OpenRaiser/Intern-Atlas huggingface dataset:
From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
Venue: arXiv cs.AI 2023
Year: 2023

Big models, exemplified by Large Language Models (LLMs), are models typically pre-trained on massive data and comprised of enormous parameters, which not only obtain significantly improved performance across diverse tasks but also present emergent capabilities absent in smaller models. However, the growing intertwining of big models with everyday human lives poses potential risks and might cause serious social harm. Therefore, many efforts have been made to align LLMs with humans to make them better follow user instructions and satisfy human preferences. Nevertheless, `what to align with' has not been fully discussed, and inappropriate alignment goals might even backfire. In this paper, we conduct a comprehensive survey of different alignment goals in existing work and trace their evolution paths to help identify the most essential goal. Particularly, we investigate related works from two perspectives: the definition of alignment goals and alignment evaluation. Our analysis encompasses three distinct levels of alignment goals and reveals a goal transformation from fundamental abilities to value orientation, indicating the potential of intrinsic human values as the alignment goal for enhanced LLMs. Based on such results, we further discuss the challenges of achieving such intrinsic value alignment and provide a collection of available resources for future research on the alignment of big models.
Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization
Venue: arXiv cs.CL 2023
Year: 2023

The Biomedical Entity Normalization (BEN) task aims to align raw, unstructured medical entities to standard entities, thus promoting data coherence and facilitating better downstream medical applications. Recently, prompt learning methods have shown promising results in this task. However, existing research falls short in tackling the more complex Chinese BEN task, especially in the few-shot scenario with limited medical data, and the vast potential of the external medical knowledge base has yet to be fully harnessed. To address these challenges, we propose a novel Knowledge-injected Prompt Learning (PL-Knowledge) method. Specifically, our approach consists of five stages: candidate entity matching, knowledge extraction, knowledge encoding, knowledge injection, and prediction output. By effectively encoding the knowledge items contained in medical entities and incorporating them into our tailor-made knowledge-injected templates, the additional knowledge enhances the model's ability to capture latent relationships between medical entities, thus achieving a better match with the standard entities. We extensively evaluate our model on a benchmark dataset in both few-shot and full-scale scenarios. Our method outperforms existing baselines, with an average accuracy boost of 12.96\% in few-shot and 0.94\% in full-data cases, showcasing its excellence in the BEN task.
Prompt-Based Length Controlled Generation with Reinforcement Learning
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) like ChatGPT and GPT-4 have attracted great attention given their surprising performance on a wide range of NLP tasks. Length controlled generation of LLMs emerges as an important topic, which enables users to fully leverage the capability of LLMs in more real-world scenarios like generating a proper answer or essay of a desired length. In addition, the autoregressive generation in LLMs is extremely time-consuming, while the ability of controlling this generated length can reduce the inference cost by limiting the length. Therefore, we propose a prompt-based length control method to achieve high-accuracy length controlled generation. In particular, we adopt reinforcement learning with the reward signal given by either trainable or rule-based reward models, which further enhances the length-control ability of LLMs by rewarding outputs that follows pre-defined control instruction. To enable rule-based inference, we also introduce standard prompt extractor to collect the standard control information from users' input. Experiments show that our method significantly improves the accuracy of prompt-based length control for summarization task on popular datasets like CNNDM and NYT. Both the standard prompt extractor and the RL-tuned model have show strong generalization ability to unseen control prompt templates.
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning
Venue: arXiv cs.CL 2023
Year: 2023

With the increasing size of pre-trained language models (PLMs), fine-tuning all the parameters in the model is not efficient, especially when there are a large number of downstream tasks, which incur significant training and storage costs. Many parameter-efficient fine-tuning (PEFT) approaches have been proposed, among which, Low-Rank Adaptation (LoRA) is a representative approach that injects trainable rank decomposition matrices into every target module. Yet LoRA ignores the importance of parameters in different modules. To address this problem, many works have been proposed to prune the parameters of LoRA. However, under limited training conditions, the upper bound of the rank of the pruned parameter matrix is still affected by the preset values. We, therefore, propose IncreLoRA, an incremental parameter allocation method that adaptively adds trainable parameters during training based on the importance scores of each module. This approach is different from the pruning method as it is not limited by the initial number of training parameters, and each parameter matrix has a higher rank upper bound for the same training overhead. We conduct extensive experiments on GLUE to demonstrate the effectiveness of IncreLoRA. The results show that our method owns higher parameter efficiency, especially when under the low-resource settings where our method significantly outperforms the baselines. Our code is publicly available.
Head-Tail Cooperative Learning Network for Unbiased Scene Graph Generation
Venue: arXiv cs.CV 2023
Year: 2023

