Process supervision, i.e., evaluating each step, is critical for complex large language model (LLM) reasoning and test-time searching with increased inference compute. Existing approaches, represented by process reward models (PRMs), primarily focus on rewarding signals up to the current step, exhibiting a one-directional nature and lacking a mechanism to model the distance to the final target. To address this problem, we draw inspiration from the A* algorithm, which states that an effective supervisory signal should simultaneously consider the incurred cost and the estimated cost for reaching the target. Building on this key insight, we introduce BiRM, a novel process supervision model that not only evaluates the correctness of previous steps but also models the probability of future success. We conduct extensive experiments on mathematical reasoning tasks and demonstrate that BiRM provides more precise evaluations of LLM reasoning steps, achieving an improvement of 3.1% on Gaokao2023 over PRM under the Best-of-N sampling method. Besides, in search-based strategies, BiRM provides more comprehensive guidance and outperforms ORM by 5.0% and PRM by 3.8% respectively on MATH-500.
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Assigning Distinct Roles to Quantized and Low-Rank Matrices Toward Optimal Weight Decomposition
PDF ↗Decomposing weight matrices into quantization and low-rank components (\bf W\approx Q+LR) is a widely used technique for compressing large language models (LLMs). Existing joint optimization methods iteratively alternate between quantization and low-rank approximation. However, these methods tend to prioritize one component at the expense of the other, resulting in suboptimal decompositions that fail to leverage each component’s unique strengths. In this work, we introduce Outlier-Driven Low-Rank Initialization (ODLRI), which assigns low-rank components the specific role of capturing activation-sensitive weights. This structured decomposition mitigates outliers’ negative impact on quantization, enabling more effective balance between quantization and low-rank approximation. Experiments on Llama2 (7B, 13B, 70B), Llama3-8B, and Mistral-7B demonstrate that incorporating ODLRI into the joint optimization framework consistently reduces activation-aware error, minimizes quantization scale, and improves perplexity and zero-shot accuracy in low-bit settings.
The rapid development and increasingly widespread applications of Large Language Models (LLMs) have made the safety issues of LLMs more prominent and critical. Although safety training is widely used in LLMs, the mismatch between pre-training and safety training still leads to safety vulnerabilities. To expose the safety vulnerabilities in LLMs and improve LLMs’ performance in safety, we propose a novel framework, SemanticCamo, which attacks LLMs through semantic camouflage.SemanticCamo bypasses safety guardrails by replacing the original unsafe content with semantic features, thereby concealing malicious intent while keeping the query’s objectives unchanged. We conduct comprehensive experiments on the state-of-the-art LLMs, including GPT-4o and Claude-3.5, finding that SemanticCamo successfully induces harmful responses from the target models in over 80% of cases on average, outperforming previous counterparts. Additionally, the performance of SemanticCamo against various defenses is evaluated, demonstrating that semantic transformations introduce critical challenges to LLM safety, necessitating targeted alignment strategies to address this vulnerability. Code and data are available at https://github.com/Jihui-Yan/SemanticCamo.
Majority voting is considered an effective method to enhance chain-of-thought reasoning, as it selects the answer with the highest ”self-consistency” among different reasoning paths (Wang et al., 2023). However, previous chain-of-thought reasoning methods typically generate only a single answer in each trial, thereby ignoring the possibility of other potential answers. As a result, these alternative answers are often overlooked in subsequent voting processes. In this work, we propose to generate ranked answers in each reasoning process and conduct ranked voting among multiple ranked answers from different responses, thereby making the overall self-consistency more reliable. Specifically, we use three ranked voting methods: Instant-runoff voting, Borda count voting, and mean reciprocal rank voting. We validate our methods on six datasets, including three multiple-choice and three open-ended question-answering tasks, using both advanced open-source and closed-source large language models. Extensive experimental results indicate that our proposed method outperforms the baselines, showcasing the potential of leveraging the information of ranked answers and using ranked voting to improve reasoning performance. Code and logs will be released.
