) and construct queries representative of the demand for goods. Crucially, we assume ignorance of the sellers’ pricing mechanisms while conducting these audits. We conservatively implement consensus with nine distinct profiles based on behavior, not demographics. We use a structural causal model for price differences and estimate model parameters using Bayesian …

Adopting contextually appropriate, audience-tailored linguistic styles is critical to the success of user-centric language generation systems (e.g., chatbots, computer-aided writing, dialog systems). While existing approaches demonstrate textual style transfer with large volumes of parallel or non-parallel data, we argue that grounding style on audience-independent external factors is innately limiting for two reasons. First, it is difficult to collect large volumes of audience-specific stylistic data. Second, some stylistic objectives (e.g., persuasiveness, memorability, empathy) are hard to define without audience feedback. In this paper, we propose the novel task of style infusion - infusing the stylistic preferences of audiences in pretrained language generation models. Since humans are better at pairwise comparisons than direct scoring - i.e., is Sample-A more persuasive/polite/empathic than Sample-B - we leverage limited pairwise human judgments to bootstrap a style analysis model and augment our seed set of judgments. We then infuse the learned textual style in a GPT-2 based text generator while balancing fluency and style adoption. With quantitative and qualitative assessments, we show that our infusion approach can generate compelling stylized examples with generic text prompts. The code and data are accessible at https://github.com/CrowdDynamicsLab/StyleInfusion.

This paper provides a robust, scalable Bluetooth Low-Energy (BLE) based indoor localization solution using commodity hardware. While WiFi-based indoor localization has been widely studied, BLE has emerged a key technology for contact-tracing in the current pandemic. To accurately estimate distance using BLE on commercial devices, systems today rely on Receiver Signal Strength Indicator(RSSI) which suffers from sampling bias and multipath effects. We propose a new metric: Packet Reception Probability (PRP) that builds on a counter-intuitive idea that we can exploit packet loss to estimate distance. We localize using a Bayesian-PRP formulation that also incorporates an explicit model of the multipath. To make deployment easy, we do not require any hardware, firmware, or driver-level changes to off-the-shelf devices, and require minimal training. PRP can achieve meter level accuracy with just 6 devices with known locations and 12 training locations. We show that fusing PRP with RSSI is beneficial at short distances < 2m. Beyond 2m, fusion is worse than PRP, as RSSI becomes effectively de-correlated with distance. Robust location accuracy at all distances and ease of deployment with PRP can help enable wide range indoor localization solutions using BLE.

In this paper we address how complex social communities emerge from local decisions by individuals with limited attention and knowledge. This problem is critical; if we understand community formation mechanisms, it may be possible to intervene to improve social welfare. We propose an interpretable, novel model for attributed community formation driven by resource-bounded individuals' strategic, selfish behavior. In our stylized model, attributed individuals act strategically in two dimensions: attribute and network structure. Agents are endowed with limited attention, and communication costs limit the number of active connections. In each time step, each agent proposes a new friendship. Agents then accept proposals, decline proposals, or remove friends, consistent with their strategy to maximize payoff. We identify criteria (number of stable triads) for convergence to some community structure and prove that our community formation model converges to a stable network. Ablations justify the ecological validity of our model and show that each aspect of the model is essential. Our empirical results on a physical world microfinance community demonstrate excellent model fits compared to baseline models.

When answering complex questions, large language models (LLMs) may produce answers that do not satisfy all criteria of the question. While existing self-evaluation techniques aim to detect if such answers are correct, these techniques are unable to determine which criteria of the question are satisfied by the generated answers. To address this issue, we propose answer-based claim decomposition (ABCD), a prompting strategy that decomposes questions into a series of true/false claims that can be used to verify which criteria of the input question an answer satisfies. Using the decomposed ABCD claims, we perform fine-grained self-evaluation. Through preliminary experiments on three datasets, including a newly-collected challenge dataset ObscureQA, we find that GPT-3.5 has some ability to determine to what extent its answer satisfies the criteria of the input question, and can give insights into the errors and knowledge gaps of the model.

