OpenRaiser/Intern-Atlas huggingface dataset:
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
Venue: arXiv cs.CL 2023
Year: 2023

While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively. To facilitate development of such systems, we release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent. To collect this dataset, each user utterance from MultiWOZ 2.2 was replaced with a small chat between two users that is semantically and pragmatically consistent with the original user utterance, thus resulting in the same dialogue state and system response. These dialogues reflect interesting dynamics of collaborative decision-making in task-oriented scenarios, e.g., social chatter and deliberation. Supported by this data, we propose the novel task of multi-user contextual query rewriting: to rewrite a task-oriented chat between two users as a concise task-oriented query that retains only task-relevant information and that is directly consumable by the dialogue system. We demonstrate that in multi-user dialogues, using predicted rewrites substantially improves dialogue state tracking without modifying existing dialogue systems that are trained for single-user dialogues. Further, this method surpasses training a medium-sized model directly on multi-user dialogues and generalizes to unseen domains.
LoRa Multi-Hop Networks for Monitoring Underground Mining Environments
Venue: arXiv cs.NI 2023
Year: 2023

Internet of Things applications have gained widespread recognition for their efficacy in typical scenarios, such as smart cities and smart healthcare. Nonetheless, there exist numerous unconventional situations where IoT technologies have not yet been massively applied, though they can be extremely useful. One of such domains is the underground mining sector, where enhancing automation monitoring through wireless communications is of essential significance. In this paper, we focus on the development, implementation, and evaluation of a LoRa-based multi-hop network tailored specifically for monitoring underground mining environments, where data traffic is sporadic, but energy efficiency is of paramount importance. We hence define a synchronization framework that makes it possible for the nodes to sleep for most of the time, waking up only when they need to exchange traffic. Notably, our network achieves a sub 40us proven synchronization accuracy between parent-child pairs with minimum overhead for diverse topologies, rendering it highly viable for subterranean operations. Furthermore, for proper network dimensioning, we model the interplay between network's throughput, frame size, and sampling periods of potential applications. Moreover, we propose a model to estimate devices' duty cycle based on their position within the multi-hop network, along with empirical observations for its validation. The proposed models make it possible to optimize the network's performance to meet the specific demands that can arise from the different subterranean use cases, in which robustness, low power operation, and compliance with radio-frequency regulations are key requirements that must be met.
Fast and Delay-Robust Multimodal Journey Planning
Venue: arXiv cs.DS 2023
Year: 2023

We study journey planning in multimodal networks consisting of public transit plus an unrestricted transfer mode (e.g., walking or cycling). In order to provide good results in practice, algorithms must account for vehicle delays. Delay-responsive algorithms receive a continuous stream of delay updates and must return optimal journeys in the currently known delay scenario. Updates are incorporated in an update phase, which must be fast (e.g., a few seconds). The fastest known approach for multimodal journey planning is ULTRA, which precomputes shortcuts representing transfers between vehicles. This allows query algorithms to find Pareto-optimal journeys regarding arrival time and the number of public transit trips without any performance loss compared to pure public transit networks. However, the precomputation phase does not account for delays and is too slow to rerun during the update phase. We present Delay-ULTRA, a delay-responsive variant of ULTRA. Since accounting for all theoretically possible delays would yield an impractically large set of shortcuts, our approach precomputes shortcuts that are provably sufficient as long as delays do not exceed a configurable limit (e.g., 5 minutes). To handle delays above the limit (which are less frequent in practice), we propose a heuristic search for missing shortcuts that is fast enough to be run during the update phase. Our experimental evaluation on real-world data shows that Delay-ULTRA fails to find less than 0.02% of optimal journeys on metropolitan and mid-sized country networks, and 0.16% on the much larger Germany network. Considering that the available delay information in realistic applications is never perfectly accurate, these error rates are negligible. Query speed is at most twice as slow as ULTRA without delay information, and up to 8 times faster than the fastest exact algorithm, which does not require a preprocessing phase.
Breaking the Token Barrier: Chunking and Convolution for Efficient Long Text Classification with BERT
Venue: arXiv cs.CL 2023
Year: 2023

