Austria

We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs). We investigate the impact of injecting diverse persona descriptions into LLM prompts across two studies, exploring whether personas increase annotation diversity and whether the impacts of individual personas on the resulting annotations are consistent and controllable. Our results indicate that persona-prompted LLMs generate more diverse annotations than LLMs prompted without personas, and that the effects of personas on LLM annotations align with subjective differences in human annotations. These effects are both controllable and repeatable, making our approach a valuable tool for enhancing data annotation in subjective NLP tasks such as toxicity detection.

ACL 2025

Personas with Attitudes: Controlling LLMs for Diverse Data Annotation

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Long-context understanding is crucial for large language models (LLMs) and has become a fundamental capability for most LLMs. However, beyond the focus on "input-long", the ability to "output-long" is equally significant, yet it remains underexplored. To address this limitation, we propose a simple, efficient, and plug-in approach, Position ID Compression (PIC), to unlock the long-form text generation potential of LLMs. The idea is straightforward: by compressing the position ids of the context, we provoke and guide LLMs to generate coherent and longer output. Specifically, we find that directly reducing the position ids by a fixed ratio significantly impacts the generation quality. To mitigate this, we propose two variants of PIC: NTK-aware PIC and Dynamic PIC. Without additional training, both methods enable LLMs to extend their generation length by approximately 1.5 times without compromising generation quality. Furthermore, by integrating supervised fine-tuning (SFT) with PIC, we propose PIC-SFT, which further improves LLMs' long-form text generation capabilities, achieving top performance on HelloBench and LongBench-Write. Extensive experiments demonstrate the effectiveness of our approach.

PIC: Unlocking Long-Form Text Generation Capabilities of Large Language Models via Position ID Compression

RAG systems rely on rerankers to identify relevant documents. However, fine-tuning these models remains challenging due to the scarcity of annotated query-document pairs. Existing distillation-based approaches suffer from training-inference misalignment and fail to capture interdependencies among candidate documents. To overcome these limitations, we reframe the reranking process as an attention-mask problem and propose Gumbel Reranking, an end-to-end training framework for rerankers aimed at minimizing the training-inference gap. In our approach, reranker optimization is reformulated as learning a stochastic, document-wise Top-$k$ attention mask using the Gumbel Trick and Relaxed Top-$k$ Sampling. This formulation enables end-to-end optimization by minimizing the overall language loss. Experiments across various settings consistently demonstrate performance gains, including a 10.4% improvement in recall on HotpotQA for distinguishing indirectly relevant documents.

Gumbel Reranking: Differentiable End-to-End Reranker Optimization

In this work, we introduce MultiReflect, a novel multimodal self-reflective Retrieval Augmented Generation (RAG)-based automated fact-checking pipeline. MultiReflect is designed to address the challenges of rapidly outdated information, limitations in human query capabilities, and expert knowledge barriers in fact-checking. Our proposed pipeline leverages the latest advancements in Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to enhance fact verification across text and images. Specifically, by integrating multimodal data processing with RAG-based evidence reflection, our system improves the accuracy of fact-checking by utilizing internet-sourced verification. We evaluate our results on the VERITE benchmarks and using several multimodal LLMs, outperforming baselines in binary classification.

MultiReflect: Multimodal Self-Reflective RAG-based Automated Fact-Checking

Retrieval-augmented generation (RAG) is a powerful paradigm for leveraging external data to enhance the capabilities of large language models (LLMs). However, most existing RAG solutions are tailored for single-modality or limited multimodal scenarios, restricting their applicability in real-world contexts where diverse data sources—including text, tables, images, and videos—must be integrated seamlessly. In this work proposes a unified \textit{Multimodal Retrieval-augmented generation (mRAG)} system designed to unify information processing across all four modalities. Our pipeline ingests and indexes data from PDFs and videos using tools like Amazon Textract, Transcribe, Langfuse, and multimodal LLMs (e.g., Claude 3.5 Sonnet) for structured extraction and semantic enrichment. The dataset includes text queries, table lookups, image-based questions, and videos. Evaluation with the Deepeval framework shows improved retrieval accuracy and response quality, especially for structured text and tables. While performance on image and video queries is lower, the multimodal integration framework remains robust, underscoring the value of unified pipelines for diverse data.

Multimodal Retrieval-Augmented Generation: Unified Information Processing Across Text, Image, Table, and Video Modalities

As large language models (LLMs) become increasingly prevalent, ensuring their robustness against adversarial misuse is crucial. This paper introduces the GAP (Graph of Attacks with Pruning) framework, an advanced approach for generating stealthy jailbreak prompts to evaluate and enhance LLM safeguards. GAP addresses limitations in existing tree-based methods by implementing an interconnected graph structure that enables knowledge sharing across attack paths. Our experimental evaluation demonstrates GAP's superiority over existing techniques, achieving a 20.8% increase in attack success rates while reducing query costs by 62.7%. GAP consistently outperforms state-of-the-art methods across various open and closed LLMs, with attack success rates of 
96%. Additionally, we present specialized variants like GAP-Auto for automated seed generation and GAP-VLM for multimodal attacks. GAP-generated prompts prove highly effective in improving content moderation systems, increasing true positive detection rates by 108.5% and accuracy by 183.6% when used for fine-tuning.

Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt. Generation for Enhanced LLM Content Moderation

Detecting harmful content is a crucial task in the landscape of NLP applications for Social Good, with hate speech being one of its most dangerous forms. But what do we mean by hate speech, how can we define it and how does prompting different definitions of hate speech affect model performance? The contribution of this work is twofold. At the theoretical level, we address the ambiguity surrounding hate speech by collecting and analyzing existing definitions from the literature. We organize these definitions into a taxonomy of 14 conceptual elements—building blocks that capture different aspects of hate speech definitions, such as references to the target of hate. At the experimental level, we employ the collection of definitions in a systematic zero-shot evaluation of three LLMs, on three hate speech datasets representing different types of data (synthetic, human-in-the-loop, and real-world). We find that choosing different definitions, i.e., definitions with a different degree of specificity in terms of encoded elements, impacts model performance, but this effect is not consistent across all architectures.

A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance

Ensuring the safe deployment of AI systems is critical in industry settings where biased outputs can lead to significant operational, reputational, and regulatory risks. Thorough evaluation before deployment is essential to prevent these hazards. Red-teaming addresses this need by employing adversarial attacks to develop guardrails that detect and reject biased or harmful queries, enabling models to be retrained or steered away from harmful outputs. However, red-teaming techniques are often limited, and malicious actors may discover new vulnerabilities that bypass safety fine-tuning, underscoring the need for ongoing research and innovative approaches. Notably, most red-teaming efforts focus on harmful or unethical instructions rather than addressing social bias, leaving this critical area under-explored despite its significant real-world impact, especially in customer-facing AI systems. We propose two bias-specific red-teaming methods, Emotional Bias Probe (EBP) and BiasKG, to evaluate how standard safety measures for harmful content mitigate bias. For BiasKG, we refactor natural language stereotypes into a knowledge graph. and use adversarial attacking strategies to induce biased responses from several open- and closed-source language models. We find our method increases bias in all models, even those trained with safety guardrails. Our work emphasizes uncovering societal bias in LLMs through rigorous evaluation, addressing adversarial challenges to ensure AI safety in high-stakes industry deployments.

Red-Teaming for Uncovering Societal Bias in Large Language Models

Annotator disagreement poses a significant challenge in subjective tasks like hate speech detection. In this paper, we introduce a novel variant of the HateWiC task that explicitly models annotator agreement by estimating the proportion of annotators who classify the meaning of a term as hateful. To tackle this challenge, we explore the use of Llama 3 models fine-tuned through Direct Preference Optimization (DPO). Our experiments show that while LLMs perform well for majority-based hate classification, they struggle with the more complex agreement-aware task. DPO fine-tuning offers improvements, particularly when applied to instruction-tuned models. Yet, our results emphasize the need for improved modeling of subjectivity in hate classification and this study can serve as foundation for future advancements.

Using LLMs and Preference Optimization for Agreement-Aware HateWiC Classification

Language changes over time, including in the hate speech domain, which evolves quickly following social dynamics and cultural shifts. While NLP research has investigated the impact of language evolution on model training and has proposed several solutions for it, its impact on model benchmarking remains under-explored. Yet, hate speech benchmarks play a crucial role to ensure model safety. In this paper, we empirically evaluate the robustness of 20 language models across two evolving hate speech experiments, and we show the temporal misalignment between static and time-sensitive evaluations. Our findings call for time-sensitive linguistic benchmarks in order to correctly and reliably evaluate language models in the hate speech domain.

Hatevolution: What Static Benchmarks Don't Tell Us

Large language model training follows a standard pipeline: tokenization, pretraining, possibly mid-training, and post training or alignment. Despite its wild success, we understand relatively little about this recipe and are almost certainly missing many opportunities to improve it. In this talk, I will focus on three such cases. I’ll describe our work on data efficient post training (e.g. LIMA, ALMA, and s1) where we argue that nearly all advanced model capabilities ultimately come from the pretraining data, even if effective alignment is still essential for controlling model behavior. I will also describe new methods for extracting more signal from the pretraining data, including new hierarchical architectures for byte-level language models (e.g. BLT) that are both tokenizer-free and scale better than traditional BPE-based methods, especially in the long tail. Finally, I will discuss decentralized, modular training algorithms (e.g. BTM) that better isolate and control the influence of specific data on specific model components and behaviors. Together, these methods promise to simplify training and improve scaling, by centering and amplifying the influence of data in architecture design.

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