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Detecting human emotions is a complex process often restricted by predetermined emotion labels, limiting the ability of systems to recognize nuanced or novel emotional expressions. To overcome this constraint, we explore a generative approach that uses large language models (LLMs) context creation and analyze the results with the usage of saliency maps. Instead of relying solely on static sample texts, our method generates dynamic backstories that provide additional context for understanding emotions in text or speech, and these backstories improved our evaluations. This novel framework re-imagines emotion detection as a context-driven process, which in turn enables systems to &quot;see the bigger picture&quot; and make more informed and accurate predictions based on added adjectives, nouns, and punctuation.

NAACL 2025

Exploring Taxonomic Emotion Detection via Saliency Mapping and Large Language Model Context Generation

context-aware nlp

backstory generation

saliency maps

emotion detection

large language models

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
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EMNLP 2022

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Detecting human emotions is a complex process often restricted by predetermined emotion labels, limiting the ability of systems to recognize nuanced or novel emotional expressions. To overcome this constraint, we explore a generative approach that uses large language models (LLMs) context creation and analyze the results with the usage of saliency maps. Instead of relying solely on static sample texts, our method generates dynamic backstories that provide additional context for understanding emotions in text or speech, and these backstories improved our evaluations. This novel framework re-imagines emotion detection as a context-driven process, which in turn enables systems to "see the bigger picture" and make more informed and accurate predictions based on added adjectives, nouns, and punctuation.

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)**

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Stigma has emerged as one of the major obstacles to effectively diagnosing depression, as it prevents users from open conversations about their struggles. This requires advanced questioning skills to carefully probe the presence of specific symptoms in an unobtrusive manner. While recent efforts have been made on depression-diagnosis-oriented dialogue systems, they largely ignore this problem, ultimately hampering their practical utility. To this end, we propose a novel and effective method, UPSD4, developing a series of strategies to promote a sense of unobtrusiveness within the dialogue system and assessing depression disorder by probing symptoms. We experimentally show that UPSD4 demonstrates a significant improvement over current baselines, including unobtrusiveness evaluation of dialogue content and diagnostic accuracy. We believe our work contributes to developing more accessible and user-friendly tools for addressing the widespread need for depression diagnosis.

Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder Diagnosis Dialogue

Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities. While previous works focused on either evaluating over stateless web services (RESTful API), based on a single turn user prompt, or an off-policy dialog trajectory, ToolSandbox includes stateful tool execution, implicit state dependencies between tools, a built-in user simulator supporting on-policy conversational evaluation and a dynamic evaluation strategy for intermediate and final milestones over arbitrary trajectory. We show that open source and proprietary models has a significant performance gap, and complex tasks like State Dependency, Canonicalization and Insufficient Information defined in ToolSandbox are challenging even the most capable SOTA LLMs, providing brand-new insights to tool-use LLM capabilities. Datasets and evaluation scripts of ToolSandbox are released at <placeholder>.

ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities

Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attention, existing academic research predominantly focuses on single-turn RAG, leaving a significant gap in addressing the complexities of multi-turn conversations found in real-world applications. To bridge this gap, we introduce CORAL, a large-scale benchmark designed to assess RAG systems in realistic multi-turn conversational settings. CORAL includes diverse information-seeking conversations automatically derived from Wikipedia and tackles key challenges such as open-domain coverage, knowledge intensity, free-form responses, and topic shifts. It supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. We propose a unified framework to standardize various conversational RAG methods and conduct a comprehensive evaluation of these methods on CORAL, demonstrating substantial opportunities for improving existing approaches.

CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmented Generation

High annotation costs from hiring or crowdsourcing complicate the creation of large, high-quality datasets needed for training reliable text classifiers. Recent research suggests using Large Language Models (LLMs) to automate the annotation process, reducing these costs while maintaining data quality. LLMs have shown promising results in annotating downstream tasks like hate speech detection and political framing. Building on the success in these areas, this study investigates whether LLMs are viable for annotating a complex task of media bias detection and whether a downstream media bias classifier can be trained on such data. We create Annolexical, the first large-scale dataset for media bias classification with over 48k synthetically annotated examples. Our classifier fine-tuned on it surpasses all of the annotator LLMs by 5-9% in Mathew’s Correlation Coefficient (MCC) and performs close to or outperforms the model trained on human-labeled data when evaluated on two media bias benchmark datasets (BABE and BASIL). This study demonstrates how our approach significantly reduces the cost of dataset creation in the media bias domain and, by extension - the development of the classifiers, while our subsequent behavioral stress-testing reveals some of its current limitations and trade-offs.

The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection

Large language models (LLMs) have significantly influenced various industries but suffer from a critical flaw, the potential sensitivity of generating harmful content, which poses severe societal risks. We developed and tested novel attack strategies on popular LLMs to expose their vulnerabilities in generating inappropriate content. These strategies, inspired by psychological phenomena such as the ''Priming Effect'', ''Safe Attention Shift'', and ''Cognitive Dissonance'', effectively attack the models' guarding mechanisms. Our experiments achieved an attack success rate (ASR) of 100% on various open-source models, including Meta's Llama-3.2, Google's Gemma-2, Mistral's Mistral-NeMo, Falcon's Falcon-mamba, Apple's DCLM, Microsoft's Phi3, and Qwen's Qwen2.5, among others. Similarly, for closed-source models such as OpenAI's GPT-4o, Google's Gemini-1.5, and Claude-3.5, we observed an ASR of at least 95% on the AdvBench dataset, which represents the current state-of-the-art. This study underscores the urgent need to reassess the use of generative models in critical applications to mitigate potential adverse societal impacts.

