More Expressive Attention with Negative Weights

We propose a novel attention mechanism, named Cog Attention, that enables attention weights to be negative for enhanced expressiveness, which stems from two key factors: (1) Cog Attention can shift the token deletion and copying function from a static OV matrix to dynamic QK inner products, with the OV matrix now focusing more on refinement or modification. The attention head can simultaneously delete, copy, or retain tokens by assigning them negative, positive, or minimal attention weights, respectively. As a result, a single attention head becomes more flexible and expressive. (2) Cog Attention improves the model's robustness against representational collapse, which can occur when earlier tokens are over-squashed into later positions, leading to homogeneous representations. Negative weights reduce effective information paths from earlier to later tokens, helping to mitigate this issue. We develop Transformer-like models which use Cog Attention as attention modules, including decoder-only models for language modeling and U-ViT diffusion models for image generation. Experiments show that models using Cog Attention exhibit superior performance compared to those employing traditional softmax attention modules. Our approach suggests a promising research direction for rethinking and breaking the entrenched constraints of traditional softmax attention, such as the requirement for non-negative weights.

RosePO: Aligning LLM-based Recommenders with Human Values

Recently, there has been a growing interest in leveraging Large Language Models (LLMs) for recommendation systems, which usually adapt a pre-trained LLM to the recommendation scenario through supervised fine-tuning (SFT). However, both the pre-training and SFT stages fail to explicitly model the comparative relationships of a user's preferences on different items. To construct a "helpful and harmless" LLM-based recommender, we propose a general framework -- Recommendation with smoothing personalized Preference Optimization (RosePO), which better aligns with customized human values during the post-training stage. Specifically, in addition to the input and chosen response that naturally align with SFT data, we design a rejected sampling strategy tailored for enhancing helpfulness, along with two strategies aimed at mitigating biases to promote harmlessness. To ensure robustness against uncertain labels present in automatically constructed preference data, we introduce a personalized smoothing factor predicted by a preference oracle into the optimization objective. Evaluation on three real-world datasets demonstrates the effectiveness of our method, showcasing not only improved recommendation performance but also mitigation of semantic hallucination and popularity bias.

LLaRA: Aligning Large Language Models with Sequential Recommenders

Sequential recommendation aims to predict the subsequent items matching user preference based on her/his historical interactions. With the development of Large Language Models (LLMs), there is growing interest in exploring the potential of LLMs for sequential recommendation by framing it as a language modeling task. Prior works represent items in the textual prompts using either ID indexing or text indexing and feed the prompts into LLMs, but falling short of either encapsulating comprehensive world knowledge or exhibiting sufficient sequential understanding. To harness the complementary strengths of traditional recommenders (which encode user behavioral knowledge) and LLMs (which possess world knowledge about items), we propose LLaRA -- a Large Language and Recommendation Assistant framework. Specifically, LLaRA represents items in LLM's input prompts using a novel hybrid approach that integrates ID-based item embeddings from traditional recommenders with textual item features. Viewing the ``sequential behavior of the user'' as a new modality in recommendation, we employ an adapter to bridge the modality gap between ID embeddings of the traditional recommenders and the input space of LLMs. Furthermore, instead of directly exposing the hybrid prompt to LLMs, we apply a curriculum learning approach to gradually ramp up training complexity. We first warm up the LLM with text-only prompting, which aligns more naturally with the LLM's language modeling capabilities. Thereafter, we progressively transition to hybrid prompting, training the adapter to incorporate behavioral knowledge from the traditional sequential recommender into the LLM. Extensive experiments demonstrate the efficacy of LLaRA framework. Our code and data are available at https://github.com/ljy0ustc/LLaRA .

Text-to-Image Generation for Abstract Concepts

Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts. Unlike concrete concepts that are usually directly associated with physical objects, expressing abstract concepts through natural language requires considerable effort, which results from their intricate semantics and connotations. An alternative approach is to leverage images to convey rich visual information as a supplement. Nevertheless, existing Text-to-Image (T2I) models are primarily trained on concrete physical objects and tend to fail to visualize abstract concepts. Inspired by the three-layer artwork theory that identifies critical factors, intent, object and form during artistic creation, we propose a framework of Text-to-Image generation for Abstract Concepts (TIAC). The abstract concept is clarified into a clear intent with a detailed definition to avoid ambiguity. LLMs then transform it into semantic-related physical objects, and the concept-dependent form is retrieved from an LLM-extracted form pattern set. Information from these three aspects will be integrated to generate prompts for T2I models via LLM. Evaluation results from human assessments and our newly designed metric concept score demonstrate the effectiveness of our framework in creating images that can sufficiently express abstract concepts.