With the surge of ChatGPT,the use of large models has significantly increased,rapidly rising to prominence across the industry and sweeping across the internet. This article is a comprehensive review of fine-tuning methods for large models. This paper investigates the latest technological advancements and the application of advanced methods in aspects such as task-adaptive fine-tuning,domain-adaptive fine-tuning,few-shot learning,knowledge distillation,multi-task learning,parameter-efficient fine-tuning,and dynamic fine-tuning.

Natural Language Processing (NLP) is witnessing a remarkable breakthrough driven by the success of Large Language Models (LLMs). LLMs have gained significant attention across academia and industry for their versatile applications in text generation, question answering, and text summarization. As the landscape of NLP evolves with an increasing number of domain-specific LLMs employing diverse techniques and trained on various corpus, evaluating performance of these models becomes paramount. To quantify the performance, it's crucial to have a comprehensive grasp of existing metrics. Among the evaluation, metrics which quantifying the performance of LLMs play a pivotal role. This paper offers a comprehensive exploration of LLM evaluation from a metrics perspective, providing insights into the selection and interpretation of metrics currently in use. Our main goal is to elucidate their mathematical formulations and statistical interpretations. We shed light on the application of these metrics using recent Biomedical LLMs. Additionally, we offer a succinct comparison of these metrics, aiding researchers in selecting appropriate metrics for diverse tasks. The overarching goal is to furnish researchers with a pragmatic guide for effective LLM evaluation and metric selection, thereby advancing the understanding and application of these large language models.

Event coreference resolution (ECR) is the task of determining whether distinct mentions of events within a multi-document corpus are actually linked to the same underlying occurrence. Images of the events can help facilitate resolution when language is ambiguous. Here, we propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models. As existing ECR benchmark datasets rarely provide images for all event mentions, we augment the popular ECB+ dataset with event-centric images scraped from the internet and generated using image diffusion models. We establish three methods that incorporate images and text for coreference: 1) a standard fused model with finetuning, 2) a novel linear mapping method without finetuning and 3) an ensembling approach based on splitting mention pairs by semantic and discourse-level difficulty. We evaluate on 2 datasets: the augmented ECB+, and AIDA Phase 1. Our ensemble systems using cross-modal linear mapping establish an upper limit (91.9 CoNLL F1) on ECB+ ECR performance given the preprocessing assumptions used, and establish a novel baseline on AIDA Phase 1. Our results demonstrate the utility of multimodal information in ECR for certain challenging coreference problems, and highlight a need for more multimodal resources in the coreference resolution space.

The use of Machine Learning models for decision-making has become the new norm not only in tech but any business field imaginable, covering any possible task at hand be it search engine recommendations, customer churn prediction, credit risk scoring, energy load forecasting, or the deployment of personalized AI assistants. This comes at a time when developing ML models has become increasingly easier with the rise of open-source, free and user-friendly Python libraries such as Keras, scikit-learn, PyTorch and as generative AI-based conversational chatbots such as ChatGPT, Gemini and Claude that can provide coding assistance -- if not ready-made code for modeling -- are evolving rapidly. Such developments yet again beg the question of interpretability in machine learning, which has been formulated in various ways in literature and been offered multiple proposed solutions such as exploring causality (see Section 2.1), explainability (see Section 2.2) or abandoning black box ML models altogether. But to make a philosophical argument, it is hard to see the benefits of highly model or domain-specific, post-hoc, or complex solutions to obtain insights into the inner-doings of machine learning models when the modeling task itself is growing ever more accessible to laypeople. Common thought on categorizing ML models in this regard would argue that parametric models descending from the fields of statistics and econometrics such as Linear or Logistic Regression are by nature more interpretable than their data-driven and non-parametric counterparts such as tree-based models or neural networks.

Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic model developed by Marshall and Molteni (1993). We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high-pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high-pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre-training the CNN on the plentiful data of the Marshall-Molteni model, and then using Transfer Learning to achieve better predictions than direct training. SHAP analysis before and after transfer learning allows a comparison between the predictive features in the reanalysis and the quasigeostrophic model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.

Context: Motivation is known to improve performance. In software development in particular, there has been considerable interest in the motivation of contributors to open source. Objective: We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self use, etc.), and evaluate their relative effect on motivation. Since motivation is an internal subjective feeling, we also analyze the validity of the answers. Method: We conducted a survey with 66 questions on motivation which was completed by 521 developers. Most of the questions used an 11 point scale. We evaluated the validity of the answers validity by comparing related questions, comparing to actual behavior on GitHub, and comparison with the same developer in a follow up survey. Results: Validity problems include moderate correlations between answers to related questions, as well as self promotion and mistakes in the answers. Despite these problems, predictive analysis, investigating how diverse motivators influence the probability of high motivation, provided valuable insights. The correlations between the different motivators are low, implying their independence. High values in all 11 motivators predict increased probability of high motivation. In addition, improvement analysis shows that an increase in most motivators predicts an increase in general motivation.

