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As the diversity of users increases, the capability of providing personalized responses by large language models (LLMs) has become increasingly important. Existing approaches have only limited successes in LLM personalization, due to the absence of personalized learning or the reliance on shared personal data. This paper proposes a new approach for a few-shot personalization of LLMs with their mis-aligned responses (Fermi). Our key idea is to learn a set of personalized prompts for each user by progressively improving the prompts using LLMs, based on user profile (e.g., demographic information) and a few examples of previous opinions. During an iterative process of prompt improvement, we incorporate the contexts of mis-aligned responses by LLMs, which are especially crucial for the effective personalization of LLMs. In addition, we develop an effective inference method to further leverage the context of the test query and the personalized prompts. Our experimental results demonstrate that Fermi significantly improves performance across various benchmarks, compared to best-performing baselines.

NAACL 2025

Few-shot Personalization of LLMs with Mis-aligned Responses

few-shot data

llm personalization

prompt optimization

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

Workflows play a crucial role in enhancing enterprise efficiency by orchestrating complex processes with multiple tools or components. However, hand-crafted workflow construction requires expert knowledge, presenting significant technical barriers. Recent advancements in Large Language Models (LLMs) have improved the generation of workflows from natural language instructions (aka NL2Workflow), yet existing single LLM agent-based methods face performance degradation on complex tasks due to the need for specialized knowledge and the strain of task-switching. To tackle these challenges, we propose WorkTeam, a multi-agent NL2Workflow framework comprising a supervisor, orchestrator, and filler agent, each with distinct roles that collaboratively enhance the conversion process. As there are currently no publicly available NL2Workflow benchmarks, we also introduce the HW-NL2Workflow dataset, which includes 3,695 real-world business samples for training and evaluation. Experimental results show that our approach significantly increases the success rate of workflow construction, providing a novel and effective solution for enterprise NL2Workflow services.

WorkTeam: Constructing Workflows from Natural Language with Multi-Agents

Chinese Search Query Spell Correction is a task designed to autonomously identify and correct typographical errors within queries in the search engine. Despite the availability of comprehensive datasets like Microsoft Speller and Webis, their monolingual nature and limited scope pose significant challenges in evaluating modern pre-trained language models such as BERT and GPT. To address this, we introduce **QSpell 250K**, a large-scale benchmark specifically developed for Chinese Query Spelling Correction. QSpell 250K offers several advantages: 1) It contains over 250K samples, which is ten times more than previous datasets. 2) It covers a broad range of topics, from formal entities to everyday colloquialisms and idiomatic expressions. 3) It includes both Chinese and English, addressing the complexities of code-switching. Each query undergoes three rounds of high-fidelity annotation to ensure accuracy. Our extensive testing across three popular models demonstrates that QSpell 250K effectively evaluates the efficacy of representative spelling correctors. We believe that QSpell 250K will significantly advance spelling correction methodologies. The accompanying data and code will be made publicly available.

QSpell 250K: A Large-Scale, Practical Dataset for Chinese Search Query Spell Correction

Automating planning with LLMs presents transformative opportunities for traditional industries, yet remains underexplored. In commercial construction, the complexity of automated scheduling often requires manual intervention to ensure precision. We propose CONSTRUCTA, a novel framework leveraging LLMs to optimize construction schedules in complex projects like semiconductor fabrication. CONSTRUCTA addresses key challenges by: (1) integrating construction-specific knowledge through static RAG; (2) employing context-sampling techniques inspired by architectural expertise to provide relevant input; and (3) deploying Construction DPO to align schedules with expert preferences using RLHF. Experiments on proprietary data demonstrate performance improvements of +42.3% in missing value prediction, +79.1% in dependency analysis, and +28.9% in automated planning compared to baseline methods, showcasing its potential to revolutionize construction workflows and inspire domain-specific LLM advancements.

CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models

Assuring the data quality of tabular datasets is essential for the efficiency of the diverse tabular downstream tasks (like summarization and fact-checking). Data-wrangling tasks effectively address the challenges associated with structured data processing to improve the quality of tabular data. Traditional statistical methods handle numeric data efficiently but often fail to understand the semantic context of the textual data in tables. Deep learning approaches are resource-intensive, requiring task and dataset-specific training. Addressing these shortcomings, we present an automated system that leverages LLMs to generate executable code for data-wrangling tasks like missing value imputation, error detection, and error correction. Our system aims to identify inherent patterns in the data while leveraging external knowledge, effectively addressing both memory-independent and memory-dependent tasks.

