Semantic Search Evaluation

We propose a novel method for evaluating the performance of a content search system that measures the semantic match between a query and the results returned by the search system. We introduce a metric called "on-topic rate" to measure the percentage of results that are relevant to the query. To achieve this, we design a pipeline that defines a golden query set, retrieves the top K results for each query, and sends calls to GPT 3.5 with formulated prompts. Our semantic evaluation pipeline helps identify common failure patterns and goals against the metric for relevance improvements.

Lie Theory Based Optimization for Unified State Planning of Mobile Manipulators

Mobile manipulators are finding use in numerous practical applications. The current issues with mobile manipulation are the large state space owing to the mobile base and the challenge of modeling high degree of freedom systems. It is critical to devise fast and accurate algorithms that generate smooth motion plans for such mobile manipulators. Existing techniques attempt to solve this problem but focus on separating the motion of the base and manipulator. We propose an approach using Lie theory to find the inverse kinematic constraints by converting the kinematic model, created using screw coordinates, between its Lie group and vector representation. An optimization function is devised to solve for the desired joint states of the entire mobile manipulator. This allows the motion of the mobile base and manipulator to be planned and applied in unison resulting in a smooth and accurate motion plan. The performance of the proposed state planner is validated on simulated mobile manipulators in an analytical experiment. Our solver is available with further derivations and results at https://github.com/peleito/slithers.

Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives

Robotic tasks often require multiple manipulators to enhance task efficiency and speed, but this increases complexity in terms of collaboration, collision avoidance, and the expanded state-action space. To address these challenges, we propose a multi-level approach combining Reinforcement Learning (RL) and Dynamic Movement Primitives (DMP) to generate adaptive, real-time trajectories for new tasks in dynamic environments using a demonstration library. This method ensures collision-free trajectory generation and efficient collaborative motion planning. We validate the approach through experiments in the PyBullet simulation environment with UR5e robotic manipulators.

Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs

Large language models can memorize and repeat their training data, causing privacy and copyright risks. To mitigate memorization, we introduce a subtle modification to the next-token training objective that we call the goldfish loss. During training, a randomly sampled subset of tokens are excluded from the loss computation. These dropped tokens are not memorized by the model, which prevents verbatim reproduction of a complete chain of tokens from the training set. We run extensive experiments training billion-scale Llama-2 models, both pre-trained and trained from scratch, and demonstrate significant reductions in extractable memorization with little to no impact on downstream benchmarks.

Loki: Low-Rank Keys for Efficient Sparse Attention

Inference on large language models can be expensive in terms of the compute and memory costs involved, especially when long sequence lengths are used. In particular, the self-attention mechanism used in such models contributes significantly to these costs, which has resulted in several recent works that propose sparse attention approximations for inference. In this work, we propose to approximate the self-attention computation by focusing on the dimensionality of key vectors computed in the attention block. Our analysis reveals that the key vectors lie in a significantly lower-dimensional space, consistently across several datasets and models. Exploiting this observation, we propose Loki, a novel sparse attention method that ranks and selects tokens in the KV-cache based on attention scores computed in low-dimensional space. Our evaluations show that Loki is able to maintain the efficacy of the models better than other popular approximation methods, while speeding up the attention computation due to reduced data movement (load/store) and compute costs.

HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text

In this paper, we discuss the development of an annotation schema to build datasets for evaluating the offline harm potential of social media texts. We define "harm potential" as the potential for an online public post to cause real-world physical harm (i.e., violence). Understanding that real-world violence is often spurred by a web of triggers, often combining several online tactics and pre-existing intersectional fissures in the social milieu, to result in targeted physical violence, we do not focus on any single divisive aspect (i.e., caste, gender, religion, or other identities of the victim and perpetrators) nor do we focus on just hate speech or mis/dis-information. Rather, our understanding of the intersectional causes of such triggers focuses our attempt at measuring the harm potential of online content, irrespective of whether it is hateful or not. In this paper, we discuss the development of a framework/annotation schema that allows annotating the data with different aspects of the text including its socio-political grounding and intent of the speaker (as expressed through mood and modality) that together contribute to it being a trigger for offline harm. We also give a comparative analysis and mapping of our framework with some of the existing frameworks.

