Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. Such information is coined as guidance. For example, in text-to-image synthesis, text input is encoded as guidance to generate semantically aligned images. Proper guidance inputs are closely tied to the performance of diffusion models. A common observation is that strong guidance promotes a tight alignment to the task-specific information, while reducing the diversity of the generated samples. In this paper, we provide the first theoretical study towards understanding the influence of guidance on diffusion models in the context of Gaussian mixture models. Under mild conditions, we prove that incorporating diffusion guidance not only boosts classification confidence but also diminishes distribution diversity, leading to a reduction in the differential entropy of the output distribution. Our analysis covers the widely adopted sampling schemes including DDPM and DDIM, and leverages comparison inequalities for differential equations as well as the Fokker-Planck equation that characterizes the evolution of probability density function, which may be of independent theoretical interest.
Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccuracies in the generated text might be obscured by the rest of the output being generally factual, making it extremely hard for the users to spot them. Current services that leverage LLMs usually do not provide any means for detecting unreliable generations. Here, we aim to bridge this gap. In particular, we propose a novel fact-checking and hallucination detection pipeline based on token-level uncertainty quantification. Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output. Moreover, we present a novel token-level uncertainty quantification method that removes the impact of uncertainty about what claim to generate on the current step and what surface form to use. Our method Claim Conditioned Probability (CCP) measures only the uncertainty of particular claim value expressed by the model. Experiments on the task of biography generation demonstrate strong improvements for CCP compared to the baselines for six different LLMs and three languages. Human evaluation reveals that the fact-checking pipeline based on uncertainty quantification is competitive with a fact-checking tool that leverages external knowledge.
Large Language Models (LLMs) have demonstrated exceptional performance in biochemical tasks, especially the molecule caption translation task, which aims to bridge the gap between molecules and natural language texts. However, previous methods in adapting LLMs to the molecule-caption translation task required extra domain-specific pre-training stages, suffered weak alignment between molecular and textual spaces, or imposed stringent demands on the scale of LLMs. To resolve the challenges, we propose In-Context Molecule Adaptation (ICMA), as a new paradigm allowing LLMs to learn the molecule-text alignment from context examples via In-Context Molecule Tuning. Specifically, ICMA incorporates the following three stages: Cross-modal Retrieval, Post-retrieval Re-ranking, and In-context Molecule Tuning. Initially, Cross-modal Retrieval utilizes BM25 Caption Retrieval and Molecule Graph Retrieval to retrieve informative context examples. Additionally, we also propose Post-retrieval Re-ranking with Sequence Reversal and Random Walk to further improve the quality of retrieval results. Finally, In-Context Molecule Tuning unlocks the in-context molecule learning capability of LLMs with retrieved examples and adapts the parameters of LLMs for the molecule-caption translation task. Experimental results demonstrate that ICMT can empower LLMs to achieve state-of-the-art or comparable performance without extra training corpora and intricate structures, showing that LLMs are inherently in-context molecule learners.
The challenge of visual grounding and masking in multimodal machine translation (MMT) systems has encouraged varying approaches to the detection and selection of visually-grounded text tokens for masking. We introduce new methods for detection of visually and contextually relevant (concrete) tokens from source sentences, including detection with natural language processing (NLP), detection with object detection, and a joint detection-verification technique. We also introduce new methods for selection of detected tokens, including shortest $n$ tokens, longest $n$ tokens, and all detected concrete tokens. We utilize the GRAM MMT architecture to train models against synthetically collated multimodal datasets of source images with masked sentences, showing performance improvements and improved usage of visual context during translation tasks over the baseline model.
Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data workers often rely on dataset summaries, especially distributions of various derived features. Some features, like toxicity or topics, are relevant to many datasets, but many interesting features are domain specific: instruments and genres for a music dataset, or diseases and symptoms for a medical dataset. Accordingly, data workers often run custom analyses for each dataset, which is cumbersome and difficult. We present AutoHistograms, a visualization tool leveragingLLMs. AutoHistograms automatically identifies relevant features, visualizes them with histograms, and allows the user to interactively query the dataset for categories of entities and create new histograms. In a user study with 10 data workers (n=10), we observe that participants can quickly identify insights and explore the data using AutoHistograms, and conceptualize a broad range of applicable use cases. Together, this tool and user study contributeto the growing field of LLM-assisted sensemaking tools.
