Theorem proving is a fundamental task in mathematics. With the advent of large language models (LLMs) and interactive theorem provers (ITPs) like Lean, there has been growing interest in integrating LLMs and ITPs to automate theorem proving. In this approach, the LLM generates proof steps (tactics), and the ITP checks the applicability of the tactics at the current goal. The two systems work together to complete the proof. In this paper, we introduce DS-Prover, a novel dynamic sampling method for theorem proving. This method dynamically determines the number of tactics to apply to expand the current goal, taking into account the remaining time compared to the total allocated time for proving a theorem. This makes the proof search process more efficient by adjusting the balance between exploration and exploitation as time passes. We also augment the training dataset by decomposing simplification and rewrite tactics with multiple premises into tactics with single premises. This gives the model more examples to learn from and helps it to predict the tactics with premises more accurately. We perform our experiments using the Mathlib dataset of the Lean theorem prover and report the performance on two standard datasets, MiniF2F and ProofNet. Our methods achieve significant performance gains on both datasets. We achieved a state-of-the-art performance (Pass@1) of 14.2% on the ProofNet dataset and a performance of 29.8% on MiniF2F, slightly surpassing the best-reported Pass@1 of 29.6% using Lean.

Since their original release, Transformer-based LLMs have been shown to be remarkably proficient on a wide array of important machine learning tasks. Their underlying Transformer architecture has become state-of-the-art for modeling and reasoning about natural language, and has shown promise in domains such as computer vision [Dosovitskiy et al., 2020] and reinforcement learning [Chen et al., 2021] as well. Contemporary instantiations of Transformer architectures are infamously large, typically requiring tremendous compute resources for both training and inference. This is by design, as Transformers trained with more parameters or data are demonstrably more capable than their slimmer predecessors--often by a significant margin [Brown et al., 2020, Touvron et al., 2023]. Still, a growing body of work suggests that Transformerbased models, and neural networks more generally, do not require all fitted parameters to retain their learned hypotheses.

Recommender systems utilizing explicit feedback have witnessed significant advancements and widespread applications over the past years. However, generating recommendations in few-shot scenarios remains a persistent challenge. Recently, large language models (LLMs) have emerged as a promising solution for addressing natural language processing (NLP) tasks, thereby offering novel insights into tackling the few-shot scenarios encountered by explicit feedback-based recommender systems. To bridge recommender systems and LLMs, we devise a prompting template that generates user and item representations based on explicit feedback. Subsequently, we integrate these LLM-processed representations into various recommendation models to evaluate their significance across diverse recommendation tasks. Our ablation experiments and case study analysis collectively demonstrate the effectiveness of LLMs in processing explicit feedback, highlighting that LLMs equipped with generative and logical reasoning capabilities can effectively serve as a component of recommender systems to enhance their performance in few-shot scenarios. Furthermore, the broad adaptability of LLMs augments the generalization potential of recommender models, despite certain inherent constraints. We anticipate that our study can inspire researchers to delve deeper into the multifaceted dimensions of LLMs's involvement in recommender systems and contribute to the advancement of the explicit feedback-based recommender systems field.

Pruning large language models (LLMs) from the BERT family has emerged as a standard compression benchmark, and several pruning methods have been proposed for this task. The recent ``Sparsity May Cry'' (SMC) benchmark put into question the validity of all existing methods, exhibiting a more complex setup where many known pruning methods appear to fail. We revisit the question of accurate BERT-pruning during fine-tuning on downstream datasets, and propose a set of general guidelines for successful pruning, even on the challenging SMC benchmark. First, we perform a cost-vs-benefits analysis of pruning model components, such as the embeddings and the classification head; second, we provide a simple-yet-general way of scaling training, sparsification and learning rate schedules relative to the desired target sparsity; finally, we investigate the importance of proper parametrization for Knowledge Distillation in the context of LLMs. Our simple insights lead to state-of-the-art results, both on classic BERT-pruning benchmarks, as well as on the SMC benchmark, showing that even classic gradual magnitude pruning (GMP) can yield competitive results, with the right approach.

The escalating complexity of modern computing frameworks has resulted in a surge in the cybersecurity vulnerabilities reported to the National Vulnerability Database (NVD) by practitioners. Despite the fact that the stature of NVD is one of the most significant databases for the latest insights into vulnerabilities, extracting meaningful trends from such a large amount of unstructured data is still challenging without the application of suitable technological methodologies. Previous efforts have mostly concentrated on software vulnerabilities; however, a holistic strategy incorporates approaches for mitigating vulnerabilities, score prediction, and a knowledge-generating system that may extract relevant insights from the Common Weakness Enumeration (CWE) and Common Vulnerability Exchange (CVE) databases is notably absent. As the number of hardware attacks on Internet of Things (IoT) devices continues to rapidly increase, we present the Hardware Vulnerability to Weakness Mapping (HW-V2W-Map) Framework, which is a Machine Learning (ML) framework focusing on hardware vulnerabilities and IoT security. The architecture that we have proposed incorporates an Ontology-driven Storytelling framework, which automates the process of updating the ontology in order to recognize patterns and evolution of vulnerabilities over time and provides approaches for mitigating the vulnerabilities. The repercussions of vulnerabilities can be mitigated as a result of this, and conversely, future exposures can be predicted and prevented. Furthermore, our proposed framework utilized Generative Pre-trained Transformer (GPT) Large Language Models (LLMs) to provide mitigation suggestions.

