CLUE is a two‑stage training pipeline that teaches models to discover and use social context cues within a conversation for pragmatic understanding. It combines supervised evidence reasoning with a GRPO reward that credits new, evidence‑supported social inferences while penalizing excessive reasoning effort. Experiments show that CLUE improves weighted F1 scores by 17.3% in‑domain and 9.3% out‑of‑domain, reduces reasoning tokens by over 80%, and aligns its explanations more closely with human‑selected conversational evidence.
arXiv:2607. 19345v1 Announce Type: cross Abstract: Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to long-context settings has emerged as an important frontier.
arXiv:2608. 16627v1 Announce Type: cross Abstract: Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL).
arXiv:2603. 01437v2 Announce Type: replace Abstract: As chain of thought (CoT) has become central to scaling reasoning capabilities in large language models (LLMs), it has also emerged as a promising tool for interpretability, suggesting the opportunity to understand model decisions through verbalized reasoning.
arXiv:2606. 14142v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge.
arXiv:2606. 01584v1 Announce Type: cross Abstract: Conversational tutoring agents have been shown to improve learning engagement and student outcomes, and large language models (LLMs) are increasingly used in these systems to provide scalable, personalized feedback.
arXiv:2509. 14704v3 Announce Type: replace Abstract: Benchmark saturation and training-data contamination increasingly obscure whether reported gains in large language models (LLMs) reflect genuine advances in reasoning or familiarity with recurring patterns in benchmark problems.
The paper introduces ContraTalk, a benchmark that tests whether dialogue models truly use acoustic cues or rely on transcript shortcuts. It formalizes cross‑modal disagreement, creates conflict and consistent QA examples, and proposes an Audio Twin representation to expose acoustic evidence to models. Experiments show that while text‑only LLMs perform well on consistent cases, they falter on conflict cases, and AudioLLMs only partially mitigate this issue.
arXiv:2608.30426v1 Announce Type: new Abstract: Current dialogue systems struggle with dynamic information retrieval, often leading to hallucinations and lower response accuracy. We address this by a...
GRACE is a step‑level benchmark for evaluating the faithfulness of chain‑of‑thought reasoning over context. It provides human annotations for each step in CoT traces from 10 models across 4 datasets, labeling faithfulness, error category, and natural‑language explanations. The benchmark introduces a data‑driven taxonomy that splits errors into GRACE‑Inference (deductive) and GRACE‑Grounding (factual) tracks, each with four categories, and demonstrates that incorporating step‑level faithfulness signals can improve downstream accuracy and reasoning reliability.
The paper introduces a new task called reconciliatory explanation generation, where large language models (LLMs) must produce explanations that make contradictory statements compatible. It proposes reusing natural language inference datasets and new quality metrics for scalable evaluation. Experiments with 13 LLMs show limited success, especially when longer reasoning chains are involved, but reinforcement‑learning training improves performance on two narrative reasoning benchmarks.
arXiv:2609.09628v1 Announce Type: cross Abstract: Inferring speaker relationships from spoken conversations is an important step towards socially aware speech understanding. However, this task remain...
SocialMaze is a new benchmark designed to evaluate large language models on social reasoning tasks that involve deep reasoning, dynamic interaction, and information uncertainty. It comprises six tasks drawn from social deduction games, everyday interactions, and digital communities, and includes automated checks and human validation to ensure data quality. Experiments with twelve LLMs reveal that stronger chain‑of‑thought reasoning improves performance on deeper inference tasks, while uncertainty consistently hurts results; targeted fine‑tuning on curated reasoning traces can markedly enhance structured social‑reasoning abilities.