The emergent large language/multimodal models facilitate the evolution of mobile agents, especially in the task of mobile UI automation. However, existing evaluation approaches, which rely on human validation or established datasets to compare agent-predicted actions with predefined ones, are unscalable and unfaithful. To overcome these limitations, this paper presents LlamaTouch, a testbed for on-device agent execution and faithful, scalable agent evaluation. By observing that the task execution process only transfers UI states, LlamaTouch employs a novel evaluation approach that only assesses whether an agent traverses all manually annotated, essential application/system states. LlamaTouch comprises three key techniques: (1) On-device task execution that enables mobile agents to interact with real mobile environments for task completion. (2) Fine-grained UI component annotation that merges pixel-level screenshots and textual screen hierarchies to explicitly identify and precisely annotate essential UI components with a rich set of designed annotation primitives. (3) A multi-level state matching algorithm that utilizes exact and fuzzy matching to accurately detect critical information in each screen with unpredictable UI layout/content dynamics. LlamaTouch currently incorporates four mobile agents and 495 UI automation tasks, encompassing both tasks in the widely-used datasets and our self-constructed ones for more diverse mobile applications. Evaluation results demonstrate the LlamaTouch's high faithfulness of evaluation in real environments and its better scalability than human validation. LlamaTouch also enables easy task annotation and integration of new mobile agents. Code and dataset are publicly available at https://github.com/LlamaTouch/LlamaTouch.

Home robots intend to make their users lives easier. Our work assists in this goal by enabling robots to inform their users of dangerous or unsanitary anomalies in their home. Some examples of these anomalies include the user leaving their milk out, forgetting to turn off the stove, or leaving poison accessible to children. To move towards enabling home robots with these abilities, we have created a new dataset, which we call SafetyDetect. The SafetyDetect dataset consists of 1000 anomalous home scenes, each of which contains unsafe or unsanitary situations for an agent to detect. Our approach utilizes large language models (LLMs) alongside both a graph representation of the scene and the relationships between the objects in the scene. Our key insight is that this connected scene graph and the object relationships it encodes enables the LLM to better reason about the scene -- especially as it relates to detecting dangerous or unsanitary situations. Our most promising approach utilizes GPT-4 and pursues a categorization technique where object relations from the scene graph are classified as normal, dangerous, unsanitary, or dangerous for children. This method is able to correctly identify over 90% of anomalous scenarios in the SafetyDetect Dataset. Additionally, we conduct real world experiments on a ClearPath TurtleBot where we generate a scene graph from visuals of the real world scene, and run our approach with no modification. This setup resulted in little performance loss. The SafetyDetect Dataset and code will be released to the public upon this papers publication.

The term bias was first introduced in the machine learning domain by Tom Mitchell in his 1980 paper titled "The need for biases in learning generalizations" Mitchell [1980]. The concept of bias refers to giving importance to particular features to improve generalization. This general idea of bias in machine learning is positive and necessary for models to perform, eliminating the risk of hyper-focusing on specific samples over others. On the contrary, bias can also be negative in machine learning. Negative bias can be defined as an inaccurate assumption made by a machine learning algorithm that is systematically or historically prejudiced against certain groups of people Zanna et al. [2022]. Decisions made by these biased algorithms could cause adverse effects on particular social groups, for example, those defined by sex, race, age, marital status, handicaps, etc., when used to make autonomous decisions in life-changing cases such as health, hiring, education, criminal sentencing, etc. Negative bias can be introduced into the machine pipeline in two main ways, through the data or the algorithm itself Blanzeisky and Cunningham [2021]. Bias due to data, also known as a negative legacy Cunningham and Delany [2021], Kamishima et al. [2012], can be caused by an imbalance in the representation of different population categories

These labels are then used in traditional supervised training schemas. This line of work in turn bifurcates This paper presents Conformer-1, an end-to-end Automatic into two main approaches. The first approach relies on generating Speech Recognition (ASR) model trained on an extensive pseudo-labels using a pre-existing baseline model [1, 6, 7], dataset of 570k hours of speech audio data, 91% of which was while the second approach attempts to source massive amounts acquired from publicly available sources. To achieve this, we of data of ambiguous quality from the public sources and then perform Noisy Student Training [1] after generating pseudolabels filter it down to a subset that is both human labeled and high for the unlabeled public data using a strong Conformer quality [8]. Our work attempts to address the data scarcity issue RNN-T baseline model. The addition of these pseudo-labeled head-on and leverages both data filtering and pseudo-labeling data results in remarkable improvements in relative Word Error to procure high-quality audio and labels at scale. Rate (WER) by 11.5% and 24.3% for our asynchronous and Following the example provided by Whisper [8], we realtime models, respectively. Additionally, the model is more sourced audio speech data from open and fair use sources available robust to background noise owing to the addition of these data.