F1 In CS.CL Papers

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 34 papers for F1 In CS.CL Papers. Dominant protocol signals include automatic metrics, human evaluation, simulation environments, with frequent benchmark focus on Retrieval, DROP and metric focus on f1, accuracy. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Top Papers Reporting This Metric

• A Mixture-of-Experts Model for Multimodal Emotion Recognition in Conversations

  Soumya Dutta, Smruthi Balaji, Sriram Ganapathy · Feb 26, 2026

  Automatic Metrics General

  Experiments on three benchmark datasets-IEMOCAP, MELD, and MOSI-show that our proposal achieves 70.9%, 69.5%, and 87.9% weighted F1-scores respectively, outperforming several baseline speech-text ERC systems.

• SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables

  Sungho Park, Jueun Kim, Wook-Shin Han · Feb 26, 2026

  Automatic Metrics Coding

  Yet existing benchmarks are small, manually curated - and therefore error-prone - and contain shallow questions that seldom demand more than two hops or invoke aggregations, grouping, or other advanced analytical operations expressible in n

• Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features

  Mohammad Yeghaneh Abkenar, Weixing Wang, Manfred Stede, Davide Picca, Mark A. Finlayson · Feb 26, 2026

  Automatic Metrics General

  Argumentation mining comprises several subtasks, among which stance classification focuses on identifying the standpoint expressed in an argumentative text toward a specific target topic.

• Probing for Knowledge Attribution in Large Language Models

  Ivo Brink, Alexander Boer, Dennis Ulmer · Feb 26, 2026

  Automatic Metrics General

  Probes trained on AttriWiki data reveal a strong attribution signal, achieving up to 0.96 Macro-F1 on Llama-3.1-8B, Mistral-7B, and Qwen-7B, transferring to out-of-domain benchmarks (SQuAD, WebQuestions) with 0.94-0.99 Macro-F1 without retr

• A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection

  Mirza Raquib, Asif Pervez Polok, Kedar Nath Biswas, Rahat Uddin Azad, Saydul Akbar Murad · Feb 25, 2026

  Automatic Metrics General

  Evaluation uses multiple metrics, including accuracy, precision, recall, F1-score, Hamming loss, Cohens kappa, and AUC-ROC.

• Distill and Align Decomposition for Enhanced Claim Verification

  Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero · Feb 25, 2026

  Human EvalAutomatic Metrics General

  Across six evaluation settings, our trained 8B decomposer improves downstream verification performance to (71.75%) macro-F1, outperforming prompt-based approaches ((+1.99), (+6.24)) and existing RL methods ((+5.84)).

• Multi-dimensional Assessment and Explainable Feedback for Counselor Responses to Client Resistance in Text-based Counseling with LLMs

  Anqi Li, Ruihan Wang, Zhaoming Chen, Yuqian Chen, Yu Lu · Feb 25, 2026

  Automatic Metrics Medicine

  Although current NLP research has examined overall counseling quality and general therapeutic skills, it fails to provide granular evaluations of high-stakes moments where clients exhibit resistance.

• MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification

  Kazi Samin Yasar Alam, Md Tanbir Chowdhury, Tamim Ahmed, Ajwad Abrar, Md Rafid Haque · Feb 25, 2026

  Human EvalAutomatic Metrics Coding

  We benchmark transformer-based models and evaluate zero-shot large language models under structured prompting.

• Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

  Mohammadreza Ghaffarzadeh-Esfahani, Nahid Yousefian, Ebrahim Heidari-Farsani, Ali Akbar Omidvarian, Sepehr Ghahraei · Feb 24, 2026

  Automatic Metrics MedicineMultilingual

  Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP).

• PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data

  Samah Fodeh, Linhai Ma, Yan Wang, Srivani Talakokkul, Ganesh Puthiaraju · Feb 24, 2026

  Automatic Metrics MedicineCoding

  Patient-generated text such as secure messages, surveys, and interviews contains rich expressions of the patient voice (PV), reflecting communicative behaviors and social determinants of health (SDoH).

• A Benchmark for Deep Information Synthesis

  Debjit Paul, Daniel Murphy, Milan Gritta, Ronald Cardenas, Victor Prokhorov · Feb 24, 2026

  Human EvalAutomatic Metrics Coding

  Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis.

• Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning

  Sanket Badhe, Deep Shah · Feb 24, 2026

  Automatic Metrics Law

  These expressive instructions render the decision-making process transparent, allowing for full human verification of logic, making this approach ideal for regulated industries such as law, finance, and content moderation, as well as high-v

• An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

  Cathy Shyr, Yan Hu, Rory J. Tinker, Thomas A. Cassini, Kevin W. Byram · Feb 23, 2026

  Automatic Metrics Medicine

  Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not operationalize the full clinical workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontolo

• How to Train Your Deep Research Agent? Prompt, Reward, and Policy Optimization in Search-R1

  Yinuo Xu, Shuo Lu, Jianjie Cheng, Meng Wang, Qianlong Xie · Feb 23, 2026

  Automatic Metrics General

  Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation.

• Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins

  Jasmin Han, Janardan Devkota, Joseph Waring, Amanda Luken, Felix Naughton · Feb 23, 2026

  Automatic Metrics General

  Perceived message effectiveness (PME) by potential intervention end-users is important for selecting and optimizing personalized smoking cessation intervention messages for mobile health (mHealth) platform delivery.

• PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification

  Isun Chehreh, Ebrahim Ansari · Feb 22, 2026

  Automatic Metrics General

  Data collection involved 60,000 raw posts from various Persian social media platforms, followed by rigorous preprocessing and hybrid annotation combining ChatGPT-based few-shot prompting with human verification.

• Retrieval Augmented Enhanced Dual Co-Attention Framework for Target Aware Multimodal Bengali Hateful Meme Detection

  Raihan Tanvir, Md. Golam Rabiul Alam · Feb 22, 2026

  Automatic Metrics CodingMultilingual

  Hateful content on social media increasingly appears as multimodal memes that combine images and text to convey harmful narratives.

• Vichara: Appellate Judgment Prediction and Explanation for the Indian Judicial System

  Pavithra PM Nair, Preethu Rose Anish · Feb 20, 2026

  Human EvalAutomatic Metrics Law

  Vichara surpasses existing judgment prediction benchmarks on both datasets, with GPT-4o mini achieving the highest performance (F1: 81.5 on PredEx, 80.3 on ILDC_expert), followed by Llama-3.1-8B.

• Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models

  Wojciech Michaluk, Tymoteusz Urban, Mateusz Kubita, Soveatin Kuntur, Anna Wroblewska · Feb 20, 2026

  Automatic Metrics Coding

  Clickbait headlines degrade the quality of online information and undermine user trust.

• Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset

  Zhuqian Zhou, Kirk Vanacore, Bakhtawar Ahtisham, Jinsook Lee, Doug Pietrzak · Feb 18, 2026

  Automatic Metrics Math

  To address this challenge, we investigate the "numeric ambiguity" problem and introduce MathEd-PII, the first benchmark dataset for PII detection in math tutoring dialogues, created through a human-in-the-loop LLM workflow that audits upstr

• Enhancing Building Semantics Preservation in AI Model Training with Large Language Model Encodings

  Suhyung Jang, Ghang Lee, Jaekun Lee, Hyunjun Lee · Feb 17, 2026

  Automatic Metrics General

  Accurate representation of building semantics, encompassing both generic object types and specific subtypes, is essential for effective AI model training in the architecture, engineering, construction, and operation (AECO) industry.

• Extracting Consumer Insight from Text: A Large Language Model Approach to Emotion and Evaluation Measurement

  Stephan Ludwig, Peter J. Danaher, Xiaohao Yang, Yu-Ting Lin, Ehsan Abedin · Feb 17, 2026

  Automatic Metrics Coding

  Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice.

• A Geometric Analysis of Small-sized Language Model Hallucinations

  Emanuele Ricco, Elia Onofri, Lorenzo Cima, Stefano Cresci, Roberto Di Pietro · Feb 16, 2026

  Automatic Metrics General

  Hallucinations -- fluent but factually incorrect responses -- pose a major challenge to the reliability of language models, especially in multi-step or agentic settings.

