LoRA-as-an-Attack! Piercing LLM Safety Under The Share-and-Play Scenario

Fine-tuning LLMs is crucial to enhancing their task-specific performance and ensuring model behaviors are aligned with human preferences. Among various fine-tuning methods, LoRA is popular for its efficiency and ease to use, allowing end-users to easily post and adopt lightweight LoRA modules on open-source platforms to tailor their model for different customization. However, such a handy share-and-play setting opens up new attack surfaces, that the attacker can render LoRA as an attacker, such as backdoor injection, and widely distribute the adversarial LoRA to the community easily. This can result in detrimental outcomes. Despite the huge potential risks of sharing LoRA modules, this aspect however has not been fully explored. To fill the gap, in this study we thoroughly investigate the attack opportunities enabled in the growing share-and-play scenario. Specifically, we study how to inject backdoor into the LoRA module and dive deeper into LoRA's infection mechanisms. We found that training-free mechanism is possible in LoRA backdoor injection. We also discover the impact of backdoor attacks with the presence of multiple LoRA adaptions concurrently as well as LoRA based backdoor transferability. Our aim is to raise awareness of the potential risks under the emerging share-and-play scenario, so as to proactively prevent potential consequences caused by LoRA-as-an-Attack. Warning: the paper contains potential offensive content generated by models.

Learning to Compress Prompt in Natural Language Formats

Large language models (LLMs) are great at processing multiple natural language processing tasks, but their abilities are constrained by inferior performance with long context, slow inference speed, and the high cost of computing the results. Deploying LLMs with precise and informative context helps users process large-scale datasets more effectively and cost-efficiently. Existing works rely on compressing long prompt contexts into soft prompts. However, soft prompt compression encounters limitations in transferability across different LLMs, especially API-based LLMs. To this end, this work aims to compress lengthy prompts in the form of natural language with LLM transferability. This poses two challenges: (i) Natural Language (NL) prompts are incompatible with back-propagation, and (ii) NL prompts lack flexibility in imposing length constraints. In this work, we propose a Natural Language Prompt Encapsulation (Nano-Capsulator) framework compressing original prompts into NL formatted Capsule Prompt while maintaining the prompt utility and transferability. Specifically, to tackle the first challenge, the Nano-Capsulator is optimized by a reward function that interacts with the proposed semantics preserving loss. To address the second question, the Nano-Capsulator is optimized by a reward function featuring length constraints. Experimental results demonstrate that the Capsule Prompt can reduce 81.4% of the original length, decrease inference latency up to 4.5x, and save 80.1% of budget overheads while providing transferability across diverse LLMs and different datasets.

Large Language Models As Faithful Explainers

Large Language Models (LLMs) have recently become proficient in addressing complex tasks by utilizing their rich internal knowledge and reasoning ability. Consequently, this complexity hinders traditional input-focused explanation algorithms for explaining the complex decision-making processes of LLMs. Recent advancements have thus emerged for self-explaining their predictions through a single feed-forward inference in a natural language format. However, natural language explanations are often criticized for lack of faithfulness since these explanations may not accurately reflect the decision-making behaviors of the LLMs. In this work, we introduce a generative explanation framework, xLLM, to improve the faithfulness of the explanations provided in natural language formats for LLMs. Specifically, we propose an evaluator to quantify the faithfulness of natural language explanation and enhance the faithfulness by an iterative optimization process of xLLM, with the goal of maximizing the faithfulness scores. Experiments conducted on three NLU datasets demonstrate that xLLM can significantly improve the faithfulness of generated explanations, which are in alignment with the behaviors of LLMs.

