Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Congmin Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kangning Zhang, Rong Shan, Zeyu Zheng, Mengyue Yang, Jianghao Lin, Yong Yu, Weinan Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

As large language models (LLMs) achieve remarkable success in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and are being actively explored currently. In this article, we focus on adapting and enhancing large language models for recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation realms, i.e., LLMs fail to effectively extract useful information from a pure textual context of long user behavior sequence, even if the length of context is well below the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely, R etrieval- e nhanced L arge La nguage models Plus (ReLLaX), which provides full-stack optimization from three perspectives, i.e., data, prompt, and parameter. For data-level enhancement, we design semantic user behavior retrieval (SUBR) to reduce the heterogeneity of the behavior sequence, thus lowering the difficulty for LLMs to extract the essential information from user behavior sequences. Although SUBR can improve the data quality, further increase in the sequence length will still raise its heterogeneity to a level where LLMs can no longer comprehend it. Hence, we further propose to perform prompt-level and parameter-level enhancement, with the integration of conventional recommendation models (CRMs). As for prompt-level enhancement, we apply soft prompt augmentation (SPA) to explicitly inject collaborative knowledge from CRMs into the prompt. The item representations of LLMs are thus more aligned with recommendation, helping LLMs better explore the item relationships in the sequence and facilitating comprehension. Finally, for parameter-level enhancement, we propose component fully-interactive LoRA (CFLoRA). By enabling sufficient interaction between the LoRA atom components, the expressive ability of LoRA is extended, making the parameters effectively capture more sequence information. Moreover, we present new perspectives to compare current LoRA-based LLM4Rec methods, i.e., from both a composite and a decomposed view. We theoretically demonstrate that the ways they employ LoRA for recommendation are degraded versions of our CFLoRA, with different constraints on atom component interactions. Extensive experiments are conducted on three real-world public datasets to demonstrate the superiority of ReLLaX compared with existing baseline models, as well as its capability to alleviate lifelong sequential behavior incomprehension. Our code is available. 1

Survey papers are foundational to the scholarly progress of research communities, offering structured overviews that guide both novices and experts across disciplines. However, the recent surge of AI-generated surveys, especially enabled by large language models (LLMs), has transformed this traditionally labor-intensive genre into a low-effort, high-volume output. While such automation lowers entry barriers, it also introduces a critical threat: the phenomenon we term the "survey paper DDoS attack" to the research community. This refers to the unchecked proliferation of superficially comprehensive but often redundant, low-quality, or even hallucinated survey manuscripts, which floods preprint platforms, overwhelms researchers, and erodes trust in the scientific record. In this position paper, we argue that we must stop uploading massive amounts of AI-generated survey papers (i.e., survey paper DDoS attack) to the research community, by instituting strong norms for AI-assisted review writing. We call for restoring expert oversight and transparency in AI usage and, moreover, developing new infrastructures such as Dynamic Live Surveys, community-maintained, version-controlled repositories that blend automated updates with human curation. Through quantitative trend analysis, quality audits, and cultural impact discussion, we show that safeguarding the integrity of surveys is no longer optional but imperative to the research community.

We primarily focus on the field of multi-scenario recommendation, which poses a significant challenge in effectively leveraging data from different scenarios to enhance predictions in scenarios with limited data. Current mainstream efforts mainly center around innovative model network architectures, with the aim of enabling the network to implicitly acquire knowledge from diverse scenarios. However, the uncertainty of implicit learning in networks arises from the absence of explicit modeling, leading to not only difficulty in training but also incomplete user representation and suboptimal performance. Furthermore, through causal graph analysis, we have discovered that the scenario itself directly influences click behavior, yet existing approaches directly incorporate data from other scenarios during the training of the current scenario, leading to prediction biases when they directly utilize click behaviors from other scenarios to train models. To address these problems, we propose the Multi-Scenario Causal-driven Adaptive Network M-scan). This model incorporates a Scenario-Aware Co-Attention mechanism that explicitly extracts user interests from other scenarios that align with the current scenario. Additionally, it employs a Scenario Bias Eliminator module utilizing causal counterfactual inference to mitigate biases introduced by data from other scenarios. Extensive experiments on two public datasets demonstrate the efficacy of our M-scan compared to the existing baseline models.

This paper is concerned with TV recommendation, where one major challenge is the coupling behavior issue that the behaviors of multiple users are coupled together and not directly distinguishable because the users share the same account. Unable to identify the current watching user and use the coupling behaviors directly could lead to sub-optimal recommendation results due to the noise introduced by the behaviors of other users. Most existing methods deal with this issue either by unsupervised clustering algorithms or depending on latent user representation learning with strong assumptions. However, they neglect to sophisticatedly model the current session behaviors, which carry the information of user identification. Another critical limitation of the existing models is the lack of supervision signal on distinguishing behaviors because they solely depend on the final click label, which is insufficient to provide effective supervision. To address the above problems, we propose the Coupling Sequence Model (COSMO) for TV recommendation. In COSMO, we design a session-aware co-attention mechanism that uses both the candidate item and session behaviors as the query to attend to the historical behaviors in a fine-grained manner. Furthermore, we propose to use the data of accounts with multiple devices (e.g., families with various TV sets), which means the behaviors of one account are generated on different devices. We regard the device information as weak supervision and propose a novel pair-wise attention loss for learning to distinguish the coupling behaviors. Extensive offline experiments and online A/B tests over a commercial TV service provider demonstrate the efficacy of COSMO compared to the existing models.

Building a multi-stage cascade ranking system is a commonly used solution to balance the efficiency and effectiveness in modern information retrieval (IR) applications, such as recommendation and web search. Despite the popularity in practice, the literature specific on multi-stage cascade ranking systems is relatively scarce. The common practice is to train rankers of each stage independently using the same user feedback data (a.k.a., impression data), disregarding the data flow and the possible interactions between stages. This straightforward solution could lead to a sub-optimal system because of the sample selection bias (SSB) issue, which is especially damaging for cascade rankers due to the negative effect accumulated in the multiple stages. Worse still, the interactions between the rankers of each stage are not fully exploited. This paper provides an elaborate analysis of this commonly used solution to reveal its limitations. By studying the essence of cascade ranking, we propose a joint training framework named RankFlow to alleviate the SSB issue and exploit the interactions between the cascade rankers, which is the first systematic solution for this topic. We propose a paradigm of training cascade rankers that emphasizes the importance of fitting rankers on stage-specific data distributions instead of the unified user feedback distribution. We design the RankFlow framework based on this paradigm: The training data of each stage is generated by its preceding stages while the guidance signals not only come from the logs but its successors. Extensive experiments are conducted on various IR scenarios, including recommendation, web search and advertisement. The results verify the efficacy and superiority of RankFlow.