OpenRaiser/Intern-Atlas huggingface dataset:
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning
Venue: arXiv cs.CL 2024
Year: 2024

Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance. However, the impact of each component of the pretraining corpus remains opaque. As a result, the organization of the pretraining corpus is still empirical and may deviate from the optimal. To address this issue, we systematically analyze the impact of 48 datasets from 5 major categories of pretraining data of LLMs and measure their impacts on LLMs using benchmarks about nine major categories of model capabilities. Our analyses provide empirical results about the contribution of multiple corpora on the performances of LLMs, along with their joint impact patterns, including complementary, orthogonal, and correlational relationships. We also identify a set of ``high-impact data'' such as Books that is significantly related to a set of model capabilities. These findings provide insights into the organization of data to support more efficient pretraining of LLMs.
On efficient normal bases over binary fields
Venue: arXiv cs.IT 2024
Year: 2024

Binary field extensions are fundamental to many applications, such as multivariate public key cryptography, code-based cryptography, and error-correcting codes. Their implementation requires a foundation in number theory and algebraic geometry and necessitates the utilization of efficient bases. The continuous increase in the power of computation, and the design of new (quantum) computers increase the threat to the security of systems and impose increasingly demanding encryption standards with huge polynomial or extension degrees. For cryptographic purposes or other common implementations of finite fields arithmetic, it is essential to explore a wide range of implementations with diverse bases. Unlike some bases, polynomial and Gaussian normal bases are well-documented and widely employed. In this paper, we explore other forms of bases of $\mathbb{F}_{2^n}$ over $\mathbb{F}_2$ to demonstrate efficient implementation of operations within different ranges. To achieve this, we leverage results on fast computations and elliptic periods introduced by Couveignes and Lercier, and subsequently expanded upon by Ezome and Sall. This leads to the establishment of new tables for efficient computation over binary fields.
Designing interactive data visualizations representing recovery progress for patients after stroke
Venue: arXiv cs.HC 2024
Year: 2024

Stroke is one of the leading causes of disability worldwide. The efficacy of recovery is determined by a variety of factors, including patient adherence to rehabilitation programs. One way to increase patient adherence to their rehabilitation program is to show patients their progress that is visualized in a simple and intuitive way. We begin to gather preliminary information on Functional Capacity, Motor Function, and Mood/cognition from occupational Therapists at the Bruyere Hospital to gain a better understanding of how stroke recovery data is collected within in-patient stroke rehabilitation centers. The future aim is to design, develop, and evaluate a data visualization tool representing progress made by patients recovering from stroke.
Faster algorithms on linear delta-matroids
Venue: arXiv cs.DS 2024
Year: 2024

We show new algorithms and constructions over linear delta-matroids. We observe an alternative representation for linear delta-matroids, as a contraction representation over a skew-symmetric matrix. This is equivalent to the more standard "twist representation" up to $O(n^ω)$-time transformations, but is much more convenient for algorithmic tasks. For instance, the problem of finding a max-weight feasible set now reduces directly to the problem of finding a max-weight basis in a linear matroid. Supported by this representation, we provide new algorithms and constructions over linear delta-matroids. We show that the union and delta-sum of linear delta-matroids define linear delta-matroids, and a representation for the resulting delta-matroid can be constructed in randomized time $O(n^ω)$. Previously, it was only known that these operations define delta-matroids. We also note that every projected linear delta-matroid can be represented as an elementary projection. This implies that several optimization problems over (projected) linear delta-matroids, including the coverage, delta-coverage, and parity problems, reduce (in their decision versions) to a single $O(n^ω)$-time matrix rank computation. Using the methods of Harvey, previously used by Cheung, Lao and Leung for linear matroid parity, we furthermore show how to solve the search versions in the same time. This improves on the $O(n^4)$-time augmenting path algorithm of Geelen, Iwata and Murota. Finally, we consider the maximum-cardinality delta-matroid intersection problem. Using Storjohann's algorithms for symbolic determinants, we show that such a solution can be found in $O(n^{ω+1})$ time. This is the first polynomial-time algorithm for the problem, solving an open question of Kakimura and Takamatsu.
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
Venue: arXiv cs.CV 2024
Year: 2024

Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that the LVLMs claim non-existent objects in the image. To mitigate the object hallucinations, instruction tuning and external model-based detection methods have been proposed, which either require large-scare computational resources or depend on the detection result of external models. However, there remains an under-explored field to utilize the LVLM itself to alleviate object hallucinations. In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects. Therefore, we propose a Logical Closed Loop-based framework for Object Hallucination Detection and Mitigation, namely LogicCheckGPT. In specific, we devise logical consistency probing to raise questions with logical correlations, inquiring about attributes from objects and vice versa. Whether their responses can form a logical closed loop serves as an indicator of object hallucination. As a plug-and-play method, it can be seamlessly applied to all existing LVLMs. Comprehensive experiments conducted on three benchmarks across four LVLMs have demonstrated significant improvements brought by our method, indicating its effectiveness and generality.
Interactive Garment Recommendation with User in the Loop
Venue: arXiv cs.CV 2024
Year: 2024

Recommending fashion items often leverages rich user profiles and makes targeted suggestions based on past history and previous purchases. In this paper, we work under the assumption that no prior knowledge is given about a user. We propose to build a user profile on the fly by integrating user reactions as we recommend complementary items to compose an outfit. We present a reinforcement learning agent capable of suggesting appropriate garments and ingesting user feedback so to improve its recommendations and maximize user satisfaction. To train such a model, we resort to a proxy model to be able to simulate having user feedback in the training loop. We experiment on the IQON3000 fashion dataset and we find that a reinforcement learning-based agent becomes capable of improving its recommendations by taking into account personal preferences. Furthermore, such task demonstrated to be hard for non-reinforcement models, that cannot exploit exploration during training.
Tool-Augmented LLMs as a Universal Interface for IDEs
Venue: arXiv cs.SE 2024
Year: 2024

Modern-day Integrated Development Environments (IDEs) have come a long way from the early text editing utilities to the complex programs encompassing thousands of functions to help developers. However, with the increasing number of efficiency-enhancing tools incorporated, IDEs gradually became sophisticated software with a steep learning curve. The rise of the Large Language Models (LLMs) capable of both natural language dialogue and code generation leads to a discourse on the obsolescence of the concept of IDE. In this work, we offer a view on the place of the LLMs in the IDEs as the universal interface wrapping the IDE facilities. We envision a model that is able to perform complex actions involving multiple IDE features upon user command, stripping the user experience of the tedious work involved in searching through options and actions. For the practical part of the work, we engage with the works exploring the ability of LLMs to call for external tools to expedite a given task execution. We showcase a proof-of-concept of such a tool.
Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition
Venue: arXiv cs.LG 2024
Year: 2024

Rapid progress in machine learning and deep learning has enabled a wide range of applications in the electricity load forecasting of power systems, for instance, univariate and multivariate short-term load forecasting. Though the strong capabilities of learning the non-linearity of the load patterns and the high prediction accuracy have been achieved, the interpretability of typical deep learning models for electricity load forecasting is less studied. This paper proposes an interpretable deep learning method, which learns a linear combination of neural networks that each attends to an input time feature. We also proposed a multi-scale time series decomposition method to deal with the complex time patterns. Case studies have been carried out on the Belgium central grid load dataset and the proposed model demonstrated better accuracy compared to the frequently applied baseline model. Specifically, the proposed multi-scale temporal decomposition achieves the best MSE, MAE and RMSE of 0.52, 0.57 and 0.72 respectively. As for interpretability, on one hand, the proposed method displays generalization capability. On the other hand, it can demonstrate not only the feature but also the temporal interpretability compared to other baseline methods. Besides, the global time feature interpretabilities are also obtained. Obtaining global feature interpretabilities allows us to catch the overall patterns, trends, and cyclicality in load data while also revealing the significance of various time-related features in forming the final outputs.
Stochastic Nonlinear Dynamical Modelling of SRAM Bitcells in Retention Mode
Venue: arXiv cs.AR 2024
Year: 2024

SRAM bitcells in retention mode behave as autonomous stochastic nonlinear dynamical systems. From observation of variability-aware transient noise simulations, we provide an unidimensional model, fully characterizable by conventional deterministic SPICE simulations, insightfully explaining the mechanism of intrinsic noise-induced bit flips. The proposed model is exploited to, first, explain the reported inaccuracy of existing closed-form near-equilibrium formulas aimed at predicting the mean time to failure and, secondly, to propose a closer estimate attractive in terms of CPU time.
Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis
Venue: arXiv cs.CL 2024
Year: 2024

In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various language models on this dataset and propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function, outperforming existing approaches. We also demonstrate the practical utility of our proposed model by constructing a novel measure of optimism. Here, we observe the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code are publicly (under CC BY 4.0 license) available on GitHub.
To Store or Not to Store: a graph theoretical approach for Dataset Versioning
Venue: arXiv cs.DS 2024
Year: 2024