Scene Graph Generation (SGG) as a critical task in image understanding, facing the challenge of head-biased prediction caused by the long-tail distribution of predicates. However, current unbiased SGG methods can easily prioritize improving the prediction of tail predicates while ignoring the substantial sacrifice in the prediction of head predicates, leading to a shift from head bias to tail bias. To address this issue, we propose a model-agnostic Head-Tail Collaborative Learning (HTCL) network that includes head-prefer and tail-prefer feature representation branches that collaborate to achieve accurate recognition of both head and tail predicates. We also propose a self-supervised learning approach to enhance the prediction ability of the tail-prefer feature representation branch by constraining tail-prefer predicate features. Specifically, self-supervised learning converges head predicate features to their class centers while dispersing tail predicate features as much as possible through contrast learning and head center loss. We demonstrate the effectiveness of our HTCL by applying it to various SGG models on VG150, Open Images V6 and GQA200 datasets. The results show that our method achieves higher mean Recall with a minimal sacrifice in Recall and achieves a new state-of-the-art overall performance. Our code is available at https://github.com/wanglei0618/HTCL.
Ensembling Uncertainty Measures to Improve Safety of Black-Box Classifiers
Venue: arXiv cs.LG 2023
Year: 2023

Machine Learning (ML) algorithms that perform classification may predict the wrong class, experiencing misclassifications. It is well-known that misclassifications may have cascading effects on the encompassing system, possibly resulting in critical failures. This paper proposes SPROUT, a Safety wraPper thROugh ensembles of UncertainTy measures, which suspects misclassifications by computing uncertainty measures on the inputs and outputs of a black-box classifier. If a misclassification is detected, SPROUT blocks the propagation of the output of the classifier to the encompassing system. The resulting impact on safety is that SPROUT transforms erratic outputs (misclassifications) into data omission failures, which can be easily managed at the system level. SPROUT has a broad range of applications as it fits binary and multi-class classification, comprising image and tabular datasets. We experimentally show that SPROUT always identifies a huge fraction of the misclassifications of supervised classifiers, and it is able to detect all misclassifications in specific cases. SPROUT implementation contains pre-trained wrappers, it is publicly available and ready to be deployed with minimal effort.
On Using Information Retrieval to Recommend Machine Learning Good Practices for Software Engineers
Venue: arXiv cs.SE 2023
Year: 2023

Machine learning (ML) is nowadays widely used for different purposes and in several disciplines. From self-driving cars to automated medical diagnosis, machine learning models extensively support users' daily activities, and software engineering tasks are no exception. Not embracing good ML practices may lead to pitfalls that hinder the performance of an ML system and potentially lead to unexpected results. Despite the existence of documentation and literature about ML best practices, many non-ML experts turn towards gray literature like blogs and Q&A systems when looking for help and guidance when implementing ML systems. To better aid users in distilling relevant knowledge from such sources, we propose a recommender system that recommends ML practices based on the user's context. As a first step in creating a recommender system for machine learning practices, we implemented Idaka. A tool that provides two different approaches for retrieving/generating ML best practices: i) an information retrieval (IR) engine and ii) a large language model. The IR-engine uses BM25 as the algorithm for retrieving the practices, and a large language model, in our case Alpaca. The platform has been designed to allow comparative studies of best practices retrieval tools. Idaka is publicly available at GitHub: https://bit.ly/idaka. Video: https://youtu.be/cEb-AhIPxnM.
Instruction Position Matters in Sequence Generation with Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning. The fine-tuning data is generally sequentially concatenated from a specific task instruction, an input sentence, and the corresponding response. Considering the locality modeled by the self-attention mechanism of LLMs, these models face the risk of instruction forgetting when generating responses for long input sentences. To mitigate this issue, we propose enhancing the instruction-following capability of LLMs by shifting the position of task instructions after the input sentences. Theoretical analysis suggests that our straightforward method can alter the model's learning focus, thereby emphasizing the training of instruction-following capabilities. Concurrently, experimental results demonstrate that our approach consistently outperforms traditional settings across various model scales (1B / 7B / 13B) and different sequence generation tasks (translation and summarization), without any additional data or annotation costs. Notably, our method significantly improves the zero-shot performance on conditional sequence generation, e.g., up to 9.7 BLEU points on WMT zero-shot translation tasks.
DarkDiff: Explainable web page similarity of TOR onion sites
Venue: arXiv cs.CR 2023
Year: 2023

In large-scale data analysis, near-duplicates are often a problem. For example, with two near-duplicate phishing emails, a difference in the salutation (Mr versus Ms) is not essential, but whether it is bank A or B is important. The state-of-the-art in near-duplicate detection is a black box approach (MinHash), so one only knows that emails are near-duplicates, but not why. We present DarkDiff, which can efficiently detect near-duplicates while providing the reason why there is a near-duplicate. We have developed DarkDiff to detect near-duplicates of homepages on the Darkweb. DarkDiff works well on those pages because they resemble the clear web of the past.
Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets
Venue: arXiv cs.CV 2023
Year: 2023

