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In recommender systems, users often seek the~*best* products through indirect, vague, or under-specified queries, such as &quot;best shoes for trail running&quot;. Such queries, also referred to as, implicit superlative queries pose a significant challenge for standard retrieval and ranking systems, as they lack an explicit mention of attributes and require identifying and reasoning over complex attributes. In this work, we explore if Large Language Models (LLMs) can improve product recommendation for such queries. As a first step, we propose a novel four-point schema for annotating the best product candidates for superlative queries and introduce a dataset, SUPPER, paired with LLM-based product annotations. We then empirically evaluate several existing retrieval and ranking approaches on our new dataset, providing insights and discussing their integration into real-world e-commerce production systems.

NAACL 2025

Show me the best! Generative Product Recommendation for Implicit Superlative Queries

product recommendation

superlatives

ranking

retrieval

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

IJCNLP-AACL 2025

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

In recommender systems, users often seek the~*best* products through indirect, vague, or under-specified queries, such as "best shoes for trail running". Such queries, also referred to as, implicit superlative queries pose a significant challenge for standard retrieval and ranking systems, as they lack an explicit mention of attributes and require identifying and reasoning over complex attributes. In this work, we explore if Large Language Models (LLMs) can improve product recommendation for such queries. As a first step, we propose a novel four-point schema for annotating the best product candidates for superlative queries and introduce a dataset, SUPPER, paired with LLM-based product annotations. We then empirically evaluate several existing retrieval and ranking approaches on our new dataset, providing insights and discussing their integration into real-world e-commerce production systems.

poster

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

This study examines performance of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA), focusing on implicit aspect extraction in novel domains. Using a synthetic sports feedback dataset, we evaluate open-weight LLMs' ability to extract aspect-polarity pairs and propose a metric for implicit aspect extraction. Our findings reveal both the promise and limitations of LLMs in ABSA tasks.

From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models

We consider the well-known and important tasks of clone detection and information retrieval for source code. The most standard setup is to search clones inside the same language code snippets. But it is also useful to find code snippets with identical behaviour in different programming languages. Nevertheless multi- and cross-lingual clone detection has been little studied in literature. We present a novel training procedure, cross-consistency training (CCT) leveraging cross-lingual similarity, that we apply to train language models on source code in various programming languages. We show that this training is effective both for encoder- and decoder-based models.
The trained encoder-based CCT-LM model
%and fine-tuned with CCT,
achieves a new state of the art on POJ-104 (monolingual C++ clone detection benchmark) with 96.73% MAP and AdvTest (monolingual Python code search benchmark) with 47.18% MRR. The decoder-based CCT-LM model shows comparable performance in these tasks. In addition, we formulate the multi- and cross-lingual clone detection problem and present XCD, a new benchmark dataset produced from CodeForces submissions.

CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search

We introduce **SPY Dataset**: a novel synthetic dataset for the task of **Personal Identifiable Information (PII) detection**, underscoring the significance of protecting PII in modern data processing. Our research innovates by leveraging Large Language Models (LLMs) to generate a dataset that emulates real-world PII scenarios. Through evaluation, we validate the dataset's quality, providing a benchmark for PII detection. Comparative analyses reveal that while PII and Named Entity Recognition (NER) share similarities, **dedicated NER models exhibit limitations** when applied to PII-specific contexts. This work contributes to the field by making the generation methodology and the generated dataset publicly [accessible](https://anonymous.4open.science/r/SPY_Dataset), thereby enabling further research and development in this field.

SPY: Enhancing Privacy with Synthetic PII Detection Dataset

Code-generating Large Language Models (LLMs) have become essential tools in modern software development, enhancing productivity and accelerating development. This paper aims to investigate the fine-tuning of code-generating LLMs using Reinforcement Learning and Direct Preference Optimization, further improving their performance. To achieve this, we enhance the training data for the reward model with the help of symbolic execution techniques, ensuring more comprehensive and objective data. With symbolic execution, we create a custom dataset that better captures the nuances in code evaluation. Our reward models, fine-tuned on this dataset, demonstrate significant improvements over the baseline, CodeRL, in estimating the quality of generated code. Our code-generating LLMs, trained with the help of reward model feedback, achieve similar results compared to the CodeRL benchmark.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs

Existing in-context learning (ICL) methods for relation extraction (RE) often prioritize language similarity over structural similarity, which can lead to overlooking entity relationships. To address this, we propose an AMR-enhanced retrieval-based ICL method for RE. Our model retrieves in-context examples based on semantic structure similarity between task inputs and training samples. We first conduct the experiments in supervised setting on four standard English RE datasets, results show that our method achieves state-of-the-art performance on three datasets and competitive results on the fourth in the supervised setting. Furthermore, our method outperforms baselines by a large margin across all datasets in the more demanding unsupervised setting.

AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction

Annotator perspectives are crucial in subjective NLP tasks, yet most models rely on aggregated labels, overlooking demographic variation. We introduce the Annotator-Wise Attention Network (AWAN), which updates token representations into feature-specific representations by attending to demographic features, allowing models to learn annotation-specific patterns. Using the EXIST sexism detection dataset, we show that the AWAN-Full approach, which leverages all possible demographic feature combinations, improves sexism detection performance when considering annotator labels. Additionally, AWAN outperforms a widely used multi-task model in predicting annotator-specific labels, highlighting its effectiveness in capturing annotation diversity for perspective-aware classification.

Demographic Features for Annotation-Aware Classification

Large Language Models (LLMs) are primarily trained on high-resource natural languages, limiting their effectiveness in low-resource settings and in tasks requiring deep logical reasoning. This research investigates the use of a custom logical language as a controlled environment for evaluating LLMs' pattern recognition and problem-solving abilities. We assess their logical accuracy and generalization capabilities by translating a dataset of logical propositions from Lean into this newly created language, which is then used to fine-tune an LLM (e.g GPT-4o). Our experiments analyze the impact of the size of the dataset and the translation methodology on the performance of the model. Our results indicate that preserving logical relationships in translation process significantly boost precision, with accuracy plateauing beyond roughly 20,000 training samples. These insights provide valuable guidelines for optimizing LLM training in formal reasoning tasks and improving performance in various low-resource language applications.

Propositional Logic as a Benchmark for Large Language Model Reasoning

Code Language Models have been trained to generate accurate solutions, typically with no regard for runtime. On the other hand, previous works that explored execution optimisation have observed corresponding drops in functional correctness. To that end, we introduce Code-Optimise, a framework that incorporates both correctness (passed, failed) and runtime (quick, slow) as learning signals via self-generated preference data. Our framework is both lightweight and robust as it dynamically selects solutions to reduce overfitting while avoiding a reliance on larger models for learning signals. Code-Optimise achieves significant improvements in pass@k while decreasing the competitive baseline runtimes by an additional 6% for in-domain data and up to 3% for out-of-domain data. As a by-product, the average length of the generated solutions is reduced by up to 48% on MBPP and 23% on HumanEval, resulting in faster and cheaper inference. The generated data and codebase is open-sourced at https://github.com/huawei-noah/HEBO/tree/Code_Optimise.

Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency

With the success of integrating large language models into the development of conversational systems, many studies have shown the effectiveness of retrieving and augmenting external knowledge for informative responses. While many existing studies agree on the necessity of Retrieval Augmented Generation (RAG), further investigation into the necessity and value of applying RAG to every turn of the conversation is needed. In this study, we propose to investigate the need for each turn of system response to be augmented with external knowledge. In particular, by leveraging human judgements on the binary choice of adaptive augmentation, we develop RAGate, a gating model, which models conversation context and relevant inputs to predict if a conversational system requires RAG for improved responses. We conduct extensive experiments on devising and applying RAGate to conversational models, joined with well-rounded analyses of various conversational scenarios. Our experimental results and analysis indicate the effective application of RAGate in RAG-based conversational systems in identifying if system responses require RAG to generate high-quality responses with high confidence. This study also identifies and shows the correlation between the generation's confidence level and the relevance of the augmented knowledge. We have also released the implementation code and resources in https://github.com/wangxieric/RAGate.

Adaptive Retrieval-Augmented Generation for Conversational Systems

Numerous adversarial defense methods have been proposed to strengthen the robustness of Natural Language Processing (NLP) models against adversarial attacks. However, many of these methods rely on predetermined linguistic knowledge and assume that attackers' synonym candidates are known, which is often unrealistic. In this work, we investigate adversarial training in the embedding space and introduce a Fast Adversarial Training (FAT) method to improve the model robustness without requiring synonym awareness. FAT leverages single-step perturbation generation and effective perturbation initialization based on two key insights: (1) adversarial perturbations generated by single-step and multi-step gradient ascent are similar, and (2) perturbations generated on the same training sample across successive epochs exhibit resemblance. By employing single-step gradient ascent and leveraging historical perturbation information, FAT not only expedites the training process but also efficiently initializes perturbations. Extensive experiments demonstrate that FAT significantly enhances the robustness of popular NLP models under scenarios where synonyms are unknown, outperforming other defense baselines under various character-level and word-level attacks.

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