In this paper, we build a visual dialogue dataset, named InfoVisDial, which provides rich informative answers in each round even with external knowledge related to the visual content. Different from existing datasets where the answer is compact and short, InfoVisDial contains long free-form answers with rich information in each round of dialogue. For effective data collection, the key idea is to bridge the large-scale multimodal model (e.g., GIT) and the language models (e.g., GPT-3). GIT can describe the image content even with scene text, while GPT-3 can generate informative dialogue based on the image description and appropriate prompting techniques. With such automatic pipeline, we can readily generate informative visual dialogue data at scale. Then, we ask human annotators to rate the generated dialogues to filter the low-quality conversations.Human analyses show that InfoVisDial covers informative and diverse dialogue topics: $54.4\%$ of the dialogue rounds are related to image scene texts, and $36.7\%$ require external knowledge. Each round's answer is also long and open-ended: $87.3\%$ of answers are unique with an average length of $8.9$, compared with $27.37\%$ and $2.9$ in VisDial. Last, we propose a strong baseline by adapting the GIT model for the visual dialogue task and fine-tune the model on InfoVisDial. Hopefully, our work can motivate more effort on this direction.

With easier access to powerful compute resources, there is a growing trend in AI for software development to develop larger language models (LLMs) to address a variety of programming tasks. Even LLMs applied to tasks from the high-performance computing (HPC) domain are huge in size and demand expensive compute resources for training. This is partly because these LLMs for HPC tasks are obtained by finetuning existing LLMs that support several natural and/or programming languages. We found this design choice confusing - why do we need large LMs trained on natural languages and programming languages unrelated to HPC for HPC-specific tasks? In this line of work, we aim to question choices made by existing LLMs by developing smaller LMs for specific domains - we call them domain-specific LMs. Specifically, we start off with HPC as a domain and build an HPC-specific LM, named MonoCoder, that is orders of magnitude smaller than existing LMs but delivers similar, if not better performance, on non-HPC and HPC tasks. Specifically, we pre-trained MonoCoder on an HPC-specific dataset (named HPCorpus) of C and C++ programs mined from GitHub. We evaluated the performance of MonoCoder against conventional multi-lingual LLMs. Results demonstrate that MonoCoder, although much smaller than existing LMs, achieves similar results on normalized-perplexity tests and much better ones in CodeBLEU competence for high-performance and parallel code generations. Furthermore, fine-tuning the base model for the specific task of parallel code generation (OpenMP parallel for pragmas) demonstrates outstanding results compared to GPT, especially when local misleading semantics are removed by our novel pre-processor Tokompiler, showcasing the ability of domain-specific models to assist in HPC-relevant tasks.

Data is stored in both structured and unstructured form. Querying both, to power natural language conversations, is a challenge. This paper introduces dIR, Discrete Information Retrieval, providing a unified interface to query both free text and structured knowledge. Specifically, a Large Language Model (LLM) transforms text into expressive representation. After the text is extracted into columnar form, it can then be queried via a text-to-SQL Semantic Parser, with an LLM converting natural language into SQL. Where desired, such conversation may be effected by a multi-step reasoning conversational agent. We validate our approach via a proprietary question/answer data set, concluding that dIR makes a whole new class of queries on free text possible when compared to traditionally fine-tuned dense-embedding-model-based Information Retrieval (IR) and SQL-based Knowledge Bases (KB). For sufficiently complex queries, dIR can succeed where no other method stands a chance.

We present a framework for robots to learn novel visual concepts and tasks via in-situ linguistic interactions with human users. Previous approaches have either used large pre-trained visual models to infer novel objects zero-shot, or added novel concepts along with their attributes and representations to a concept hierarchy. We extend the approaches that focus on learning visual concept hierarchies by enabling them to learn novel concepts and solve unseen robotics tasks with them. To enable a visual concept learner to solve robotics tasks one-shot, we developed two distinct techniques. Firstly, we propose a novel approach, Hi-Viscont(HIerarchical VISual CONcept learner for Task), which augments information of a novel concept to its parent nodes within a concept hierarchy. This information propagation allows all concepts in a hierarchy to update as novel concepts are taught in a continual learning setting. Secondly, we represent a visual task as a scene graph with language annotations, allowing us to create novel permutations of a demonstrated task zero-shot in-situ. We present two sets of results. Firstly, we compare Hi-Viscont with the baseline model (FALCON) on visual question answering(VQA) in three domains. While being comparable to the baseline model on leaf level concepts, Hi-Viscont achieves an improvement of over 9% on non-leaf concepts on average. We compare our model's performance against the baseline FALCON model. Our framework achieves 33% improvements in success rate metric, and 19% improvements in the object level accuracy compared to the baseline model. With both of these results we demonstrate the ability of our model to learn tasks and concepts in a continual learning setting on the robot.

Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces. To combine the controllability of VAE latent spaces with the state-of-the-art performance of recent large language models (LLMs), we present in this work LlaMaVAE, which combines expressive encoder and decoder models (sentenceT5 and LlaMA) with a VAE architecture, aiming to provide better text generation control to LLMs. In addition, to conditionally guide the VAE generation, we investigate a new approach based on flow-based invertible neural networks (INNs) named Invertible CVAE. Experimental results reveal that LlaMaVAE can outperform the previous state-of-the-art VAE language model, Optimus, across various tasks, including language modelling, semantic textual similarity and definition modelling. Qualitative analysis on interpolation and traversal experiments also indicates an increased degree of semantic clustering and geometric consistency, which enables better generation control.