Efficient Inference for Large Language Model-based Generative Recommendation

Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caused by autoregressive decoding. For lossless LLM decoding acceleration, Speculative Decoding (SD) has emerged as a promising solution. However, applying SD to generative recommendation presents unique challenges due to the requirement of generating top-K items (i.e., K distinct token sequences) as a recommendation list by beam search. This leads to more stringent verification in SD, where all the top-K sequences from the target LLM must be successfully drafted by the draft model at each decoding step. To alleviate this, we consider 1) boosting top-K sequence alignment between the draft model and the target LLM, and 2) relaxing the verification strategy to reduce trivial LLM calls. To this end, we propose an alignment framework named AtSpeed, which presents the AtSpeed-S optimization objective for top-K alignment under the strict top-K verification. Moreover, we introduce a relaxed sampling verification strategy that allows high-probability non-top-K drafted sequences to be accepted, significantly reducing LLM calls. Correspondingly, we propose AtSpeed-R for top-K alignment under this relaxed sampling verification. Empirical results on two real-world datasets demonstrate that AtSpeed significantly accelerates LLM-based generative recommendation, e.g., near 2x speedup under strict top-K verification and up to 2.5 speedup under relaxed sampling verification. The codes and datasets will be released in the near future.

Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence

Recommending items solely catering to users' historical interests narrows users' horizons. Recent works have considered steering target users beyond their historical interests by directly adjusting items exposed to them. However, the recommended items for direct steering might not align perfectly with users' interests evolution, detrimentally affecting target users' experience. To avoid this issue, we propose a new task named Proactive Recommendation in Social Networks (PRSN) that indirectly steers users' interest by utilizing the influence of social neighbors, i.e., indirect steering by adjusting the exposure of a target item to target users' neighbors. The key to PRSN lies in answering an interventional question: what would a target user's feedback be on a target item if the item is exposed to the user's different neighbors? To answer this question, we resort to causal inference and formalize PRSN as: (1) estimating the potential feedback of a user on an item, under the network interference by the item's exposure to the user's neighbors; and (2) adjusting the exposure of a target item to target users' neighbors to trade off steering performance and the damage to the neighbors' experience. To this end, we propose a Neighbor Interference Recommendation (NIRec) framework with two key modules: (1)an interference representation-based estimation module for modeling potential feedback; and (2) a post-learning-based optimization module for optimizing a target item's exposure to trade off steering performance and the neighbors' experience by greedy search. We conduct extensive semi-simulation experiments based on three real-world datasets, validating the steering effectiveness of NIRec.

Frequency Diverse RIS (FD-RIS) Enhanced Wireless Communications via Joint Distance-Angle Beamforming

The conventional reconfigurable intelligent surface (RIS) assisted far-field communication systems can only implement angle beamforming, which actually limits the capability for reconfiguring the wireless propagation environment. To overcome this limitation, this paper proposes a newly designed frequency diverse RIS (FD-RIS), which can achieve joint distance-angle beamforming with the assistance of the time modulation technology. The signal processing model for FD-RIS-aided wireless communications is first derived. Then, an optimization problem aimed at maximizing the achievable rate is formulated where the frequency-time modulations are jointly optimized to achieve distance-angle beamforming. Furthermore, a novel iterative algorithm based on the cross-entropy optimization (CEO) framework is proposed to effectively handle the non-convex optimization problem. The numerical results validate that the proposed FD-RIS assisted communication scheme can achieve a notable performance improvement compared with the baseline scheme utilizing traditional RIS. In addition, the effectiveness of the proposed CEO algorithm is further verified by comparing with the benchmark using the genetic algorithm (GA).

Incorporate LLMs with Influential Recommender System

Recommender systems have achieved increasing accuracy over the years. However, this precision often leads users to narrow their interests, resulting in issues such as limited diversity and the creation of echo chambers. Current research addresses these challenges through proactive recommender systems by recommending a sequence of items (called influence path) to guide user interest in the target item. However, existing methods struggle to construct a coherent influence path that builds up with items the user is likely to enjoy. In this paper, we leverage the Large Language Model's (LLMs) exceptional ability for path planning and instruction following, introducing a novel approach named LLM-based Influence Path Planning (LLM-IPP). Our approach maintains coherence between consecutive recommendations and enhances user acceptability of the recommended items. To evaluate LLM-IPP, we implement various user simulators and metrics to measure user acceptability and path coherence. Experimental results demonstrate that LLM-IPP significantly outperforms traditional proactive recommender systems. This study pioneers the integration of LLMs into proactive recommender systems, offering a reliable and user-engaging methodology for future recommendation technologies.

Debias Can be Unreliable: Mitigating Bias Issue in Evaluating Debiasing Recommendation

Recent work has improved recommendation models remarkably by equipping them with debiasing methods. Due to the unavailability of fully-exposed datasets, most existing approaches resort to randomly-exposed datasets as a proxy for evaluating debiased models, employing traditional evaluation scheme to represent the recommendation performance. However, in this study, we reveal that traditional evaluation scheme is not suitable for randomly-exposed datasets, leading to inconsistency between the Recall performance obtained using randomly-exposed datasets and that obtained using fully-exposed datasets. Such inconsistency indicates the potential unreliability of experiment conclusions on previous debiasing techniques and calls for unbiased Recall evaluation using randomly-exposed datasets. To bridge the gap, we propose the Unbiased Recall Evaluation (URE) scheme, which adjusts the utilization of randomly-exposed datasets to unbiasedly estimate the true Recall performance on fully-exposed datasets. We provide theoretical evidence to demonstrate the rationality of URE and perform extensive experiments on real-world datasets to validate its soundness.

