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cs.CL updates on arXiv.org

Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents A Triadic Suffix Tokenization Scheme for Numerical Reasoning Hidden Measurement Error in LLM Pipelines Distorts Annotation, Evaluation, and Benchmarking Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method CocoaBench: Evaluating Unified Digital Agents in the Wild MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis Evaluating Memory Capability in Continuous Lifelog Scenario How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts Efficient Training for Cross-lingual Speech Language Models Shared Emotion Geometry Across Small Language Models: A Cross-Architecture Study of Representation, Behavior, and Methodological Confounds A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities Uncertainty-Aware Web-Conditioned Scientific Fact-Checking When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies When Verification Fails: How Compositionally Infeasible Claims Escape Rejection Mem$^2$Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation AOP-Smart: A RAG-Enhanced Large Language Model Framework for Adverse Outcome Pathway Analysis OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation Advancing Polish Language Modeling through Tokenizer Optimization in the Bielik v3 7B and 11B Series TInR: Exploring Tool-Internalized Reasoning in Large Language Models Do BERT Embeddings Encode Narrative Dimensions? A Token-Level Probing Analysis of Time, Space, Causality, and Character in Fiction Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation BlasBench: An Open Benchmark for Irish Speech Recognition Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models Learning and Enforcing Context-Sensitive Control for LLMs HeceTokenizer: A Syllable-Based Tokenization Approach for Turkish Retrieval Efficient Process Reward Modeling via Contrastive Mutual Information Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment Bridging Linguistic Gaps: Cross-Lingual Mapping in Pre-Training and Dataset for Enhanced Multilingual LLM Performance Knowing What to Stress: A Discourse-Conditioned Text-to-Speech Benchmark Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models LLMs Should Incorporate Explicit Mechanisms for Human Empathy ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization Structure-Grounded Knowledge Retrieval via Code Dependencies for Multi-Step Data Reasoning From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation Dynamic Adaptive Attention and Supervised Contrastive Learning: A Novel Hybrid Framework for Text Sentiment Classification EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning Instruction Data Selection via Answer Divergence CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning Turing or Cantor: That is the Question LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset NameBERT: Scaling Name-Based Nationality Classification with LLM-Augmented Open Academic Data BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection A Structured Clustering Approach for Inducing Media Narratives Adaptive Multi-Expert Reasoning via Difficulty-Aware Routing and Uncertainty-Guided Aggregation Comparative Analysis of Large Language Models in Healthcare CodeComp: Structural KV Cache Compression for Agentic Coding Relational Probing: LM-to-Graph Adaptation for Financial Prediction FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification Nationality encoding in language model hidden states: Probing culturally differentiated representations in persona-conditioned academic text Think in Sentences: Explicit Sentence Boundaries Enhance Language Model's Capabilities Training-Free Cross-Lingual Dysarthria Severity Assessment via Phonological Subspace Analysis in Self-Supervised Speech Representations CircuitSynth: Reliable Synthetic Data Generation Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models Mirroring Minds: Asymmetric Linguistic Accommodation and Diagnostic Identity in ADHD and Autism Reddit Communities Computational Implementation of a Model of Category-Theoretic Metaphor Comprehension CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models Weird Generalization is Weirdly Brittle Human vs. Machine Deception: Distinguishing AI-Generated and Human-Written Fake News Using Ensemble Learning Should We be Pedantic About Reasoning Errors in Machine Translation? Simulating Organized Group Behavior: New Framework, Benchmark, and Analysis Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling Claim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering GIANTS: Generative Insight Anticipation from Scientific Literature Many-Tier Instruction Hierarchy in LLM Agents Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition Regime-Conditional Retrieval: Theory and a Transferable Router for Two-Hop QA ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering Revisiting the Capacity Gap in Chain-of-Thought Distillation from a Practical Perspective Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs $p1$: Better Prompt Optimization with Fewer Prompts Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition Skip-Connected Policy Optimization for Implicit Advantage PRAGMA: Revolut Foundation Model Linear Representations of Hierarchical Concepts in Language Models Generating High Quality Synthetic Data for Dutch Medical Conversations HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation Toward Generalized Cross-Lingual Hateful Language Detection with Web-Scale Data and Ensemble LLM Annotations Self-Calibrating Language Models via Test-Time Discriminative Distillation Re-Mask and Redirect: Exploiting Denoising Irreversibility in Diffusion Language Models H-AdminSim: A Multi-Agent Simulator for Realistic Hospital Administrative Workflows with FHIR Integration Reasoning Models Will Sometimes Lie About Their Reasoning
SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis
Huzaifa Pardawala, Siddhant Sukhani, Agam Shah, Veer Kejriwal, A · 2024-10-28 · via cs.CL updates on arXiv.org

Fact-checking is extensively studied in the context of misinformation and disinformation, addressing objective inaccuracies. However, a softer form of misinformation involves responses that are factually correct but lack certain features such as clarity and relevance. This challenge is prevalent in formal Question-Answer (QA) settings such as press conferences in finance, politics, sports, and other domains, where subjective answers can obscure transparency. Despite this, there is a lack of manually annotated datasets for subjective features across multiple dimensions. To address this gap, we introduce SubjECTive-QA, a human annotated dataset on Earnings Call Transcripts' (ECTs) QA sessions as the answers given by company representatives are often open to subjective interpretations and scrutiny. The dataset includes 49,446 annotations for long-form QA pairs across six features: Assertive, Cautious, Optimistic, Specific, Clear, and Relevant. These features are carefully selected to encompass the key attributes that reflect the tone of the answers provided during QA sessions across different domain. Our findings are that the best-performing Pre-trained Language Model (PLM), RoBERTa-base, has similar weighted F1 scores to Llama-3-70b-Chat on features with lower subjectivity, such as Relevant and Clear, with a mean difference of 2.17% in their weighted F1 scores. The models perform significantly better on features with higher subjectivity, such as Specific and Assertive, with a mean difference of 10.01% in their weighted F1 scores. Furthermore, testing SubjECTive-QA's generalizability using QAs from White House Press Briefings and Gaggles yields an average weighted F1 score of 65.97% using our best models for each feature, demonstrating broader applicability beyond the financial domain. SubjECTive-QA is publicly available under the CC BY 4.0 license