Semantic text representation is a fundamental task in the field of natural language processing. Existing text embedding (e.g., SimCSE and LLM2Vec) have demonstrated excellent performance, but the values of each dimension are difficult to trace and interpret. Bag-of-words, as classic sparse interpretable embeddings, suffers from poor performance. Recently, Benara et al. (2024) propose interpretable text embeddings using large language models, which forms ”0/1” embeddings based on responses to a series of questions. These interpretable text embeddings are typically high-dimensional (larger than 10,000). In this work, we propose Low-dimensional (lower than 500) Dense and Interpretable text embeddings with Relative representations (LDIR). The numerical values of its dimensions indicate semantic relatedness to different anchor texts through farthest point sampling, offering both semantic representation as well as a certain level of traceability and interpretability. We validate LDIR on multiple semantic textual similarity, retrieval, and clustering tasks. Extensive experimental results show that LDIR performs close to the black-box baseline models and outperforms the interpretable embeddings baselines with much fewer dimensions.
Detecting toxic content using language models is important but challenging. While large language models (LLMs) have demonstrated strong performance in understanding Chinese, recent studies show that simple character substitutions in toxic Chinese text can easily confuse the state-of-the-art (SOTA) LLMs. In this paper, we highlight the multimodal nature of Chinese language as a key challenge for deploying LLMs in toxic Chinese detection. First, we propose a taxonomy of 3 perturbation strategies and 8 specific approaches in toxic Chinese content. Then, we curate a dataset based on this taxonomy, and benchmark 9 SOTA LLMs (from both the US and China) to assess if they can detect perturbed toxic Chinese text. Additionally, we explore cost-effective enhancement solutions like in-context learning (ICL) and supervised fine-tuning (SFT). Our results reveal two important findings. (1) LLMs are less capable of detecting perturbed multimodal Chinese toxic contents. (2) ICL or SFT with a small number of perturbed examples may cause the LLMs “overcorrect”: misidentify many normal Chinese contents as toxic.
Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to extract aspect-sentiment pairs from text and image data. While significant progress has been made in image-aspect alignment, due to the subtlety and complexity of language expressions, there are not always explicit aspect words in the language to align with images. Existing methods typically assume a direct alignment between images and aspects, matching the entire image with a corresponding aspect. This rough alignment of images and aspects introduces noise. To address the above issues, this paper proposes a Dual-Aware Enhanced Alignment Network (DaNet) designed for fine-grained multimodal aspect-image alignment and denoising. Specifically, we first introduce a Multimodal Denoising Encoder (MDE) that jointly image and text to guide the compression and denoising of visual sequences. And then, aspect-aware and sentiment-aware networks are constructed to jointly enhance fine-grained alignment and denoising of text-image information. To better align implicit aspects, an Implicit Aspect Opinion Generation (IAOG) pretraining is designed under the guidance of large language model. Extensive experiments across three MABSA subtasks demonstrate that DaNet outperforms existing methods. Code will be available at https://github.com/***/DaNet.
The field of AI healthcare has undergone a significant transformation with the advent of large language models (LLMs), yet the challenges of interpretability within these models remain largely unaddressed. This study introduces **Chain-of-Diagnosis (CoD)** to enhance the interpretability of medical automatic diagnosis. CoD transforms the diagnostic process into a diagnostic chain that mirrors a physician’s thought process, providing a transparent reasoning pathway. Additionally, CoD outputs the disease confidence distribution to ensure transparency in decision-making. This interpretability makes model diagnostics controllable and aids in identifying critical symptoms for inquiry through the entropy reduction of confidences. With CoD, we developed **DiagnosisGPT**, capable of diagnosing 9,604 diseases for validating CoD. Experimental results demonstrate that DiagnosisGPT outperforms other LLMs on automatic diagnostic tasks across three real-world benchmarks. Moreover, DiagnosisGPT provides interpretability while ensuring controllability in diagnostic rigor.