• Curriculum Learning and Pseudo-Labeling Improve the Generalization of Multi-Label Arabic Dialect Identification Models

  Ali Mekky, Mohamed El Zeftawy, Lara Hassan, Amr Keleg, Preslav Nakov · Feb 12, 2026

  Automatic Metrics Coding

  Being modeled as a single-label classification task for a long time, recent work has argued that Arabic Dialect Identification (ADI) should be framed as a multi-label classification task.

• Human Values in a Single Sentence: Moral Presence, Hierarchies, and Transformer Ensembles on the Schwartz Continuum

  Víctor Yeste, Paolo Rosso · Jan 20, 2026

  Automatic Metrics Coding

  We study sentence-level detection of the 19 human values in the refined Schwartz continuum in about 74k English sentences from news and political manifestos (ValueEval'24 corpus).

• Event Detection with a Context-Aware Encoder and LoRA for Improved Performance on Long-Tailed Classes

  Abdullah Al Monsur, Nitesh Vamshi Bommisetty, Gene Louis Kim · Jan 17, 2026

  Automatic Metrics Coding

  The current state of event detection research has two notable re-occurring limitations that we investigate in this study.

• CAST: Character-and-Scene Episodic Memory for Agents

  Kexin Ma, Bojun Li, Yuhua Tang, Liting Sun, Ruochun Jin · Jan 14, 2026

  Automatic Metrics General

  Episodic memory is a central component of human memory, which refers to the ability to recall coherent events grounded in who, when, and where.

• WISE: Web Information Satire and Fakeness Evaluation

  Gaurab Chhetri, Subasish Das, Tausif Islam Chowdhury · Dec 30, 2025

  Automatic Metrics General

  This study develops WISE (Web Information Satire and Fakeness Evaluation) framework which benchmarks eight lightweight transformer models alongside two baseline models on a balanced dataset of 20,000 samples from Fakeddit, annotated as eith

• RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA

  Ruiyi Yang, Hao Xue, Imran Razzak, Hakim Hacid, Flora D. Salim · Oct 23, 2025

  Automatic Metrics General

  A Head Agent provides guidance that leads retrieval, while an Iteration Agent selects and expands HSeq via structure-respecting actions (e.g., parent/child hops, table row/column neighbors, KG relations); Finally the head agent composes can

• PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation

  Xiangjun Zai, Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu · Oct 14, 2025

  Automatic Metrics General

  Experiments across multiple domains demonstrate that PRoH achieves state-of-the-art performance, surpassing the prior SOTA model HyperGraphRAG by an average of 19.73% in F1 and 8.41% in Generation Evaluation (G-E) score, while maintaining s

• Peeking inside the Black-Box: Reinforcement Learning for Explainable and Accurate Relation Extraction

  Xinyu Guo, Zhengliang Shi, Minglai Yang, Mahdi Rahimi, Mihai Surdeanu · Oct 7, 2025

  Human EvalAutomatic Metrics General

  Finally, human evaluation shows that our best model generates relational keywords closely aligned with gold labels, increasing human explanation quality ratings by 54% (relative).

• Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models

  Maximilian Kreutner, Marlene Lutz, Markus Strohmaier · Jun 13, 2025

  Automatic MetricsSimulation Env Coding

  Large Language Models (LLMs) display remarkable capabilities to understand or even produce political discourse but have been found to consistently exhibit a progressive left-leaning bias.

• EmoGRACE: Aspect-based emotion analysis for social media data

  Christina Zorenböhmer, Sebastian Schmidt, Bernd Resch · Mar 19, 2025

  Automatic Metrics General

  While sentiment analysis has advanced from sentence to aspect-level, i.e., the identification of concrete terms related to a sentiment, the equivalent field of Aspect-based Emotion Analysis (ABEA) is faced with dataset bottlenecks and the i

• Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes

  Rahul Garg, Trilok Padhi, Hemang Jain, Ugur Kursuncu, Ponnurangam Kumaraguru · Nov 19, 2024

  Automatic MetricsSimulation Env General

  Experimental results from our study on two hate speech benchmark datasets demonstrate superior performance over the state-of-the-art baselines across AU-ROC, F1, and Recall with improvements of 1.1%, 7%, and 35%, respectively.

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