In this work, we study the cost efficient data versioning problem, where the goal is to optimize the storage and reconstruction (retrieval) costs of data versions, given a graph of datasets as nodes and edges capturing edit/delta information. One central variant we study is MinSum Retrieval (MSR) where the goal is to minimize the total retrieval costs, while keeping the storage costs bounded. This problem (along with its variants) was introduced by Bhattacherjee et al. [VLDB'15]. While such problems are frequently encountered in collaborative tools (e.g., version control systems and data analysis pipelines), to the best of our knowledge, no existing research studies the theoretical aspects of these problems. We establish that the currently best-known heuristic, LMG, can perform arbitrarily badly in a simple worst case. Moreover, we show that it is hard to get $o(n)$-approximation for MSR on general graphs even if we relax the storage constraints by an $O(\log n)$ factor. Similar hardness results are shown for other variants. Meanwhile, we propose poly-time approximation schemes for tree-like graphs, motivated by the fact that the graphs arising in practice from typical edit operations are often not arbitrary. As version graphs typically have low treewidth, we further develop new algorithms for bounded treewidth graphs. Furthermore, we propose two new heuristics and evaluate them empirically. First, we extend LMG by considering more potential ``moves'', to propose a new heuristic LMG-All. LMG-All consistently outperforms LMG while having comparable run time on a wide variety of datasets, i.e., version graphs. Secondly, we apply our tree algorithms on the minimum-storage arborescence of an instance, yielding algorithms that are qualitatively better than all previous heuristics for MSR, as well as for another variant BoundedMin Retrieval (BMR).
Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic
Venue: arXiv cs.CL 2024
Year: 2024

Aligned language models face a significant limitation as their fine-tuning often results in compromised safety. To tackle this, we propose a simple method RESTA that performs LLM safety realignment. RESTA stands for REstoring Safety through Task Arithmetic. At its core, it involves a simple arithmetic addition of a safety vector to the weights of the compromised model. We demonstrate the effectiveness of RESTA in both parameter-efficient and full fine-tuning, covering a wide range of downstream tasks, including instruction following in Chinese, English, and Hindi, as well as problem-solving capabilities in Code and Math. We also showcase the generalizability of RESTA on three existing safety evaluation benchmarks and a multilingual benchmark dataset proposed as a part of this work, consisting of 550 harmful questions covering 11 categories, each with 5 sub-categories of harm. Overall, RESTA decreases the harmfulness of the compromised model from 18.6% to 5.1% and from 9.2% to 1.5% in parameter-efficient and full fine-tuning, respectively, while maintaining most of the model's performance on the task. We release the source codes at: https://github.com/declare-lab/resta.
SInViG: A Self-Evolving Interactive Visual Agent for Human-Robot Interaction
Venue: arXiv cs.RO 2024
Year: 2024

Linguistic ambiguity is ubiquitous in our daily lives. Previous works adopted interaction between robots and humans for language disambiguation. Nevertheless, when interactive robots are deployed in daily environments, there are significant challenges for natural human-robot interaction, stemming from complex and unpredictable visual inputs, open-ended interaction, and diverse user demands. In this paper, we present SInViG, which is a self-evolving interactive visual agent for human-robot interaction based on natural languages, aiming to resolve language ambiguity, if any, through multi-turn visual-language dialogues. It continuously and automatically learns from unlabeled images and large language models, without human intervention, to be more robust against visual and linguistic complexity. Benefiting from self-evolving, it sets new state-of-the-art on several interactive visual grounding benchmarks. Moreover, our human-robot interaction experiments show that the evolved models consistently acquire more and more preferences from human users. Besides, we also deployed our model on a Franka robot for interactive manipulation tasks. Results demonstrate that our model can follow diverse user instructions and interact naturally with humans in natural language, despite the complexity and disturbance of the environment.
Generative Kaleidoscopic Networks
Venue: arXiv cs.LG 2024
Year: 2024

We discovered that the neural networks, especially the deep ReLU networks, demonstrate an `over-generalization' phenomenon. That is, the output values for the inputs that were not seen during training are mapped close to the output range that were observed during the learning process. In other words, the neural networks learn a many-to-one mapping and this effect is more prominent as we increase the number of layers or the depth of the neural network. We utilize this property of neural networks to design a dataset kaleidoscope, termed as `Generative Kaleidoscopic Networks'. Succinctly, if we learn a model to map from input $x\in\mathbb{R}^D$ to itself $f_\mathcal{N}(x)\rightarrow x$, the proposed `Kaleidoscopic sampling' procedure starts with a random input noise $z\in\mathbb{R}^D$ and recursively applies $f_\mathcal{N}(\cdots f_\mathcal{N}(z)\cdots )$. After a burn-in period duration, we start observing samples from the input distribution and the quality of samples recovered improves as we increase the depth of the model. Scope: We observed this phenomenon to various degrees for the other deep learning architectures like CNNs, Transformers & U-Nets and we are currently investigating them further.
Interpretable Embedding for Ad-hoc Video Search
Venue: arXiv cs.CV 2024
Year: 2024