Many practical systems for image-based surface reconstruction employ a stereo/multi-stereo paradigm, due to its ability to scale for large scenes and its ease of implementation for out-of-core operations. In this process, multiple and abundant depth maps from stereo matching must be combined and fused into a single, consistent, and clean point cloud. However, the noises and outliers caused by stereo matching and the heterogenous geometric errors of the poses present a challenge for existing fusion algorithms, since they mostly assume Gaussian errors and predict fused results based on data from local spatial neighborhoods, which may inherit uncertainties from multiple depths resulting in lowered accuracy. In this paper, we propose a novel depth fusion paradigm, that instead of numerically fusing points from multiple depth maps, selects the best depth map per point, and combines them into a single and clean point cloud. This paradigm, called select-and-combine (SAC), is achieved through modeling the point level fusion using local Markov Netlets, a micro-network over point across neighboring views for depth/view selection, followed by a Netlets collapse process for point combination. The Markov Netlets are optimized such that they can inherently leverage spatial consistencies among depth maps of neighboring views, thus they can address errors beyond Gaussian ones. Our experiment results show that our approach outperforms existing depth fusion approaches by increasing the F1 score that considers both accuracy and completeness by 2.07% compared to the best existing method. Finally, our approach generates clearer point clouds that are 18% less redundant while with a higher accuracy before fusion
On the Existence of EFX (and Pareto-Optimal) Allocations for Binary Chores
Venue: arXiv cs.GT 2023
Year: 2023

We study the problem of allocating a group of indivisible chores among agents while each chore has a binary marginal. We focus on the fairness criteria of envy-freeness up to any item (EFX) and investigate the existence of EFX allocations. We show that when agents have additive binary cost functions, there exist EFX and Pareto-optimal (PO) allocations that can be computed in polynomial time. To the best of our knowledge, this is the first setting of a general number of agents that admits EFX and PO allocations, before which EFX and PO allocations have only been shown to exist for three bivalued agents. We further consider more general cost functions: cancelable and general monotone (both with binary marginal). We show that EFX allocations exist and can be computed for binary cancelable chores, but EFX is incompatible with PO. For general binary marginal functions, we propose an algorithm that computes (partial) envy-free (EF) allocations with at most $n-1$ unallocated items.
Development and external validation of a lung cancer risk estimation tool using gradient-boosting
Venue: arXiv cs.LG 2023
Year: 2023

Lung cancer is a significant cause of mortality worldwide, emphasizing the importance of early detection for improved survival rates. In this study, we propose a machine learning (ML) tool trained on data from the PLCO Cancer Screening Trial and validated on the NLST to estimate the likelihood of lung cancer occurrence within five years. The study utilized two datasets, the PLCO (n=55,161) and NLST (n=48,595), consisting of comprehensive information on risk factors, clinical measurements, and outcomes related to lung cancer. Data preprocessing involved removing patients who were not current or former smokers and those who had died of causes unrelated to lung cancer. Additionally, a focus was placed on mitigating bias caused by censored data. Feature selection, hyper-parameter optimization, and model calibration were performed using XGBoost, an ensemble learning algorithm that combines gradient boosting and decision trees. The ML model was trained on the pre-processed PLCO dataset and tested on the NLST dataset. The model incorporated features such as age, gender, smoking history, medical diagnoses, and family history of lung cancer. The model was well-calibrated (Brier score=0.044). ROC-AUC was 82% on the PLCO dataset and 70% on the NLST dataset. PR-AUC was 29% and 11% respectively. When compared to the USPSTF guidelines for lung cancer screening, our model provided the same recall with a precision of 13.1% vs. 9.3% on the PLCO dataset and 3.2% vs. 3.1% on the NLST dataset. The developed ML tool provides a freely available web application for estimating the likelihood of developing lung cancer within five years. By utilizing risk factors and clinical data, individuals can assess their risk and make informed decisions regarding lung cancer screening. This research contributes to the efforts in early detection and prevention strategies, aiming to reduce lung cancer-related mortality rates.
Curriculum Learning with Adam: The Devil Is in the Wrong Details
Venue: arXiv cs.LG 2023
Year: 2023

