DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling

Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we propose DynaMo, a suite of multi-token prediction language models that reduce net inference times. Our models $\textit{dynamically}$ predict multiple tokens based on their confidence in the predicted joint probability distribution. We propose a lightweight technique to train these models, leveraging the weights of traditional autoregressive counterparts. Moreover, we propose novel ways to enhance the estimated joint probability to improve text generation quality, namely co-occurrence weighted masking and adaptive thresholding. We also propose systematic qualitative and quantitative methods to rigorously test the quality of generated text for non-autoregressive generation. One of the models in our suite, DynaMo-7.3B-T3, achieves same-quality generated text as the baseline (Pythia-6.9B) while achieving 2.57$\times$ speed-up with only 5.87% and 2.67% parameter and training time overheads, respectively.

Compositional Generalization in Spoken Language Understanding

State-of-the-art spoken language understanding (SLU) models have shown tremendous success in benchmark SLU datasets, yet they still fail in many practical scenario due to the lack of model compositionality when trained on limited training data. In this paper, we study two types of compositionality: (a) novel slot combination, and (b) length generalization. We first conduct in-depth analysis, and find that state-of-the-art SLU models often learn spurious slot correlations during training, which leads to poor performance in both compositional cases. To mitigate these limitations, we create the first compositional splits of benchmark SLU datasets and we propose the first compositional SLU model, including compositional loss and paired training that tackle each compositional case respectively. On both benchmark and compositional splits in ATIS and SNIPS, we show that our compositional SLU model significantly outperforms (up to $5\%$ F1 score) state-of-the-art BERT SLU model.

Token Fusion: Bridging the Gap between Token Pruning and Token Merging

Vision Transformers (ViTs) have emerged as powerful backbones in computer vision, outperforming many traditional CNNs. However, their computational overhead, largely attributed to the self-attention mechanism, makes deployment on resource-constrained edge devices challenging. Multiple solutions rely on token pruning or token merging. In this paper, we introduce "Token Fusion" (ToFu), a method that amalgamates the benefits of both token pruning and token merging. Token pruning proves advantageous when the model exhibits sensitivity to input interpolations, while token merging is effective when the model manifests close to linear responses to inputs. We combine this to propose a new scheme called Token Fusion. Moreover, we tackle the limitations of average merging, which doesn't preserve the intrinsic feature norm, resulting in distributional shifts. To mitigate this, we introduce MLERP merging, a variant of the SLERP technique, tailored to merge multiple tokens while maintaining the norm distribution. ToFu is versatile, applicable to ViTs with or without additional training. Our empirical evaluations indicate that ToFu establishes new benchmarks in both classification and image generation tasks concerning computational efficiency and model accuracy.

Prompt Tuning for Zero-shot Compositional Learning

Open World Compositional Zero-Shot Learning (OW-CZSL) is known to be an extremely challenging task, which aims to recognize unseen compositions formed from seen attributes and objects without any prior assumption of the output space. In order to achieve this goal, a model has to be "smart" and "knowledgeable". To be smart, a model should be good at reasoning the interactions between attributes and objects from the seen compositions. While "knowledgeable" means the model owns "common sense" to the open world that can "foresee" some features of the unseen compositions. Most previous work focuses on the "smart" part, while few of them provided an effective solution to achieve the "knowledgeable" goal. In this paper, we proposed a framework named Multi-Modal Prompt Tuning (MMPT) to inherit the "knowledgeable" property from the large pre-trained vision-language model. Extensive experiments show that our proposed MMPT obtains new state-of-the-art results in OW-CZSL task. On the UT-Zappos dataset, MMPT pushes the AUC score to $29.8$, while the previous best score is $26.5$. On the more challenging MIT-States dataset, the AUC score of MMPT is 1.5 times better than the current state-of-the-art.

Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters

Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only providing a few example images for each concept. This setting is known as continual diffusion. Here, we ask the question: Can we scale these methods to longer concept sequences without forgetting? Although prior work mitigates the forgetting of previously learned concepts, we show that its capacity to learn new tasks reaches saturation over longer sequences. We address this challenge by introducing a novel method, STack-And-Mask INcremental Adapters (STAMINA), which is composed of low-ranked attention-masked adapters and customized MLP tokens. STAMINA is designed to enhance the robust fine-tuning properties of LoRA for sequential concept learning via learnable hard-attention masks parameterized with low rank MLPs, enabling precise, scalable learning via sparse adaptation. Notably, all introduced trainable parameters can be folded back into the model after training, inducing no additional inference parameter costs. We show that STAMINA outperforms the prior SOTA for the setting of text-to-image continual customization on a 50-concept benchmark composed of landmarks and human faces, with no stored replay data. Additionally, we extended our method to the setting of continual learning for image classification, demonstrating that our gains also translate to state-of-the-art performance in this standard benchmark.

CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss

This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pairwise data. The learnt models in the latter domain can then be used for a diverse set of tasks in a zero-shot way, similar to ``Contrastive Language-Image Pre-training (CLIP)'' and ``Locked-image Tuning (LiT)'' that have recently gained considerable attention. Most existing works for cross-modal representation alignment (including CLIP and LiT) use the standard contrastive training objective, which employs sets of positive and negative examples to align similar and repel dissimilar training data samples. However, similarity amongst training examples has a more continuous nature, thus calling for a more `non-binary' treatment. To address this, we propose a novel loss function called Continuously Weighted Contrastive Loss (CWCL) that employs a continuous measure of similarity. With CWCL, we seek to align the embedding space of one modality with another. Owing to the continuous nature of similarity in the proposed loss function, these models outperform existing methods for 0-shot transfer across multiple models, datasets and modalities. Particularly, we consider the modality pairs of image-text and speech-text and our models achieve 5-8% (absolute) improvement over previous state-of-the-art methods in 0-shot image classification and 20-30% (absolute) improvement in 0-shot speech-to-intent classification and keyword classification.

TrojPrompt: A Black-box Trojan Attack on Pre-trained Language Models

Prompt learning has been proven to be highly effective in improving pre-trained language model (PLM) adaptability, surpassing conventional fine-tuning paradigms, and showing exceptional promise in an ever-growing landscape of applications and APIs tailored for few-shot learning scenarios. Despite the growing prominence of prompt learning-based APIs, their security concerns remain underexplored. In this paper, we undertake a pioneering study on the Trojan susceptibility of prompt-learning PLM APIs. We identified several key challenges, including discrete-prompt, few-shot, and black-box settings, which limit the applicability of existing backdoor attacks. To address these challenges, we propose TrojPrompt, an automatic and black-box framework to effectively generate universal and stealthy triggers and insert Trojans into hard prompts. Specifically, we propose a universal API-driven trigger discovery algorithm for generating universal triggers for various inputs by querying victim PLM APIs using few-shot data samples. Furthermore, we introduce a novel progressive trojan poisoning algorithm designed to generate poisoned prompts that retain efficacy and transferability across a diverse range of models. Our experiments and results demonstrate TrojPrompt's capacity to effectively insert Trojans into text prompts in real-world black-box PLM APIs, while maintaining exceptional performance on clean test sets and significantly outperforming baseline models. Our work sheds light on the potential security risks in current models and offers a potential defensive approach.

Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA

Recent works demonstrate a remarkable ability to customize text-to-image diffusion models while only providing a few example images. What happens if you try to customize such models using multiple, fine-grained concepts in a sequential (i.e., continual) manner? In our work, we show that recent state-of-the-art customization of text-to-image models suffer from catastrophic forgetting when new concepts arrive sequentially. Specifically, when adding a new concept, the ability to generate high quality images of past, similar concepts degrade. To circumvent this forgetting, we propose a new method, C-LoRA, composed of a continually self-regularized low-rank adaptation in cross attention layers of the popular Stable Diffusion model. Furthermore, we use customization prompts which do not include the word of the customized object (i.e., "person" for a human face dataset) and are initialized as completely random embeddings. Importantly, our method induces only marginal additional parameter costs and requires no storage of user data for replay. We show that C-LoRA not only outperforms several baselines for our proposed setting of text-to-image continual customization, which we refer to as Continual Diffusion, but that we achieve a new state-of-the-art in the well-established rehearsal-free continual learning setting for image classification. The high achieving performance of C-LoRA in two separate domains positions it as a compelling solution for a wide range of applications, and we believe it has significant potential for practical impact.

To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement

Keyword spotting systems continuously process audio streams to detect keywords. One of the most challenging tasks in designing such systems is to reduce False Alarm (FA) which happens when the system falsely registers a keyword despite the keyword not being uttered. In this paper, we propose a simple yet elegant solution to this problem that follows from the law of total probability. We show that existing deep keyword spotting mechanisms can be improved by Successive Refinement, where the system first classifies whether the input audio is speech or not, followed by whether the input is keyword-like or not, and finally classifies which keyword was uttered. We show across multiple models with size ranging from 13K parameters to 2.41M parameters, the successive refinement technique reduces FA by up to a factor of 8 on in-domain held-out FA data, and up to a factor of 7 on out-of-domain (OOD) FA data. Further, our proposed approach is "plug-and-play" and can be applied to any deep keyword spotting model.

ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation

The ability to accurately locate and navigate to a specific object is a crucial capability for embodied agents that operate in the real world and interact with objects to complete tasks. Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments. In this work, we present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC), that transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience nor any other training on the visual environments. First, ESC leverages a pre-trained vision and language model for open-world prompt-based grounding and a pre-trained commonsense language model for room and object reasoning. Then ESC converts commonsense knowledge into navigation actions by modeling it as soft logic predicates for efficient exploration. Extensive experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines, and achieves new state-of-the-art results for zero-shot object navigation (e.g., 225\% relative Success Rate improvement than CoW on MP3D).