With the emergence of large language models (LLMs), multimodal models based on LLMs have demonstrated significant potential. Models such as LLaSM, X-LLM, and SpeechGPT exhibit an impressive ability to comprehend and generate human instructions. However, their performance often falters when faced with complex tasks like end-to-end speech translation (E2E-ST), a cross-language and cross-modal translation task. In comparison to single-modal models, multimodal models lag behind in these scenarios. This paper introduces LST, a Large multimodal model designed to excel at the E2E-ST task. LST consists of a speech frontend, an adapter, and a LLM backend. The training of LST consists of two stages: (1) Modality adjustment, where the adapter is tuned to align speech representation with text embedding space, and (2) Downstream task fine-tuning, where both the adapter and LLM model are trained to optimize performance on the E2EST task. Experimental results on the MuST-C speech translation benchmark demonstrate that LST-13B achieves BLEU scores of 30.39/41.55/35.33 on En-De/En-Fr/En-Es language pairs, surpassing previous models and establishing a new state-of-the-art. Additionally, we conduct an in-depth analysis of single-modal model selection and the impact of training strategies, which lays the foundation for future research. We will open up our code and models after review.

Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving, and its robustness to unseen conditions is a requirement to avoid life-critical failures. Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed. However, the nature of a MOT system is manifold - requiring object detection and instance association - and adapting all its components is non-trivial. In this paper, we analyze the effect of domain shift on appearance-based trackers, and introduce DARTH, a holistic test-time adaptation framework for MOT. We propose a detection consistency formulation to adapt object detection in a self-supervised fashion, while adapting the instance appearance representations via our novel patch contrastive loss. We evaluate our method on a variety of domain shifts - including sim-to-real, outdoor-to-indoor, indoor-to-outdoor - and substantially improve the source model performance on all metrics. Code: https://github.com/mattiasegu/darth.

As of September 2023, ChatGPT correctly answers "what is 7+8" with 15, but when asked "7+8=15, True or False" it responds with "False". This inconsistency between generating and validating an answer is prevalent in language models (LMs) and erodes trust. In this paper, we propose a framework for measuring the consistency between generation and validation (which we call generator-validator consistency, or GV-consistency), finding that even GPT-4, a state-of-the-art LM, is GV-consistent only 76% of the time. To improve the consistency of LMs, we propose to finetune on the filtered generator and validator responses that are GV-consistent, and call this approach consistency fine-tuning. We find that this approach improves GV-consistency of Alpaca-30B from 60% to 93%, and the improvement extrapolates to unseen tasks and domains (e.g., GV-consistency for positive style transfers extrapolates to unseen styles like humor). In addition to improving consistency, consistency fine-tuning improves both generator quality and validator accuracy without using any labeled data. Evaluated across 6 tasks, including math questions, knowledge-intensive QA, and instruction following, our method improves the generator quality by 16% and the validator accuracy by 6.3% across all tasks. Language models (LMs) can generate high-quality responses to task prompts; however, the same model can sometimes produce contradictory responses when validating its own answers. For example, in September 2023, ChatGPT correctly responds to "what is 7+8?" with "15", but when prompted "7+8=15, True or False" it responds with "False" A consistent LM that answers "15" to the generator query should also answer "True" to the validator query, and we call this Figure 1: To measure generatorvalidator consistency between generation and validation generatorvalidator consistency, we prompt a LM consistency or GV-consistency. Then, we check if the language models, and it can be applied to a broad range same LM consistently responds to a corresponding of tasks.

This paper explores the robustness of LLMs' preference to their internal memory or the given prompt, which may contain contrasting information in real-world applications due to noise or task settings. To this end, we establish a quantitative benchmarking framework and conduct the role playing intervention to control LLMs' preference. In specific, we define two types of robustness, factual robustness targeting the ability to identify the correct fact from prompts or memory, and decision style to categorize LLMs' behavior in making consistent choices -- assuming there is no definitive "right" answer -- intuitive, dependent, or rational based on cognitive theory. Our findings, derived from extensive experiments on seven open-source and closed-source LLMs, reveal that these models are highly susceptible to misleading prompts, especially for instructing commonsense knowledge. While detailed instructions can mitigate the selection of misleading answers, they also increase the incidence of invalid responses. After Unraveling the preference, we intervene different sized LLMs through specific style of role instruction, showing their varying upper bound of robustness and adaptivity.

Lifelong learning without catastrophic forgetting (i.e., resiliency) remains an open problem for deep neural networks. The prior art mostly focuses on convolutional neural networks. With the increasing dominance of Transformers in deep learning, it is a pressing need to study lifelong learning with Transformers. Due to the complexity of training Transformers in practice, for lifelong learning, a question naturally arises: Can Transformers be learned to grow in a task aware way, that is to be dynamically transformed by introducing lightweight learnable plastic components to the architecture, while retaining the parameter-heavy, but stable components at streaming tasks? To that end, motivated by the lifelong learning capability maintained by the functionality of Hippocampi in human brain, we explore what would be, and how to implement, Artificial Hippocampi (ArtiHippo) in Transformers. We present a method to identify, and learn to grow, ArtiHippo in Vision Transformers (ViTs) for resilient lifelong learning in four aspects: (i) Where to place ArtiHippo to enable plasticity while preserving the core function of ViTs at streaming tasks? (ii) How to represent and realize ArtiHippo to ensure expressivity and adaptivity for tackling tasks of different nature in lifelong learning? (iii) How to learn to grow ArtiHippo to exploit task synergies (i.e., the learned knowledge) and overcome catastrophic forgetting? (iv) How to harness the best of our proposed ArtiHippo and prompting-based approaches? In experiments, we test the proposed method on the challenging Visual Domain Decathlon (VDD) benchmark and the 5-Dataset benchmark under the task-incremental lifelong learning setting. It obtains consistently better performance than the prior art with sensible ArtiHippo learned continually. To our knowledge, it is the first attempt of lifelong learning with ViTs on the challenging VDD benchmark.