Large Language models (LLMs) are achieving state-of-the-art performance in many different downstream tasks. However, the increasing urgency of data privacy requires LLMs to train with Differential Privacy (DP) on private data. Concurrently it is also necessary to compress LLMs for real-life deployments on resource-constrained devices or latency-sensitive applications. Differential privacy and model compression generally must trade off utility loss to achieve their objectives. Moreover, concurrently achieving both can result in even more utility loss. To this end, we propose a novel differentially private knowledge distillation algorithm that exploits synthetic data generated by a differentially private LLM. The knowledge of a teacher model is transferred onto the student in two ways: one way from the synthetic data itself, the hard labels, and the other way by the output distribution of the teacher model evaluated on the synthetic data, the soft labels. Furthermore, if the teacher and student share a similar architectural structure, we can further distill knowledge by exploiting hidden representations. Our results show that our framework substantially improves the utility over existing baselines with strong privacy parameters, {\epsilon} = 2, validating that we can successfully compress autoregressive LLMs while preserving the privacy of training data.
Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. However, extracting information from textual rules to convert them to a machine-readable format has been a challenge due to the complexities associated with natural language and the limited resources that can support advanced machine-learning techniques. To address this challenge, we introduce CODE-ACCORD, a unique dataset compiled under the EU Horizon ACCORD project. CODE-ACCORD comprises 862 self-contained sentences extracted from the building regulations of England and Finland. Aligned with our core objective of facilitating information extraction from text for machine-readable rule generation, each sentence was annotated with entities and relations. Entities represent specific components such as "window" and "smoke detectors", while relations denote semantic associations between these entities, collectively capturing the conveyed ideas in natural language. We manually annotated all the sentences using a group of 12 annotators. Each sentence underwent annotations by multiple annotators and subsequently careful data curation to finalise annotations, ensuring their accuracy and reliability, thereby establishing the dataset as a solid ground truth. CODE-ACCORD offers a rich resource for diverse machine learning and natural language processing (NLP) related tasks in ACC, including text classification, entity recognition and relation extraction. To the best of our knowledge, this is the first entity and relation-annotated dataset in compliance checking, which is also publicly available.
Text document clustering can play a vital role in organizing and handling the everincreasing number of text documents. Uninformative and redundant features included in large text documents reduce the effectiveness of the clustering algorithm. Feature selection (FS) is a well-known technique for removing these features. Since FS can be formulated as an optimization problem, various meta-heuristic algorithms have been employed to solve it. Teaching-Learning-Based Optimization (TLBO) is a novel meta-heuristic algorithm that benefits from the low number of parameters and fast convergence. A hybrid method can simultaneously benefit from the advantages of TLBO and tackle the possible entrapment in the local optimum. By proposing a hybrid of TLBO, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) operators, this paper suggests a filter-based FS algorithm (TLBO-GWO). Six benchmark datasets are selected, and TLBO-GWO is compared with three recently proposed FS algorithms with similar approaches, the main TLBO and GWO. The comparison is conducted based on clustering evaluation measures, convergence behavior, and dimension reduction, and is validated using statistical tests. The results reveal that TLBO-GWO can significantly enhance the effectiveness of the text clustering technique (K-means).
Neural machine translation (MT) models achieve strong results across a variety of settings, but it is widely believed that they are highly sensitive to "noisy" inputs, such as spelling errors, abbreviations, and other formatting issues. In this paper, we revisit this insight in light of recent multilingual MT models and large language models (LLMs) applied to machine translation. Somewhat surprisingly, we show through controlled experiments that these models are far more robust to many kinds of noise than previous models, even when they perform similarly on clean data. This is notable because, even though LLMs have more parameters and more complex training processes than past models, none of the open ones we consider use any techniques specifically designed to encourage robustness. Next, we show that similar trends hold for social media translation experiments -- LLMs are more robust to social media text. We include an analysis of the circumstances in which source correction techniques can be used to mitigate the effects of noise. Altogether, we show that robustness to many types of noise has increased.
The rise of machine learning in recent years has brought benefits to various research fields such as wide fire detection. Nevertheless, small object detection and rare object detection remain a challenge. To address this problem, we present a dataset automata that can generate ground truth paired datasets using diffusion models. Specifically, we introduce a mask-guided diffusion framework that can fusion the wildfire into the existing images while the flame position and size can be precisely controlled. In advance, to fill the gap that the dataset of wildfire images in specific scenarios is missing, we vary the background of synthesized images by controlling both the text prompt and input image. Furthermore, to solve the color tint problem or the well-known domain shift issue, we apply the CLIP model to filter the generated massive dataset to preserve quality. Thus, our proposed framework can generate a massive dataset of that images are high-quality and ground truth-paired, which well addresses the needs of the annotated datasets in specific tasks.