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Numerous recent techniques for text style transfer characterize their approaches as variants of reinforcement learning and preference optimization. In this work, we consider the relationship between these approaches and a class of optimization approaches developed primarily for (non-neural) statistical machine translation, formerly known as &#39;tuning&#39;. Inspired by these techniques from the past, we improve upon established preference optimization approaches, incorporating multiple iterations of exploration and optimization, and choosing contrastive examples by following a &#39;hope&#39; vs &#39;fear&#39; sampling strategy. Cognizant of the difference between machine translation and style transfer, however, we further tailor our framework with a new pseudo-parallel data generation method and a dynamic weighted reward aggregation method to tackle the lack of parallel data and the need for a multi-objective reward. We evaluate our model on two commonly used text style transfer datasets. Through automatic and human evaluation results we show the effectiveness and the superiority of our model compared to state-of-the-art baselines.

NAACL 2025

Style Transfer with Multi-iteration Preference Optimization

preference optimization

alignment

style transfer

machine translation

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Numerous recent techniques for text style transfer characterize their approaches as variants of reinforcement learning and preference optimization. In this work, we consider the relationship between these approaches and a class of optimization approaches developed primarily for (non-neural) statistical machine translation, formerly known as 'tuning'. Inspired by these techniques from the past, we improve upon established preference optimization approaches, incorporating multiple iterations of exploration and optimization, and choosing contrastive examples by following a 'hope' vs 'fear' sampling strategy. Cognizant of the difference between machine translation and style transfer, however, we further tailor our framework with a new pseudo-parallel data generation method and a dynamic weighted reward aggregation method to tackle the lack of parallel data and the need for a multi-objective reward. We evaluate our model on two commonly used text style transfer datasets. Through automatic and human evaluation results we show the effectiveness and the superiority of our model compared to state-of-the-art baselines.

technical paper

### 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. 
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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Dialect adapters that improve the performance of LLMs for NLU tasks on certain sociolects/dialects/national varieties ('dialects' for the sake of brevity) have been reported for encoder models. In this paper, we extend the idea of dialect adapters to decoder models in our architecture called LoRDD. Using MD-3, a publicly available dataset of word game-playing conversations between dialectal speakers, our task is Target Word Prediction (TWP) from a masked conversation. LoRDD combines task adapters and dialect adapters where the latter employ contrastive learning on pseudo-parallel conversations from MD-3. Our experiments on Indian English and Nigerian English conversations with two models (Mistral and Gemma) demonstrate that LoRDD outperforms four baselines on TWP. Additionally, it significantly reduces the performance gap with American English, narrowing it to 12% and 5.8% for word similarity, and 25% and 4.5% for accuracy, respectively. The focused contribution of LoRDD is in its promise for dialect adaptation of decoder models using TWP, a simplified version of the commonly used next-word prediction task.

Predicting the Target Word of Game-playing Conversations using a Low-Rank Dialect Adapter for Decoder Models

Large language models (LLMs) hold revolutionary potential to digitize and enhance the Health & Public Services (H&PS) industry. Despite their advanced linguistic abilities, concerns about accuracy, stability, and traceability still persist, especially in high-stakes areas such as transportation systems. Moreover, the predominance of English in LLM development raises questions about how they perform in non-English contexts. This study originated from a real world industrial GenAI application, introduces a novel cross-lingual benchmark dataset comprising nearly 99,869 real traffic incident records from Vienna (2013-2023) to assess the robustness of state-of-the-art LLMs (>= 9) in the spatio vs temporal domain for traffic incident classification. We then explored three hypotheses — sentence indexing, date-to-text conversion, and German-to-English translation — and incorporated Retrieval Augmented Generation (RAG) to further examine the LLM hallucinations in both spatial and temporal domain. Our experiments reveal significant performance disparities in the spatio-temporal domain and demonstrate what types of hallucinations that RAG can mitigate and how it achieves this. We also provide open access to our H&PS traffic incident dataset, with the project demo and code available at Website <https://sites.google.com/view/llmhallucination/home>