Catastrophic forgetting, the phenomenon of forgetting previously learned tasks when learning a new one, is a major hurdle in developing continual learning algorithms. A popular method to alleviate forgetting is to use a memory buffer, which stores a subset of previously learned task examples for use during training on new tasks. The de facto method of filling memory is by randomly selecting previous examples. However, this process could introduce outliers or noisy samples that could hurt the generalization of the model. This paper introduces Memory Outlier Elimination (MOE), a method for identifying and eliminating outliers in the memory buffer by choosing samples from label-homogeneous subpopulations. We show that a space with a high homogeneity is related to a feature space that is more representative of the class distribution. In practice, MOE removes a sample if it is surrounded by samples from different labels. We demonstrate the effectiveness of MOE on CIFAR-10, CIFAR-100, and CORe50, outperforming previous well-known memory population methods.

Knowledge Distillation (KD), which transfers the knowledge of a well-trained large model (teacher) to a small model (student), has become an important area of research for practical deployment of recommender systems. Recently, Relaxed Ranking Distillation (RRD) has shown that distilling the ranking information in the recommendation list significantly improves the performance. However, the method still has limitations in that 1) it does not fully utilize the prediction errors of the student model, which makes the training not fully efficient, and 2) it only distills the user-side ranking information, which provides an insufficient view under the sparse implicit feedback. This paper presents Dual Correction strategy for Distillation (DCD), which transfers the ranking information from the teacher model to the student model in a more efficient manner. Most importantly, DCD uses the discrepancy between the teacher model and the student model predictions to decide which knowledge to be distilled. By doing so, DCD essentially provides the learning guidance tailored to "correcting" what the student model has failed to accurately predict. This process is applied for transferring the ranking information from the user-side as well as the item-side to address sparse implicit user feedback. Our experiments show that the proposed method outperforms the state-of-the-art baselines, and ablation studies validate the effectiveness of each component.

Solving a linear system $Ax=b$ is a fundamental scientific computing primitive for which numerous solvers and preconditioners have been developed. These come with parameters whose optimal values depend on the system being solved and are often impossible or too expensive to identify; thus in practice sub-optimal heuristics are used. We consider the common setting in which many related linear systems need to be solved, e.g. during a single numerical simulation. In this scenario, can we sequentially choose parameters that attain a near-optimal overall number of iterations, without extra matrix computations? We answer in the affirmative for Successive Over-Relaxation (SOR), a standard solver whose parameter $\omega$ has a strong impact on its runtime. For this method, we prove that a bandit online learning algorithm -- using only the number of iterations as feedback -- can select parameters for a sequence of instances such that the overall cost approaches that of the best fixed $\omega$ as the sequence length increases. Furthermore, when given additional structural information, we show that a contextual bandit method asymptotically achieves the performance of the instance-optimal policy, which selects the best $\omega$ for each instance. Our work provides the first learning-theoretic treatment of high-precision linear system solvers and the first end-to-end guarantees for data-driven scientific computing, demonstrating theoretically the potential to speed up numerical methods using well-understood learning algorithms.

Work on continual learning (CL) has largely focused on the problems arising from the dynamically-changing data distribution. However, CL can be decomposed into two sub-problems: (a) shifts in the data distribution, and (b) dealing with the fact that the data is split into chunks and so only a part of the data is available to be trained on at any point in time. In this work, we look at the latter subproblem--the chunking of data--and note that previous analysis of chunking in the CL literature is sparse. We show that chunking is an important part of CL, accounting for around half of the performance drop from offline learning in our experiments. Furthermore, our results reveal that current CL algorithms do not address the chunking sub-problem, only performing as well as plain SGD training when there is no shift in the data distribution. We analyse why performance drops when learning occurs on chunks of data, and find that forgetting, which is often seen to be a problem due to distribution shift, still arises and is a significant problem. Motivated by an analysis of the linear case, we show that per-chunk weight averaging improves performance in the chunking setting and that this performance transfers to the full CL setting. Hence, we argue that work on chunking can help advance CL in general. How should we update a neural network efficiently when we observe new data?

In the past years, AlphaFold has revolutionised protein science. This Artificial Intelligence (AI) tool was trained on protein data collected by life scientists for over 50 years, and is able to predict the 3D shape of proteins with high accuracy. Its success prompted the modelling of an astounding 215 million proteins last year, providing insights into the shapes of almost any protein. This is particularly interesting for proteins that have not been studied experimentally, a complex and time-consuming process. "There are now many sources of protein information, containing valuable insights into how proteins evolve and work" says Joana Pereira, the leader of the study.