Transformer-based models, specifically BERT, have propelled research in various NLP tasks. However, these models are limited to a maximum token limit of 512 tokens. Consequently, this makes it non-trivial to apply it in a practical setting with long input. Various complex methods have claimed to overcome this limit, but recent research questions the efficacy of these models across different classification tasks. These complex architectures evaluated on carefully curated long datasets perform at par or worse than simple baselines. In this work, we propose a relatively simple extension to vanilla BERT architecture called ChunkBERT that allows finetuning of any pretrained models to perform inference on arbitrarily long text. The proposed method is based on chunking token representations and CNN layers, making it compatible with any pre-trained BERT. We evaluate chunkBERT exclusively on a benchmark for comparing long-text classification models across a variety of tasks (including binary classification, multi-class classification, and multi-label classification). A BERT model finetuned using the ChunkBERT method performs consistently across long samples in the benchmark while utilizing only a fraction (6.25\%) of the original memory footprint. These findings suggest that efficient finetuning and inference can be achieved through simple modifications to pre-trained BERT models.
Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding
Venue: arXiv cs.CL 2023
Year: 2023

In the era of the Internet of Things (IoT), the retrieval of relevant medical information has become essential for efficient clinical decision-making. This paper introduces MedFusionRank, a novel approach to zero-shot medical information retrieval (MIR) that combines the strengths of pre-trained language models and statistical methods while addressing their limitations. The proposed approach leverages a pre-trained BERT-style model to extract compact yet informative keywords. These keywords are then enriched with domain knowledge by linking them to conceptual entities within a medical knowledge graph. Experimental evaluations on medical datasets demonstrate MedFusion Rank's superior performance over existing methods, with promising results with a variety of evaluation metrics. MedFusionRank demonstrates efficacy in retrieving relevant information, even from short or single-term queries.
Defining a New NLP Playground
Venue: arXiv cs.CL 2023
Year: 2023

The recent explosion of performance of large language models (LLMs) has changed the field of Natural Language Processing (NLP) more abruptly and seismically than any other shift in the field's 80-year history. This has resulted in concerns that the field will become homogenized and resource-intensive. The new status quo has put many academic researchers, especially PhD students, at a disadvantage. This paper aims to define a new NLP playground by proposing 20+ PhD-dissertation-worthy research directions, covering theoretical analysis, new and challenging problems, learning paradigms, and interdisciplinary applications.
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
Venue: arXiv cs.LG 2023
Year: 2023

Offline reinforcement learning (RL) can in principle synthesize more optimal behavior from a dataset consisting only of suboptimal trials. One way that this can happen is by "stitching" together the best parts of otherwise suboptimal trajectories that overlap on similar states, to create new behaviors where each individual state is in-distribution, but the overall returns are higher. However, in many interesting and complex applications, such as autonomous navigation and dialogue systems, the state is partially observed. Even worse, the state representation is unknown or not easy to define. In such cases, policies and value functions are often conditioned on observation histories instead of states. In these cases, it is not clear if the same kind of "stitching" is feasible at the level of observation histories, since two different trajectories would always have different histories, and thus "similar states" that might lead to effective stitching cannot be leveraged. Theoretically, we show that standard offline RL algorithms conditioned on observation histories suffer from poor sample complexity, in accordance with the above intuition. We then identify sufficient conditions under which offline RL can still be efficient -- intuitively, it needs to learn a compact representation of history comprising only features relevant for action selection. We introduce a bisimulation loss that captures the extent to which this happens, and propose that offline RL can explicitly optimize this loss to aid worst-case sample complexity. Empirically, we show that across a variety of tasks either our proposed loss improves performance, or the value of this loss is already minimized as a consequence of standard offline RL, indicating that it correlates well with good performance.
Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

Deep neural networks have exhibited remarkable performance in various domains. However, the reliance of these models on spurious features has raised concerns about their reliability. A promising solution to this problem is last-layer retraining, which involves retraining the linear classifier head on a small subset of data without spurious cues. Nevertheless, selecting this subset requires human supervision, which reduces its scalability. Moreover, spurious cues may still exist in the selected subset. As a solution to this problem, we propose a novel ranking framework that leverages an open vocabulary object detection technique to identify images without spurious cues. More specifically, we use the object detector as a measure to score the presence of the target object in the images. Next, the images are sorted based on this score, and the last-layer of the model is retrained on a subset of the data with the highest scores. Our experiments on the ImageNet-1k dataset demonstrate the effectiveness of this ranking framework in sorting images based on spuriousness and using them for last-layer retraining.
Overcoming membrane locking in quadratic NURBS-based discretizations of linear Kirchhoff-Love shells: CAS elements
Venue: arXiv cs.CE 2023
Year: 2023