Text watermarking, which modify tokens to embed watermark, has proven effective in detecting machine-generated texts. Yet its application to low-entropy texts like code and mathematics presents significant challenges. A fair number of tokens in these texts are hardly modifiable without changing the intended meaning, causing statistical measures to falsely indicate the absence of a watermark. Existing research addresses this issue by rely mainly on a limited number of high-entropy tokens, which are considered flexible for modification, and accurately reflecting watermarks. However, their detection accuracy remains suboptimal, as they neglect strong watermark evidences embedded in low entropy tokens modified through watermarking. To overcome this limitation, we introduce Bayes’ Rule derived Watermark Detector (BRWD), which exploit watermark information from every token, by leveraging the posterior probability of watermark’s presence. We theoretically prove the optimality of our method in terms of detection accuracy, and demonstrate its superiority across various datasets, models, and watermark injection strategies. Notably, our method achieves up to 50% and 70% relative improvements in detection accuracy over the best baselines in code generation and math problem-solving tasks, respectively. Our code is available at https://github.com/cczslp/BRWD.
The capabilities of large language models (LLMs) have been enhanced by training on data that reflects human thought processes, such as the Chain-of-Thought format. However, evidence suggests that the conventional scheme of next-word prediction may not fully capture how humans learn to think. Inspired by how humans generalize mathematical reasoning, we propose a new approach named ClozeMath to fine-tune LLMs for mathematical reasoning. Our ClozeMath involves a text-infilling task that predicts masked equations from a given solution, analogous to cloze exercises used in human learning. Experiments on GSM8K, MATH, and GSM-Symbolic show that ClozeMath surpasses the strong baseline Masked Thought in performance and robustness, with two test-time scaling decoding algorithms, Beam Search and Chain-of-Thought decoding. Additionally, we conduct an ablation study to analyze the effects of various architectural and implementation choices on our approach.
Knowledge retrieval and response generation are fundamental to task-oriented dialogue systems. However, dialogue context frequently contains noisy or irrelevant information, leading to sub-optimal result in knowledge retrieval. One possible approach to retrieving knowledge is to manually annotate standard queries for each dialogue. Yet, this approach is hindered by the challenge of data scarcity, as human annotation is costly. To solve the challenge, we propose an LLM-enhanced model of query-guided knowledge retrieval for task-oriented dialogue. It generates high-quality queries for knowledge retrieval in task-oriented dialogue solely using low-resource annotated queries. To strengthen the performance correlation between response generation and knowledge retrieval, we propose a retrieval preservation mechanism by further selecting the most relevant knowledge from retrieved top-K records and explicitly incorporating these as prompts to guide a generator in response generation. Experiments on three standard benchmarks demonstrate that our model and mechanism outperform previous state-of-the-art by 3.26% on average with two widely used evaluation metrics.
Data watermarking in language models injects traceable signals, such as specific token sequences or stylistic patterns, into copyrighted text, allowing copyright holders to track and verify training data ownership. Previous data watermarking techniques primarily focus on effective memorization after pretraining, while overlooking challenges that arise in other stages of the LLM pipeline, such as the risk of watermark filtering during data preprocessing, or potential forgetting through post-training, or verification difficulties due to API-only access. We propose a novel data watermarking approach that injects coherent and plausible yet fictitious knowledge into training data using generated passages describing a fictitious entity and its associated attributes. Our watermarks are designed to be memorized by the LLM through seamlessly integrating in its training data, making them harder to detect lexically during preprocessing. We demonstrate that our watermarks can be effectively memorized by LLMs, and that increasing our watermarks’ density, length, and diversity of attributes strengthens their memorization. We further show that our watermarks remain robust throughout LLM development, maintaining their effectiveness after continual pretraining and supervised finetuning. Finally, we show that our data watermarks can be evaluated even under API-only access via question answering.
Frequently updating Large Language Model (LLM)-based recommender systems to adapt to dynamic user interests—as done for traditional ones—is impractical due to high training costs, even with acceleration methods. This work explores the possibility of adapting the model to dynamic user interests without any model-level updates via In-context Learning (ICL), which enables adaptation through few-shot examples within input prompts. While using recent user interactions as ICL demonstrations offers a potential solution for dynamic interest adaptation, existing LLM-based recommenders face critical limitations: recommendation-specific tuning often diminishes the model’s in-context learning ability, and the original LLM’s ICL lacks task-specific optimization for recommendations. To bridge this gap, we introduce RecICL, a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats. RecICL achieves dual objectives: (1) preserving fundamental ICL capabilities during recommendation adaptation and (2) dynamically capturing user preference evolution through the most recent interactions. Extensive experiments across multiple benchmarks demonstrate RecICL’s superior performance, achieving better results without model updates. Our implementation is publicly available at https://anonymous.4open.science/r/RecICL-8003.