LETA: Learning Transferable Attribution for Generic Vision Explainer

Explainable machine learning significantly improves the transparency of deep neural networks~(DNN). However, existing work is constrained to explaining the behavior of individual model predictions, and lacks the ability to transfer the explanation across various models and tasks. This limitation results in explaining various tasks being time- and resource-consuming. To address this problem, we develop a pre-trained, DNN-based, generic explainer on large-scale image datasets, and leverage its transferability to explain various vision models for downstream tasks. In particular, the pre-training of generic explainer focuses on LEarning Transferable Attribution (LETA). The transferable attribution takes advantage of the versatile output of the target backbone encoders to comprehensively encode the essential attribution for explaining various downstream tasks. LETA guides the pre-training of the generic explainer towards the transferable attribution, and introduces a rule-based adaptation of the transferable attribution for explaining downstream tasks, without the need for additional training on downstream data. Theoretical analysis demonstrates that the pre-training of LETA enables minimizing the explanation error bound aligned with the conditional $\mathcal{V}$-information on downstream tasks. Empirical studies involve explaining three different architectures of vision models across three diverse downstream datasets. The experiment results indicate LETA is effective in explaining these tasks without the need for additional training on the data of downstream tasks.

CODA: Temporal Domain Generalization via Concept Drift Simulator

In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the "concept drift". Existing works propose model-specific strategies to achieve temporal generalization in the near-future domain. However, the diverse characteristics of real-world datasets necessitate customized prediction model architectures. To this end, there is an urgent demand for a model-agnostic temporal domain generalization approach that maintains generality across diverse data modalities and architectures. In this work, we aim to address the concept drift problem from a data-centric perspective to bypass considering the interaction between data and model. Developing such a framework presents non-trivial challenges: (i) existing generative models struggle to generate out-of-distribution future data, and (ii) precisely capturing the temporal trends of joint distribution along chronological source domains is computationally infeasible. To tackle the challenges, we propose the COncept Drift simulAtor (CODA) framework incorporating a predicted feature correlation matrix to simulate future data for model training. Specifically, CODA leverages feature correlations to represent data characteristics at specific time points, thereby circumventing the daunting computational costs. Experimental results demonstrate that using CODA-generated data as training input effectively achieves temporal domain generalization across different model architectures.

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph-based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system, enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath.

DISPEL: Domain Generalization via Domain-Specific Liberating

Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains. However, existing domain generalization approaches often bring in prediction-irrelevant noise or require the collection of domain labels. To address these challenges, we consider the domain generalization problem from a different perspective by categorizing underlying feature groups into domain-shared and domain-specific features. Nevertheless, the domain-specific features are difficult to be identified and distinguished from the input data. In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processing fine-grained masking approach that can filter out undefined and indistinguishable domain-specific features in the embedding space. Specifically, DISPEL utilizes a mask generator that produces a unique mask for each input data to filter domain-specific features. The DISPEL framework is highly flexible to be applied to any fine-tuned models. We derive a generalization error bound to guarantee the generalization performance by optimizing a designed objective loss. The experimental results on five benchmarks demonstrate DISPEL outperforms existing methods and can further generalize various algorithms.

Towards Assumption-free Bias Mitigation

Despite the impressive prediction ability, machine learning models show discrimination towards certain demographics and suffer from unfair prediction behaviors. To alleviate the discrimination, extensive studies focus on eliminating the unequal distribution of sensitive attributes via multiple approaches. However, due to privacy concerns, sensitive attributes are often either unavailable or missing in real-world scenarios. Therefore, several existing works alleviate the bias without sensitive attributes. Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias. The latter requires strong assumptions about the correlation between sensitive and non-sensitive attributes. As data distribution and task goals vary, the strong assumption on non-sensitive attributes may not be valid and require domain expertise. In this work, we propose an assumption-free framework to detect the related attributes automatically by modeling feature interaction for bias mitigation. The proposed framework aims to mitigate the unfair impact of identified biased feature interactions. Experimental results on four real-world datasets demonstrate that our proposed framework can significantly alleviate unfair prediction behaviors by considering biased feature interactions.

SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also leads to increased output variance, resulting in notably divergent outputs even when prompts share similar meanings. To tackle this challenge, we introduce a model-agnostic Soft Prompt-Based Calibration (SPeC) pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings on multiple clinical note tasks and LLMs indicate that our method not only bolsters performance but also effectively curbs variance for various LLMs, providing a more uniform and dependable solution for summarizing vital medical information.