Quadratic NURBS-based discretizations of the Galerkin method suffer from membrane locking when applied to Kirchhoff-Love shell formulations. Membrane locking causes not only smaller displacements than expected, but also large-amplitude spurious oscillations of the membrane forces. Continuous-assumed-strain (CAS) elements have been recently introduced to remove membrane locking in quadratic NURBS-based discretizations of linear plane curved Kirchhoff rods (Casquero et al., CMAME, 2022). In this work, we generalize CAS elements to vanquish membrane locking in quadratic NURBS-based discretizations of linear Kirchhoff-Love shells. CAS elements bilinearly interpolate the membrane strains at the four corners of each element. Thus, the assumed strains have C0 continuity across element boundaries. To the best of the authors' knowledge, CAS elements are the first assumed-strain treatment to effectively overcome membrane locking in quadratic NURBS-based discretizations of Kirchhoff-Love shells while satisfying the following important characteristics for computational efficiency: (1) No additional degrees of freedom are added, (2) No additional systems of algebraic equations need to be solved, (3) No matrix multiplications or matrix inversions are needed to obtain the stiffness matrix, and (4) The nonzero pattern of the stiffness matrix is preserved. The benchmark problems show that CAS elements, using either 2x2 or 3x3 Gauss-Legendre quadrature points per element, are an effective locking treatment since this element type results in more accurate displacements for coarse meshes and excises the spurious oscillations of the membrane forces. The benchmark problems also show that CAS elements outperform state-of-the-art element types based on Lagrange polynomials equipped with either assumed-strain or reduced-integration locking treatments.
Towards A Natural Language Interface for Flexible Multi-Agent Task Assignment
Venue: arXiv cs.RO 2023
Year: 2023

Task assignment and scheduling algorithms are powerful tools for autonomously coordinating large teams of robotic or AI agents. However, the decisions these system make often rely on components designed by domain experts, which can be difficult for non-technical end-users to understand or modify to their own ends. In this paper we propose a preliminary design for a flexible natural language interface for a task assignment system. The goal of our approach is both to grant users more control over a task assignment system's decision process, as well as render these decisions more transparent. Users can direct the task assignment system via natural language commands, which are applied as constraints to a mixed-integer linear program (MILP) using a large language model (LLM). Additionally, our proposed system can alert users to potential issues with their commands, and engage them in a corrective dialogue in order to find a viable solution. We conclude with a description of our planned user-evaluation in the simulated environment Overcooked and describe next steps towards developing a flexible and transparent task allocation system.
Longer Fixations, More Computation: Gaze-Guided Recurrent Neural Networks
Venue: arXiv cs.CL 2023
Year: 2023

Humans read texts at a varying pace, while machine learning models treat each token in the same way in terms of a computational process. Therefore, we ask, does it help to make models act more like humans? In this paper, we convert this intuition into a set of novel models with fixation-guided parallel RNNs or layers and conduct various experiments on language modeling and sentiment analysis tasks to test their effectiveness, thus providing empirical validation for this intuition. Our proposed models achieve good performance on the language modeling task, considerably surpassing the baseline model. In addition, we find that, interestingly, the fixation duration predicted by neural networks bears some resemblance to humans' fixation. Without any explicit guidance, the model makes similar choices to humans. We also investigate the reasons for the differences between them, which explain why "model fixations" are often more suitable than human fixations, when used to guide language models.
Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language
Venue: arXiv cs.CL 2023
Year: 2023

Counterspeech, i.e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship. However, properly countering hateful language requires countering and dispelling the underlying inaccurate stereotypes implied by such language. In this work, we draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. We first examine the convincingness of each of these strategies through a user study, and then compare their usages in both human- and machine-generated counterspeech datasets. Our results show that human-written counterspeech uses countering strategies that are more specific to the implied stereotype (e.g., counter examples to the stereotype, external factors about the stereotype's origins), whereas machine-generated counterspeech uses less specific strategies (e.g., generally denouncing the hatefulness of speech). Furthermore, machine-generated counterspeech often employs strategies that humans deem less convincing compared to human-produced counterspeech. Our findings point to the importance of accounting for the underlying stereotypical implications of speech when generating counterspeech and for better machine reasoning about anti-stereotypical examples.
Robust Safety Classifier for Large Language Models: Adversarial Prompt Shield
Venue: arXiv cs.CL 2023
Year: 2023