Defending Jailbreak Attack in VLMs via Cross-modality Information Detector

Vision Language Models (VLMs) extend the capacity of LLMs to comprehensively understand vision information, achieving remarkable performance in many vision-centric tasks. Despite that, recent studies have shown that these models are susceptible to jailbreak attacks, which refer to an exploitative technique where malicious users can break the safety alignment of the target model and generate misleading and harmful answers. This potential threat is caused by both the inherent vulnerabilities of LLM and the larger attack scope introduced by vision input. To enhance the security of VLMs against jailbreak attacks, researchers have developed various defense techniques. However, these methods either require modifications to the model's internal structure or demand significant computational resources during the inference phase. Multimodal information is a double-edged sword. While it increases the risk of attacks, it also provides additional data that can enhance safeguards. Inspired by this, we propose $\underline{\textbf{C}}$ross-modality $\underline{\textbf{I}}$nformation $\underline{\textbf{DE}}$tecto$\underline{\textbf{R}}$ ($\textit{CIDER})$, a plug-and-play jailbreaking detector designed to identify maliciously perturbed image inputs, utilizing the cross-modal similarity between harmful queries and adversarial images. This simple yet effective cross-modality information detector, $\textit{CIDER}$, is independent of the target VLMs and requires less computation cost. Extensive experimental results demonstrate the effectiveness and efficiency of $\textit{CIDER}$, as well as its transferability to both white-box and black-box VLMs.

Revolutionizing Text-to-Image Retrieval as Autoregressive Token-to-Voken Generation

Text-to-image retrieval is a fundamental task in multimedia processing, aiming to retrieve semantically relevant cross-modal content. Traditional studies have typically approached this task as a discriminative problem, matching the text and image via the cross-attention mechanism (one-tower framework) or in a common embedding space (two-tower framework). Recently, generative cross-modal retrieval has emerged as a new research line, which assigns images with unique string identifiers and generates the target identifier as the retrieval target. Despite its great potential, existing generative approaches are limited due to the following issues: insufficient visual information in identifiers, misalignment with high-level semantics, and learning gap towards the retrieval target. To address the above issues, we propose an autoregressive voken generation method, named AVG. AVG tokenizes images into vokens, i.e., visual tokens, and innovatively formulates the text-to-image retrieval task as a token-to-voken generation problem. AVG discretizes an image into a sequence of vokens as the identifier of the image, while maintaining the alignment with both the visual information and high-level semantics of the image. Additionally, to bridge the learning gap between generative training and the retrieval target, we incorporate discriminative training to modify the learning direction during token-to-voken training. Extensive experiments demonstrate that AVG achieves superior results in both effectiveness and efficiency.

Optimal Kernel Choice for Score Function-based Causal Discovery

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.

Debiased Recommendation with Noisy Feedback

Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased learning of the prediction model under MNAR data, three typical solutions have been proposed, including error-imputation-based (EIB), inverse-propensity-scoring (IPS), and doubly robust (DR) methods. However, these methods ignore an alternative form of bias caused by the inconsistency between the observed ratings and the users' true preferences, also known as noisy feedback or outcome measurement errors (OME), e.g., due to public opinion or low-quality data collection process. In this work, we study intersectional threats to the unbiased learning of the prediction model from data MNAR and OME in the collected data. First, we design OME-EIB, OME-IPS, and OME-DR estimators, which largely extend the existing estimators to combat OME in real-world recommendation scenarios. Next, we theoretically prove the unbiasedness and generalization bound of the proposed estimators. We further propose an alternate denoising training approach to achieve unbiased learning of the prediction model under MNAR data with OME. Extensive experiments are conducted on three real-world datasets and one semi-synthetic dataset to show the effectiveness of our proposed approaches. The code is available at https://github.com/haoxuanli-pku/KDD24-OME-DR.

Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs

Large Language Models (LLMs) excel in various natural language processing tasks but struggle with hallucination issues. Existing solutions have considered utilizing LLMs' inherent reasoning abilities to alleviate hallucination, such as self-correction and diverse sampling methods. However, these methods often overtrust LLMs' initial answers due to inherent biases. The key to alleviating this issue lies in overriding LLMs' inherent biases for answer inspection. To this end, we propose a CounterFactual Multi-Agent Debate (CFMAD) framework. CFMAD presets the stances of LLMs to override their inherent biases by compelling LLMs to generate justifications for a predetermined answer's correctness. The LLMs with different predetermined stances are engaged with a skeptical critic for counterfactual debate on the rationality of generated justifications. Finally, the debate process is evaluated by a third-party judge to determine the final answer. Extensive experiments on four datasets of three tasks demonstrate the superiority of CFMAD over existing methods.