How LLMs React to Industrial Spatio-Temporal Data? Assessing Hallucination with a Novel Traffic Incident Benchmark Dataset

This study addresses two key challenges in structuring radiology reports: the lack of a practical structuring schema and datasets to evaluate model generalizability. To address these challenges, we propose a "Finding-Centric Structuring,'' which organizes reports around individual findings, facilitating secondary use. We also construct JRadFCS, a large-scale dataset with annotated named entities (NEs) and relations, comprising 8,428 Japanese Computed Tomography (CT) reports from seven facilities, providing a comprehensive resource for evaluating model generalizability. Our experiments reveal performance gaps when applying models trained on single-facility reports to those from other facilities. We further analyze factors contributing to these gaps and demonstrate that augmenting the training set based on these performance-correlated factors can efficiently enhance model generalizability.

Finding-Centric Structuring of Japanese Radiology Reports and Analysis of Performance Gaps for Multiple Facilities

Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning. However, analyzing live dialogues in real-time necessitates low-latency processing systems, making it impractical to deploy models with billions of parameters due to latency constraints. As a result, practitioners often prefer smaller models with millions of parameters, trained on high-quality, human-annotated datasets. Yet, curating such datasets is both time-consuming and costly. Consequently, there is a growing need to combine the scalability of LLM-generated labels with the precision of human annotations, enabling fine-tuned smaller models to achieve both higher speed and accuracy comparable to larger models. In this paper, we introduce a simple yet effective framework to address this challenge. Our approach is specifically designed for per-utterance classification problems, which encompass tasks such as intent detection, dialogue state tracking, and more. To mitigate the impact of labeling errors from LLMs -- the primary source of inaccuracies in student models -- we propose a noise-reduced preference learning loss. Experimental results demonstrate that our method significantly improves accuracy across utterance-level dialogue tasks, including sentiment detection (over 2%), dialogue act classification (over 1.5%), etc.

Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings

Designing effective debt collection systems is crucial for improving operational efficiency and reducing costs in the financial industry. However, the challenges of maintaining script diversity, contextual relevance, and coherence make this task particularly difficult. This paper presents a debt collection system based on real debtor-collector data from a major commercial bank. We construct a script library from real-world debt collection conversations, and propose a two-stage retrieval based response system for contextual relevance. Experimental results show that our system improves script diversity, enhances response relevance, and achieves practical deployment efficiency through knowledge distillation. This work offers a scalable and automated solution, providing valuable insights for advancing debt collection practices in real-world applications.

A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge

Universal query embeddings that accurately capture the semantic meaning of search queries are crucial for supporting a range of query understanding (QU) tasks within enterprises.
However, current embedding approaches often struggle to effectively represent queries due to the shortness of search queries and their tendency for surface-level variations.


We propose a user-behavior-driven contrastive learning approach which directly aligns embeddings according to user intent.
This approach uses intent-aligned query pairs as positive examples, derived from two types of real-world user interactions: (1) clickthrough data, in which queries leading to clicks on the same URLs are assumed to share the same intent, and (2) session data, in which queries within the same user session are considered to share intent.
By incorporating these query pairs into a robust contrastive learning framework, we can construct query embedding models that align with user intent while minimizing reliance on surface-level lexical similarities.


Evaluations on real-world QU tasks demonstrated that these models substantially outperformed state-of-the-art text embedding models such as mE5 and SimCSE.
Our models have been deployed in our search engine to support QU technologies.

Search Query Embeddings via User-behavior-driven Contrastive Learning

Context:
Despite the impressive capabilities of Large Language Models (LLMs), they pose significant risks in many domains and therefore require guardrails throughout the lifecycle.


Problem:
Many such guardrails are trained as classifiers with domain-specific human text datasets obtained from sources such as social media and they achieve reasonable performance against closed-domain benchmarks. When deployed in the real world, however, the guardrails have to deal with machine text in an open domain, and their performance deteriorates drastically, rendering them almost unusable due to a high level of false refusal.