CodeGenWrangler: Data Wrangling task automation using Code-Generating Models

Code explanations are crucial in real-world life, from educating students to aligning technical projects with business goals. However, existing approaches face challenges balancing faithfulness to the original code and personalization for diverse user needs. This paper addresses these challenges by introducing a novel benchmark and method for generating faithful personalized code explanations. Our benchmark, FaithfulPersonaCodeX, incorporates code samples and user profiles, employing various evaluation metrics to evaluate both faithfulness and personalization. We propose DISCO, a new method that uses a self-critique mechanism and two-stage optimization to balance faithfulness and personalization in code explanations, addressing the limitations of current large language model approaches. Our proposed model, DISCO, achieves a notable 3.7% improvement in Pass@5 compared to the strong baseline method, Self-Consistency, while maintaining high personalization with a 61.08% win rate in the LLM-as-a-Judge evaluation, effectively balancing faithfulness and user-specific needs in code explanations.

FaithfulPersona: Balancing Faithfulness and Personalization in Code Explanations through Self-Critique

Although large language models rely on parametric knowledge to achieve exceptional performance across various question-answering tasks, they still face challenges when addressing knowledge-based long-tail questions. Augmented generation techniques, such as chain-of-thought prompting and retrieval augmentation, can effectively enhance the ability of these models to answer long-tail questions. However, improving accuracy through augmented generation often results in significant latency within question-answering systems. This paper addresses the issue of ''when and how to augment the input'' by proposing an adaptive question routing framework. This framework employs a query router to select the most appropriate augmentation path at the right time, thereby enhancing both the accuracy and efficiency of question-answering systems. Extensive comparative experiments on benchmarks such as AmbigNQ, HotpotQA, MMLU-STEM, and PopQA demonstrate that our method surpasses existing approaches in both accuracy and efficiency. Furthermore, this paper introduces two metrics for evaluating adaptive question augmentation methods and presents a new benchmark for adaptive question augmentation, aiming to advance the field.

When and How to Augment Your Input: Question Routing Helps Balance the Accuracy and Efficiency of Large Language Models

In recent years, NLP has made remarkable strides, with language and language-vision models transforming applications across a wide range of domains—including healthcare, education, social sciences, humanities, the arts. Yet, many of these models and the datasets they rely on reflect only a small fraction of the world’s population, which often leads to gaps in performance, biases, and missed opportunities for impact. In this talk, I will draw on insights from over a decade of cross-cultural research to highlight key limitations in current benchmarks and models, from their narrow linguistic and cultural scope to challenges in evaluation and application design. I will share key lessons learned along the way and make the case for cross-cultural NLP—one that more effectively captures the diversity of behaviors, beliefs, and linguistic expressions across different communities—not only as a way to build stronger and more robust systems but also to foster an even more innovative research community.

The Power of Many: How Cross-Cultural NLP Will Drive Innovation and Lead to Better Systems

Although Large Language Models (LLMs) can generate coherent text, they often struggle to recognise user intent behind queries. In contrast, Natural Language Understanding (NLU) models interpret the purpose and key information of user input for responsive interactions. Existing NLU models typically map utterances to a dual-level semantic frame, involving sentence-level intent (SI) and word-level slot (WS) labels. However, real-life conversations primarily consist of multi-turn dialogues, requiring the interpretation of complex and extended exchanges. Researchers encounter challenges in addressing all facets of multi-turn dialogue using a unified NLU model. This paper introduces MIDAS, a novel approach leveraging multi-level intent, domain, and slot knowledge distillation for multi-turn NLU. We construct distinct teachers for SI detection, WS filling, and conversation-level domain (CD) classification, each fine-tuned for specific knowledge. A multi-teacher loss is proposed to facilitate the integration of these teachers, guiding a student model in multi-turn dialogue tasks. Results demonstrate the efficacy of our model in improving multi-turn conversation understanding, showcasing the potential for advancements in NLU through multi-level dialogue knowledge distillation. Our implementation is open-sourced on GitHub (https://github.com/adlnlp/Midas).

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