Intrinsic Model Weaknesses: How Priming Attacks Unveil Vulnerabilities in Large Language Models

Word-level alignment in speech-text pre-training has demonstrated significant effectiveness, particularly with models like SPECTRA that enhance cross-modal interactions and understand multi-turn dialog contexts. However, these advancements are constrained by a reliance on word-level annotated data, limiting their broader applicability and failing to fully exploit the vast amount of unannotated data available. This paper introduces an **U**nsupervised **S**peech-text word-level alignment with **D**ynamic, **P**rogramming (USDP), which reduces the dependency on scarce annotated resources. We propose an iterative training method for USDP, inspired by the EM algorithm. This approach uses Dynamic Programming and EM principles to iteratively refine temporal alignment predictions. Initially, corresponding speech segments are identified based on the model’s temporal predictions. A predictor then forecasts text words, and Dynamic Programming is applied to determine the optimal alignment, further refining the model’s predictions. Furthermore, we conduct extensive experiments on six benchmark datasets across four different downstream speech-text tasks, including Emotion Recognition in Conversation (ERC), Multimodal Sentiment Analysis (MSA), Spoken Language Understanding (SLU), and Dialogue State Tracking (DST). The experimental results demonstrate that our method significantly enhances the accuracy of models on these speech-text downstream tasks compared to existing approaches.

Unsupervised Speech-text word-level alignment with Dynamic Programming

Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handling of multimodal data. In response to this gap, we introduce SciAssess, a benchmark specifically designed for the comprehensive evaluation of LLMs in scientific literature analysis. It aims to thoroughly assess the efficacy of LLMs by evaluating their capabilities in Memorization (L1), Comprehension (L2), and Analysis & Reasoning (L3). It encompasses a variety of tasks drawn from diverse scientific fields, including biology, chemistry, material, and medicine. To ensure the reliability of SciAssess, rigorous quality control measures have been implemented, ensuring accuracy, anonymization, and compliance with copyright standards. SciAssess evaluates 11 LLMs, highlighting their strengths and areas for improvement. We hope this evaluation supports the ongoing development of LLM applications in scientific literature analysis. SciAssess and its resources are available at <https://github.com/sci-assess/SciAssess>.

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis

In automated essay scoring (AES), recent efforts have shifted toward cross-prompt settings that score essays on unseen prompts for practical applicability. However, prior methods trained with essay-score pairs of specific prompts pose challenges in obtaining prompt-generalized essay representation. In this work, we propose a grammar-aware cross-prompt trait scoring (GAPS), which internally captures prompt-independent syntactic aspects to learn generic essay representation. We acquire grammatical error-corrected information in essays via the grammar error correction technique and design the AES model to seamlessly integrate such information. By internally referring to both the corrected and the original essays, the model can focus on generic features during training. Empirical experiments validate our method's generalizability, showing remarkable improvements in prompt-independent and grammar-related traits. Furthermore, GAPS achieves notable QWK gains in the most challenging cross-prompt scenario, highlighting its strength in evaluating unseen prompts.

Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring

Embedding-based retrieval (EBR), the mainstream approach in information retrieval (IR), aims to help users obtain relevant information and plays a crucial role in retrieval-augmented generation (RAG) techniques of large language models (LLMs). Numerous methods have been proposed to significantly improve the quality of retrieved content and many generic benchmarks are proposed to evaluate the retrieval abilities of embedding models. However, texts in the medical domain present unique contexts, structures, and language patterns, such as terminology, doctor-patient dialogue, and electronic health records (EHRs). Despite these unique features, specific benchmarks for medical context retrieval are still lacking. In this paper, we propose MedEureka, an enriched benchmark designed to evaluate medical-context retrieval capabilities of embedding models with multi-granularity and multi-data types. MedEureka includes four levels of granularity and six types of medical texts, encompassing 18 datasets, incorporating granularity and data type description to prompt instruction-fine-tuned text embedding models for embedding generation. We also provide the MedEureka Toolkit to support evaluation on the MedEureka test set. Our experiments evaluate state-of-the-art open-source and proprietary embedding models, and fine-tuned classical baselines, providing a detailed performance analysis. This underscores the challenges of using embedding models for medical domain retrieval and the need for further research. Our code and data are released in the repository: <https://github.com/JOHNNY-fans/MedEureka>.

MedEureka: A Medical Domain Benchmark for Multi-Granularity and Multi-Data-Type Embedding-Based Retrieval

Spatial relation hallucinations pose a persistent challenge in large vision-language models (LVLMs), leading to generate incorrect predictions about object positions and spatial configurations within an image. To address this issue, we propose a constraint-aware prompting framework designed to reduce spatial relation hallucinations. Specifically, we introduce two types of constraints: (1) bidirectional constraint, which ensures consistency in pairwise object relations, and (2) transitivity constraint, which enforces relational dependence across multiple objects. By incorporating these constraints, LVLMs can produce more spatially coherent and consistent outputs. We evaluate our method on three widely-used spatial relation datasets, demonstrating performance improvements over existing approaches. Additionally, a systematic analysis of various bidirectional relation analysis choices and transitivity reference selections highlights greater possibilities of our methods in incorporating constraints to mitigate spatial relation hallucinations.

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Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder Diagnosis Dialogue

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