Large Language Models' safety remains a critical concern due to their vulnerability to adversarial attacks, which can prompt these systems to produce harmful responses. In the heart of these systems lies a safety classifier, a computational model trained to discern and mitigate potentially harmful, offensive, or unethical outputs. However, contemporary safety classifiers, despite their potential, often fail when exposed to inputs infused with adversarial noise. In response, our study introduces the Adversarial Prompt Shield (APS), a lightweight model that excels in detection accuracy and demonstrates resilience against adversarial prompts. Additionally, we propose novel strategies for autonomously generating adversarial training datasets, named Bot Adversarial Noisy Dialogue (BAND) datasets. These datasets are designed to fortify the safety classifier's robustness, and we investigate the consequences of incorporating adversarial examples into the training process. Through evaluations involving Large Language Models, we demonstrate that our classifier has the potential to decrease the attack success rate resulting from adversarial attacks by up to 60%. This advancement paves the way for the next generation of more reliable and resilient conversational agents.
XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision
Venue: arXiv cs.CL 2023
Year: 2023

Text classification aims to effectively categorize documents into pre-defined categories. Traditional methods for text classification often rely on large amounts of manually annotated training data, making the process time-consuming and labor-intensive. To address this issue, recent studies have focused on weakly-supervised and extremely weakly-supervised settings, which require minimal or no human annotation, respectively. In previous methods of weakly supervised text classification, pseudo-training data is generated by assigning pseudo-labels to documents based on their alignment (e.g., keyword matching) with specific classes. However, these methods ignore the importance of incorporating the explanations of the generated pseudo-labels, or saliency of individual words, as additional guidance during the text classification training process. To address this limitation, we propose XAI-CLASS, a novel explanation-enhanced extremely weakly-supervised text classification method that incorporates word saliency prediction as an auxiliary task. XAI-CLASS begins by employing a multi-round question-answering process to generate pseudo-training data that promotes the mutual enhancement of class labels and corresponding explanation word generation. This pseudo-training data is then used to train a multi-task framework that simultaneously learns both text classification and word saliency prediction. Extensive experiments on several weakly-supervised text classification datasets show that XAI-CLASS outperforms other weakly-supervised text classification methods significantly. Moreover, experiments demonstrate that XAI-CLASS enhances both model performance and explainability.
Active Neural Topological Mapping for Multi-Agent Exploration
Venue: arXiv cs.RO 2023
Year: 2023

This paper investigates the multi-agent cooperative exploration problem, which requires multiple agents to explore an unseen environment via sensory signals in a limited time. A popular approach to exploration tasks is to combine active mapping with planning. Metric maps capture the details of the spatial representation, but are with high communication traffic and may vary significantly between scenarios, resulting in inferior generalization. Topological maps are a promising alternative as they consist only of nodes and edges with abstract but essential information and are less influenced by the scene structures. However, most existing topology-based exploration tasks utilize classical methods for planning, which are time-consuming and sub-optimal due to their handcrafted design. Deep reinforcement learning (DRL) has shown great potential for learning (near) optimal policies through fast end-to-end inference. In this paper, we propose Multi-Agent Neural Topological Mapping (MANTM) to improve exploration efficiency and generalization for multi-agent exploration tasks. MANTM mainly comprises a Topological Mapper and a novel RL-based Hierarchical Topological Planner (HTP). The Topological Mapper employs a visual encoder and distance-based heuristics to construct a graph containing main nodes and their corresponding ghost nodes. The HTP leverages graph neural networks to capture correlations between agents and graph nodes in a coarse-to-fine manner for effective global goal selection. Extensive experiments conducted in a physically-realistic simulator, Habitat, demonstrate that MANTM reduces the steps by at least 26.40% over planning-based baselines and by at least 7.63% over RL-based competitors in unseen scenarios.
How is Software Reuse Discussed in Stack Overflow?
Venue: arXiv cs.SE 2023
Year: 2023

Software reuse is a crucial external quality attribute targeted by open-source and commercial projects. Despite that software reuse has experienced an increased adoption throughout the years, little is known about what aspects of code reuse developers discuss. In this paper, we present an empirical study of 1,409 posts to better understand the challenges developers face when reusing code. Our findings show that 'visual studio' is the top occurring bigrams for question posts, and there are frequent design patterns utilized by developers for the purpose of reuse. We envision our findings enabling researchers to develop guidelines to be utilized to foster software reuse.