Large Language Models (LLMs) have demonstrated remarkable performance on coding related tasks, particularly on assisting humans in programming and facilitating programming automation. However, existing benchmarks for evaluating the code understanding and generation capacities of LLMs suffer from severe limitations. First, most benchmarks are deficient as they focus on a narrow range of popular programming languages and specific tasks, whereas the real-world software development scenarios show dire need to implement systems with multilingual programming environments to satisfy diverse requirements. Practical programming practices also strongly expect multi-task settings for testing coding capabilities of LLMs comprehensively and robustly. Second, most benchmarks also fail to consider the actual executability and the consistency of execution results of the generated code. To bridge these gaps between existing benchmarks and expectations from practical applications, we introduce CodeScope, an execution-based, multilingual, multi-task, multi-dimensional evaluation benchmark for comprehensively gauging LLM capabilities on coding tasks. CodeScope covers 43 programming languages and 8 coding tasks. It evaluates the coding performance of LLMs from three dimensions (perspectives): difficulty, efficiency, and length. To facilitate execution-based evaluations of code generation, we develop MultiCodeEngine, an automated code execution engine that supports 14 programming languages. Finally, we systematically evaluate and analyze 8 mainstream LLMs on CodeScope tasks and demonstrate the superior breadth …

We present InfoMotif, a new semi-supervised, motif-regularized, learning framework over graphs. We overcome two key limitations of message passing in popular graph neural networks (GNNs): localization (a k-layer GNN cannot utilize features outside the k-hop neighborhood of the labeled training nodes) and over-smoothed (structurally indistinguishable) representations. We formulate attributed structural roles of nodes based on their occurrence in different network motifs, independent of network proximity. Network motifs are higher-order structures indicating connectivity patterns between nodes and are crucial to the organization of complex networks. Two nodes share attributed structural roles if they participate in topologically similar motif instances over covarying sets of attributes. InfoMotif achieves architecture-agnostic regularization of arbitrary GNNs through novel self-supervised learning objectives based on …

Conversational surveys, where an agent asks open-ended questions through natural language interfaces, offer a new way to collect information from people. A good follow-up question in a conversational survey prompts high-quality information and delivers engaging experiences. However, generating high-quality follow-up questions on the fly is a non-trivial task. The agent needs to understand the diverse and complex participant responses, adhere to the survey goal, and generate clear and coherent questions. In this study, we propose a knowledge-driven follow-up question generation framework. The framework combines a knowledge selection module to identify salient topics in participants' responses and a generative model guided by selected knowledge entity-relation pairs. To investigate the effectiveness of the proposed framework, we build a new dataset for open-domain follow-up question generation and present a new set of reference-free evaluation metrics based on Gricean Maxim. Our experiments demonstrate that our framework outperforms a GPT-based baseline in both objective evaluation and human-expert evaluation.

In this paper, we propose MuTATE, a Multi-Task Augmented approach to learn Transferable Embeddings of knowledge graphs. Previous knowledge graph representation techniques either employ task-agnostic geometric hypotheses to learn informative node embeddings or integrate task-specific learning objectives like attribute prediction. In contrast, our framework unifies multiple co-dependent learning objectives with knowledge graph enrichment. We define co-dependence as multiple tasks that extract covariant distributions of entities and their relationships for prediction or regression objectives. We facilitate knowledge transfer in this setting: tasksgraph, graphtasks, and task-1task-2 via task-specific residual functions to specialize the node embeddings for each task, motivated by domain-shift theory. We show 5% relative gains over state-of-the-art knowledge graph embedding baselines on two public …

Researchers often query online social platforms through their application programming interfaces (API) to find target populations such as people with mental illness (De Choudhury et al. in Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing, CSCW ’17. ACM, New York, pp 353–369, https://doi.org/10.1145/2998181.2998220 , 2017) and jazz musicians (Heckathorn and Jeffri in Poetics 28(4):307, 2001). Entities of such target population satisfy a property that is typically identified using an oracle (human or a pre-trained classifier). When the property of the target entities is not directly queryable via the API, we refer to the property as ‘hidden’ and the population as hidden population. Our objective in this paper is to sample such target populations that satisfy a certain property that can be verified by an oracle (typically a pre-trained …

The quality variance in user-generated content is a major bottleneck to serving communities on online platforms. Current content ranking methods primarily evaluate text and non-textual content features of each user post in isolation. In this paper, we demonstrate the utility of considering the implicit and explicit relational aspects across user content to assess their quality. First, we develop a modular platform-agnostic framework to represent the contrastive (or competing) and similarity-based relational aspects of user-generated content via independently induced content graphs. Second, we develop two complementary graph convolutional operators that enable feature contrast for competing content and feature smoothing/sharing for similar content. Depending on the edge semantics of each content graph, we embed its nodes via one of the above two mechanisms. We also show that our contrastive operator creates …