Answering query with semantic concepts has long been the mainstream approach for video search. Until recently, its performance is surpassed by concept-free approach, which embeds queries in a joint space as videos. Nevertheless, the embedded features as well as search results are not interpretable, hindering subsequent steps in video browsing and query reformulation. This paper integrates feature embedding and concept interpretation into a neural network for unified dual-task learning. In this way, an embedding is associated with a list of semantic concepts as an interpretation of video content. This paper empirically demonstrates that, by using either the embedding features or concepts, considerable search improvement is attainable on TRECVid benchmarked datasets. Concepts are not only effective in pruning false positive videos, but also highly complementary to concept-free search, leading to large margin of improvement compared to state-of-the-art approaches.
A novel framework for adaptive stress testing of autonomous vehicles in multi-lane roads
Venue: arXiv cs.RO 2024
Year: 2024

Stress testing is an approach for evaluating the reliability of systems under extreme conditions which help reveal vulnerable scenarios that standard testing may overlook. Identifying such scenarios is of great importance in autonomous vehicles (AV) and other safety-critical systems. Since failure events are rare, naive random search approaches require a large number of vehicle operation hours to identify potential system failures. Adaptive Stress Testing (AST) is a method addressing this constraint by effectively exploring the failure trajectories of AV using a Markov decision process and employs reinforcement learning techniques to identify driving scenarios with high probability of failures. However, existing AST frameworks are able to handle only simple scenarios, such as one vehicle moving longitudinally on a single lane road which is not realistic and has a limited applicability. In this paper, we propose a novel AST framework to systematically explore corner cases of intelligent driving models that can result in safety concerns involving both longitudinal and lateral vehicle's movements. Specially, we develop a new reward function for Deep Reinforcement Learning to guide the AST in identifying crash scenarios based on the collision probability estimate between the AV under test (i.e., the ego vehicle) and the trajectory of other vehicles on the multi-lane roads. To demonstrate the effectiveness of our framework, we tested it with a complex driving model vehicle that can be controlled in both longitudinal and lateral directions. Quantitative and qualitative analyses of our experimental results demonstrate that our framework outperforms the state-of-the-art AST scheme in identifying corner cases with complex driving maneuvers.
HU at SemEval-2024 Task 8A: Can Contrastive Learning Learn Embeddings to Detect Machine-Generated Text?
Venue: arXiv cs.CL 2024
Year: 2024

This paper describes our system developed for SemEval-2024 Task 8, ``Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection'' Machine-generated texts have been one of the main concerns due to the use of large language models (LLM) in fake text generation, phishing, cheating in exams, or even plagiarizing copyright materials. A lot of systems have been developed to detect machine-generated text. Nonetheless, the majority of these systems rely on the text-generating model. This limitation is impractical in real-world scenarios, as it's often impossible to know which specific model the user has used for text generation. In this work, we propose a $\textbf{single}$ model based on contrastive learning, which uses $\textbf{$\approx$40% of the baseline's parameters}$ (149M vs. 355M) but shows a comparable performance on the test dataset $(\textbf{21st out of 137 participants})$. Our key finding is that even without an ensemble of multiple models, a single base model can have comparable performance with the help of data augmentation and contrastive learning. Our code is publicly available at https://github.com/dipta007/SemEval24-Task8.
Head-wise Shareable Attention for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) suffer from huge number of parameters, which restricts their deployment on edge devices. Weight sharing is one promising solution that encourages weight reuse, effectively reducing memory usage with less performance drop. However, current weight sharing techniques primarily focus on small-scale models like BERT and employ coarse-grained sharing rules, e.g., layer-wise. This becomes limiting given the prevalence of LLMs and sharing an entire layer or block obviously diminishes the flexibility of weight sharing. In this paper, we present a perspective on head-wise shareable attention for large language models. We further propose two memory-efficient methods that share parameters across attention heads, with a specific focus on LLMs. Both of them use the same dynamic strategy to select the shared weight matrices. The first method directly reuses the pre-trained weights without retraining, denoted as $\textbf{DirectShare}$. The second method first post-trains with constraint on weight matrix similarity and then shares, denoted as $\textbf{PostShare}$. Experimental results reveal our head-wise shared models still maintain satisfactory capabilities, demonstrating the feasibility of fine-grained weight sharing applied to LLMs.