Curriculum learning (CL) posits that machine learning models -- similar to humans -- may learn more efficiently from data that match their current learning progress. However, CL methods are still poorly understood and, in particular for natural language processing (NLP), have achieved only limited success. In this paper, we explore why. Starting from an attempt to replicate and extend a number of recent curriculum methods, we find that their results are surprisingly brittle when applied to NLP. A deep dive into the (in)effectiveness of the curricula in some scenarios shows us why: when curricula are employed in combination with the popular Adam optimisation algorithm, they oftentimes learn to adapt to suboptimally chosen optimisation parameters for this algorithm. We present a number of different case studies with different common hand-crafted and automated CL approaches to illustrate this phenomenon, and we find that none of them outperforms optimisation with only Adam with well-chosen hyperparameters. As such, our results contribute to understanding why CL methods work, but at the same time urge caution when claiming positive results.
Electromagnets Under the Table: an Unobtrusive Magnetic Navigation System for Microsurgery
Venue: arXiv cs.RO 2023
Year: 2023

Miniature magnetic tools have the potential to enable minimally invasive surgical techniques to be applied to space-restricted surgical procedures in areas such as neurosurgery. However, typical magnetic navigation systems, which create the magnetic fields to drive such tools, either cannot generate large enough fields, or surround the patient in a way that obstructs surgeon access to the patient. This paper introduces the design of a magnetic navigation system with eight electromagnets arranged completely under the operating table, to endow the system with maximal workspace accessibility, which allows the patient to lie down on the top surface of the system without any constraints. The found optimal geometric layout of the electromagnets maximizes the field strength and uniformity over a reasonable neurosurgical operating volume. The system can generate non-uniform magnetic fields up to 38 mT along the x and y axes and 47 mT along the z axis at a working distance of 120 mm away from the actuation system workbench, deep enough to deploy magnetic microsurgical tools in the brain. The forces which can be exerted on millimeter-scale magnets used in prototype neurosurgical tools are validated experimentally. Due to its large workspace, this system could be used to control milli-robots in a variety of surgical applications.
Recent Developments in Pandora's Box Problem: Variants and Applications
Venue: arXiv cs.GT 2023
Year: 2023

In 1979, Weitzman introduced Pandora's box problem as a framework for sequential search with costly inspections. Recently, there has been a surge of interest in Pandora's box problem, particularly among researchers working at the intersection of economics and computation. This survey provides an overview of the recent literature on Pandora's box problem, including its latest extensions and applications in areas such as market design, decision theory, and machine learning.
Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption
Venue: arXiv cs.CY 2023
Year: 2023

Over the last year, the ascent of Generative AI (GenAI) has raised concerns about its impact on core skill development, such as problem-solving and algorithmic thinking, in Computer Science students. Preliminary anonymous surveys show that at least 48.5% of our students use GenAI for homework. With the proliferation of these tools, the academic community must contemplate the appropriate role of these tools in education. Neglecting this might culminate in a phenomenon we term the "Junior-Year Wall," where students struggle in advanced courses due to prior over-dependence on GenAI. Instead of discouraging GenAI use, which may unintentionally foster covert usage, our research seeks to answer: "How can educators guide students' interactions with GenAI to preserve core skill development during their foundational academic years?" We introduce "AI-Lab," a pedagogical framework for guiding students in effectively leveraging GenAI within core collegiate programming courses. This framework accentuates GenAI's benefits and potential as a pedagogical instrument. By identifying and rectifying GenAI's errors, students enrich their learning process. Moreover, AI-Lab presents opportunities to use GenAI for tailored support such as topic introductions, detailed examples, corner case identification, rephrased explanations, and debugging assistance. Importantly, the framework highlights the risks of GenAI over-dependence, aiming to intrinsically motivate students towards balanced usage. This approach is premised on the idea that mere warnings of GenAI's potential failures may be misconstrued as instructional shortcomings rather than genuine tool limitations. Additionally, AI-Lab offers strategies for formulating prompts to elicit high-quality GenAI responses. For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
Operational requirements for localization in autonomous vehicles
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous vehicles (AVs) need to determine their position and orientation accurately with respect to global coordinate system or local features under different scene geometries, traffic conditions and environmental conditions. \cite{reid2019localization} provides a comprehensive framework for the localization requirements for AVs. However, the framework is too restrictive whereby - (a) only a very small deviation from the lane is tolerated (one every $10^{8}$ hours), (b) all roadway types are considered same without any attention to restriction provided by the environment onto the localization and (c) the temporal nature of the location and orientation is not considered in the requirements. In this research, we present a more practical view of the localization requirement aimed at keeping the AV safe during an operation. We present the following novel contributions - (a) we propose a deviation penalty as a cumulative distribution function of the Weibull distribution which starts from the adjacent lane boundary, (b) we customize the parameters of the deviation penalty according to the current roadway type, particular lane boundary that the ego vehicle is against and roadway curvature and (c) we update the deviation penalty based on the available gap in the adjacent lane. We postulate that this formulation can provide a more robust and achievable view of the localization requirements than previous research while focusing on safety.