A Framework for Realistic Simulation of Daily Human Activity

For social robots like Astro which interact with and adapt to the daily movements of users within the home, realistic simulation of human activity is needed for feature development and testing. This paper presents a framework for simulating daily human activity patterns in home environments at scale, supporting manual configurability of different personas or activity patterns, variation of activity timings, and testing on multiple home layouts. We introduce a method for specifying day-to-day variation in schedules and present a bidirectional constraint propagation algorithm for generating schedules from templates. We validate the expressive power of our framework through a use case scenario analysis and demonstrate that our method can be used to generate data closely resembling human behavior from three public datasets and a self-collected dataset. Our contribution supports systematic testing of social robot behaviors at scale, enables procedural generation of synthetic datasets of human movement in different households, and can help minimize bias in training data, leading to more robust and effective robots for home environments.

Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization

Despite their better convergence properties compared to first-order optimizers, second-order optimizers for deep learning have been less popular due to their significant computational costs. The primary efficiency bottleneck in such optimizers is matrix inverse calculations in the preconditioning step, which are expensive to compute on GPUs. In this paper, we introduce Jorge, a second-order optimizer that promises the best of both worlds -- rapid convergence benefits of second-order methods, and high computational efficiency typical of first-order methods. We address the primary computational bottleneck of computing matrix inverses by completely eliminating them using an approximation of the preconditioner computation. This makes Jorge extremely efficient on GPUs in terms of wall-clock time. Further, we describe an approach to determine Jorge's hyperparameters directly from a well-tuned SGD baseline, thereby significantly minimizing tuning efforts. Our empirical evaluations demonstrate the distinct advantages of using Jorge, outperforming state-of-the-art optimizers such as SGD, AdamW, and Shampoo across multiple deep learning models, both in terms of sample efficiency and wall-clock time.

Communication-minimizing Asynchronous Tensor Parallelism

As state-of-the-art neural networks scale to billions of parameters, designing parallel algorithms that can train these networks efficiently on multi-GPU clusters has become critical. This paper presents Tensor3D, a novel three-dimensional (3D) approach to parallelize tensor computations, that strives to minimize the idle time incurred due to communication in parallel training of large multi-billion parameter models. First, we introduce an intelligent distribution of neural network parameters across GPUs that eliminates communication required for satisfying data dependencies of individual layers. Then, we propose a novel overdecomposition of the parallel training process, using which we achieve significant overlap of communication with computation, thereby reducing GPU idle time. Finally, we present a communication model, which helps users identify communication optimal decompositions of available hardware resources for a given neural network. For a 28B parameter CNN on 256 A100 GPUs, Tensor3D improves the training time by nearly 60% as compared to Megatron-LM.

A Novel Tensor-Expert Hybrid Parallelism Approach to Scale Mixture-of-Experts Training

A new neural network architecture called Mixture-of-Experts (MoE) has been proposed recently that increases the parameters of a neural network (the base model) by adding sparsely activated expert blocks, without changing the total number of floating point operations for training or inference. In theory, this architecture allows us to train arbitrarily large models while keeping the computational costs same as that of the base model. However, beyond 64 to 128 experts blocks, prior work has observed diminishing returns in the test accuracies of these MoE models. Thus, training high quality MoE models requires us to scale the size of the base models, along with the number of expert blocks. In this work, we propose a novel, three-dimensional, hybrid parallel algorithm that combines tensor, expert, and data parallelism to enable the training of MoE models with 4-8x larger base models than the current state-of-the-art -- DeepSpeed-MoE. We propose memory optimizations in the optimizer step, and communication optimizations that eliminate redundant movement of data. Removing these redundancies provides a speedup of nearly 21%. When training a 40 billion parameter MoE model (6.7 billion base model with 16 experts) on 128 V100 GPUs, our optimizations significantly improve the peak half precision flop/s from 20% to 27%.