Point cloud-based 3D sparse convolution networks are widely employed to process voxel features efficiently. However, the irregularity of voxel sparsity poses significant challenges, leading to increased hardware complexity and inefficiencies. We propose an algorithm-hardware co-design for sparse 3D convolution. At the algorithm level, an on-the-fly thresholdbased voxel skipping is adopted, enhancing efficiency. At the hardware level, a hierarchical 3-stage Voxel Search and Skipping is developed to systematically narrow down the non-zero search space, enhancing both performance and hardware utilization. We implemented the proposed accelerator in a 65 nm process to demonstrate a 77.7% reduction in delay compared to a baseline design, which does not support the proposed sparsity adaptations. The proposed system also achieved the $1.34 \times$ and $2.22 \times$ higher energy efficiency and throughput as compared to the state-ofarts.

The rapid growth of online shopping stores, such as Amazon, has led to services reaching billions of people worldwide. With global retail sales exceeding $6 trillion in 2024, customer expectations for personalized and seamless shopping experiences have heightened. Traditional online shopping experiences, such as search and navigation systems, often fall short in addressing complex shopping journeys. Conversational shopping (such as Amazon Rufus) offers a transformative approach by enabling dynamic, multi-turn dialogues that closely resemble human interactions. This allows customers to explore product options, seek clarifications, and receive personalized recommendations, thereby enhancing product discovery and informed decision-making. In this paper, we share our year-long journey of using language models for conversational shopping at Amazon and introduce how we use LLM fine-tuning techniques to enhance LLMs for a conversational shopping experience like Amazon Rufus. We also introduce innovative strategies for training data collection and demonstrate real-world applications, including product recommendations, clarification mechanisms, and internationalization for global customers.

Pre-trained multilingual language models are gaining popularity due to their cross-lingual zero-shot transfer ability, but these models do not perform equally well in all languages. Evaluating task-specific performance of a model in a large number of languages is often a challenge due to lack of labeled data, as is targeting improvements in low performing languages through few-shot learning. We present a tool - LITMUS Predictor - that can make reliable performance projections for a fine-tuned task-specific model in a set of languages without test and training data, and help strategize data labeling efforts to optimize performance and fairness objectives.

Code-switching is the use of more than one language in the same conversation or utterance.Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on cross-lingual and multilingual tasks.We present an evaluation benchmark, GLUECoS, for code-switched languages, that spans several NLP tasks in English-Hindi and English-Spanish.Specifically, our evaluation benchmark includes Language Identification from text, POS tagging, Named Entity Recognition, Sentiment Analysis, Question Answering and a new task for code-switching, Natural Language Inference.We present results on all these tasks using cross-lingual word embedding models and multilingual models.In addition, we fine-tune multilingual models on artificially generated code-switched data.Although multilingual models perform significantly better than cross-lingual models, our results show that in most tasks, across both language pairs, multilingual models fine-tuned on code-switched data perform best, showing that multilingual models can be further optimized for code-switching tasks.

Since its earliest days, harassment and abuse have plagued the Internet. Recent research has focused on in-domain methods to detect abusive content and faces several challenges, most notably the need to obtain large training corpora. In this paper, we introduce a novel computational approach to address this problem called Bag of Communities (BoC)---a technique that leverages large-scale, preexisting data from other Internet communities. We then apply BoC toward identifying abusive behavior within a major Internet community. Specifically, we compute a post's similarity to 9 other communities from 4chan, Reddit, Voat and MetaFilter. We show that a BoC model can be used on communities "off the shelf" with roughly 75% accuracy---no training examples are needed from the target community. A dynamic BoC model achieves 91.18% accuracy after seeing 100,000 human-moderated posts, and uniformly outperforms in-domain methods. Using this conceptual and empirical work, we argue that the BoC approach may allow communities to deal with a range of common problems, like abusive behavior, faster and with fewer engineering resources.

In 2015, Reddit closed several subreddits-foremost among them r/fatpeoplehate and r/CoonTown-due to violations of Reddit's anti-harassment policy. However, the effectiveness of banning as a moderation approach remains unclear: banning might diminish hateful behavior, or it may relocate such behavior to different parts of the site. We study the ban of r/fatpeoplehate and r/CoonTown in terms of its effect on both participating users and affected subreddits. Working from over 100M Reddit posts and comments, we generate hate speech lexicons to examine variations in hate speech usage via causal inference methods. We find that the ban worked for Reddit. More accounts than expected discontinued using the site; those that stayed drastically decreased their hate speech usage-by at least 80%. Though many subreddits saw an influx of r/fatpeoplehate and r/CoonTown "migrants," those subreddits saw no significant changes in hate speech usage. In other words, other subreddits did not inherit the problem. We conclude by reflecting on the apparent success of the ban, discussing implications for online moderation, Reddit and internet communities more broadly.