The rapid rise of large language models (LLMs) in text streaming services has introduced significant cost and Quality of Experience (QoE) challenges in serving millions of daily requests, especially in meeting Time-To-First-Token (TTFT) and Time-Between-Token (TBT) requirements for real-time interactions. Our real-world measurements show that both server-based and on-device deployments struggle to meet diverse QoE demands: server deployments face high costs and last-hop issues (e.g., Internet latency and dynamics), while on-device LLM inference is constrained by resources. We introduce , a device-server cooperative scheduler designed to optimize users’ QoE by adaptively routing requests and migrating response generation between endpoints while maintaining cost constraints. employs cost-aware scheduling, leveraging the predictable speed of on-device LLM inference with the flexible capacity of server-based inference to dispatch requests on the fly, while introducing a token-level migration mechanism to ensure consistent token delivery during migration. Evaluations on real-world workloads—including commercial services like OpenAI GPT and DeepSeek, and open-source deployments such as LLaMA3—show that can improve users’ QoE by reducing tail TTFT (11-52%) and mean TTFT (6-78%) across different model-device configurations, while dramatically reducing serving costs by up to 84% through its migration mechanism while maintaining comparable QoE levels.
Locate-then-Edit Knowledge Editing (LEKE) is a key technique for updating large language models (LLMs) without full retraining. However, existing methods assume a single-user setting and become inefficient in real-world multi-client scenarios, where decentralized organizations (e.g., hospitals, financial institutions) independently update overlapping knowledge, leading to redundant mediator knowledge vector (MKV) computations and privacy concerns.To address these challenges, we introduce \textbf{Fed}erated \textbf{L}ocate-then-\textbf{E}dit \textbf{K}nowledge \textbf{E}diting (FedLEKE), a novel task that enables multiple clients to collaboratively perform LEKE while preserving privacy and reducing computational overhead. To achieve this, we propose FedEdit, a two-stage framework that optimizes MKV selection and reuse.In the first stage, clients locally apply LEKE and upload the computed MKVs. In the second stage, rather than relying solely on server-based MKV sharing, FedLEKE allows clients retrieve relevant MKVs based on cosine similarity, enabling knowledge re-edit and minimizing redundant computations.Experimental results on two benchmark datasets demonstrate that FedEdit retains over 96% of the performance of non-federated LEKE while significantly outperforming a FedAvg-based baseline by approximately twofold. Besides, we find that MEMIT performs more consistently than PMET in the FedLEKE task with our FedEdit framework. Our code is available at https://github.com/zongkaiz/FedLEKE.
Most Large language models (LLMs) adopt an autoregressive architecture, predicting the next word token based on the preceding context. While this approach is robust for language generation tasks such as writing and summarization, it has limitations for high-level reasoning tasks, such as prediction and decision-making. To overcome these limitations, we introduce a new method called Prediction-Augmented Generation (PAG). PAG can improve the generation quality and predictive accuracy of large language models in inference-driven tasks by integrating task-specific predictive models as external tools, enabling more structured and precise reasoning. Moreover, our method does not simply copy the inferences of a predictive model, but improves the inference results with knowledge from the large language model to create better predictions. We comprehensively evaluate our proposed method on diverse datasets for automatic diagnosis tasks requiring extensive domain knowledge and advanced reasoning.