Solution:
In this paper, using a self-harm detector as an example, we demonstrate the specific challenges facing guardrail deployment due to the data drift between training and production environments. More specifically, we formed two hypotheses about the potential causes, i.e. closed vs. open domain, human vs. LLM-generated text, and conducted five experiments to explore various potential remedies, including their respective advantages and disadvantages.


Evaluation:
While focusing on one example, our experience and knowledge of LLM guardrails give us great confidence that our work contributes to a more thorough understanding of guardrail deployment and can be generalized as a methodology to build more robust domain-specific guardrails in real-world applications.

Challenges and Remedies of Domain-Specific Classifiers as LLM Guardrails: Self-Harm as a Case Study

Manual assignment of Anatomical Therapeutic Chemical (ATC) codes to prescription records is a significant bottleneck in healthcare research and operations at Ontario Health and InterRAI Canada, requiring extensive expert time and effort. To automate this process while maintaining data privacy, we develop a practical approach using locally deployable large language models (LLMs). Inspired by recent advances in automatic International Classification of Diseases (ICD) coding, our method frames ATC coding as a hierarchical information extraction task, guiding LLMs through the ATC ontology level-by-level to reduce complexity. We evaluate our approach using GPT-4 as a performance ceiling and focus development on open-source Llama models suitable for privacy-sensitive deployment. Testing across Health Canada drug product data, RABBITS benchmark, and real clinical notes from Ontario Health, our method achieves 78% exact match accuracy with GPT-4 and 60% with Llama 3.1 70B on Health Canada product names, with similar performance on actual clinical prescriptions. We investigate knowledge grounding strategies by augmenting LLM prompts with drug definitions, finding modest improvements in accuracy. Further, we demonstrate that fine-tuned Llama 3.1 8B achieves performance comparable to zero-shot Llama 3.1 70B, suggesting that effective ATC coding is feasible with limited computational resources. Our results establish the viability of automated ATC coding in privacy-sensitive healthcare environments while highlighting practical considerations for deployment. This work provides Ontario Health and InterRAI Canada with a foundation for automating ATC coding processes in healthcare settings, without compromising data privacy or security.

Zero-Shot ATC Coding with Large Language Models for Clinical Assessments

Large Language Models (LLMs) have impacted the writing process, enhancing productivity by collaborating with humans in content creation platforms. However, generating high-quality, user-aligned text to satisfy real-world content creation needs remains challenging. We propose WritingPath, a framework that uses explicit outlines to guide LLMs in generating goal-oriented, high-quality text. Our approach draws inspiration from structured writing planning and reasoning paths, focusing on reflecting user intentions throughout the writing process. To validate our approach in real-world scenarios, we construct a diverse dataset from unstructured blog posts to benchmark writing performance and introduce a comprehensive evaluation framework assessing the quality of outlines and generated texts. Our evaluations with various LLMs demonstrate that the WritingPath approach significantly enhances text quality according to evaluations by both LLMs and professional writers.

Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models

Large Language Models (LLMs) have achieved significant success in open-domain question answering. However, they continue to face challenges such as hallucinations and knowledge cutoffs. These issues can be mitigated through in-context learning by providing LLMs with relevant context before generating answers. Recent literature proposes Knowledge Graph Prompting (KGP) which integrates knowledge graphs with an LLM-based traversal agent to substantially enhance document retrieval quality. 
However, KGP requires costly fine-tuning with large datasets and remains prone to hallucination. 
In this paper, we propose CuriousLLM, an enhancement that integrates a curiosity-driven reasoning mechanism into an LLM agent. This mechanism enables the agent to generate relevant follow-up questions, thereby guiding the information retrieval process more efficiently.
Central to our approach is the development of the new Follow-upQA dataset, which includes questions and supporting evidence as input, with follow-up questions serving as ground truths. 
These follow-up questions either inquire about what is still missing to fully answer the user's query or use special tokens to signify that the retrieved evidence is sufficient. 
Our experiments show that CuriousLLM significantly boosts LLM performance in multi-document question answering (MD-QA), circumventing the substantial computational costs and latency from the original KGP framework.

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