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning
PDF ↗As Multimodal Large Language Models (MLLMs) develop, their potential security issues have become increasingly prominent. **Machine Unlearning (MU)**, as an effective strategy for forgetting specific knowledge in training data, has been widely used in privacy protection. However, *MU for safety in MLLM has yet to be fully explored*. To address this issue, we propose , a safety unlearning benchmark for MLLMs, consisting of 3,000 images and 28.8K VQA pairs. We comprehensively evaluate unlearning methods from two perspectives: **_forget quality_** and **_model utility_**. Our findings show that existing MU methods struggle to maintain model performance while implementing the forget operation and often suffer from **_over-forgetting_**. Hence, we introduce **Prompt Decouple (PD) Loss** to alleviate over-forgetting through decouple prompt during unlearning process. To quantitatively measure over-forgetting mitigated by PD Loss, we propose a new metric called **Safe Answer Refusal Rate (SARR)**. Experimental results demonstrate that combining PD Loss with existing unlearning methods can effectively prevent over-forgetting and achieve a decrease of 79.5% in the SARR metric of LLaVA-7B and LLaVA-13B, while maintaining forget quality and model utility. Our code and dataset will be released upon acceptance. **Warning: This paper contains examples of harmful language and images, and reader discretion is recommended.**
Imagine to Hear: Auditory Knowledge Generation can be an Effective Assistant for Language Models
PDF ↗Language models pretrained on text-only corpora often struggle with tasks that require auditory commonsense knowledge.Previous work addresses this problem by augmenting the language model to retrieve knowledge from external audio databases.This approach has several limitations, such as the potential lack of relevant audio in databases and the high costs associated with constructing the databases. To address these issues, we propose Imagine to Hear, a novel approach that dynamically generates auditory knowledge using generative models. Our framework detects multiple audio-related textual spans from the given prompt and generates corresponding auditory knowledge. We develop several mechanisms to efficiently process multiple auditory knowledge, including a CLAP-based rejection sampler and a language-audio fusion module. Our experiments show that our method achieves state-of-the-art performance on AuditoryBench without relying on external databases, highlighting the effectiveness of our generation-based approach.
Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions
PDF ↗We present an end-to-end framework for generating synthetic users for evaluating interactive agents designed to encourage positive behavior changes, such as in health and lifestyle coaching. The synthetic users are grounded in health and lifestyle conditions, specifically sleep and diabetes management in this study, to ensure realistic interactions with the health coaching agent. Synthetic users are created in two stages: first, structured data are generated grounded in real-world health and lifestyle factors in addition to basic demographics and behavioral attributes; second, full profiles of the synthetic users are developed conditioned on the structured data. Interactions between synthetic users and the coaching agent are simulated using generative agent-based models such as Concordia, or directly by prompting a language model. Using two independently-developed agents for sleep and diabetes coaching as case studies, the validity of this framework is demonstrated by analyzing the coaching agent’s understanding of the synthetic users’ needs and challenges. Finally, through multiple blinded evaluations of user-coach interactions by human experts, we demonstrate that our synthetic users with health and behavioral attributes more accurately portray real human users with the same attributes, compared to generic synthetic users not grounded in such attributes. The proposed framework lays the foundation for efficient development of conversational agents through extensive, realistic, and grounded simulated interactions.
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting
PDF ↗Keyword spotting (KWS) offers a vital mechanism to identify spoken commands in voice-enabled systems, where user demands often shift, requiring models to learn new keywords continually over time. However, a major problem is catastrophic forgetting, where models lose their ability to recognize earlier keywords. Although several continual learning methods have proven their usefulness for reducing forgetting, most existing approaches depend on storing and revisiting old data to combat catastrophic forgetting. Though effective, these methods face two practical challenges: 1) privacy risks from keeping user data and 2) large memory and time consumption that limit deployment on small devices. To address these issues, we propose an exemplar-free Analytic Continual Learning (AnalyticKWS) method that updates model parameters without revisiting earlier data. Inspired by efficient learning principles, AnalyticKWS computes a closed-form analytical solution for model updates and requires only a single epoch of adaptation for incoming keywords. AnalyticKWS demands fewer computational resources by avoiding gradient-based updates and does not store old data. By eliminating the need for back-propagation during incremental learning, the model remains lightweight and efficient. As a result, AnalyticKWS meets the challenges mentioned earlier and suits resource-limited settings well. Extensive experiments on various datasets and settings show that AnalyticKWS consistently outperforms existing continual learning methods.