AI Frontiers: cs.CL Papers - Oct 18, 2025

Description

In this episode of AI Frontiers, we explore 23 cutting-edge papers from arXiv's Computation and Language (cs.CL) category, all uploaded on October 18, 2025. These papers advance natural language processing (NLP), focusing on large language models (LLMs), ethical reasoning, multi-agent collaborations, and domain-specific adaptations. Key themes include enhancing LLMs for specialized fields like biomedicine and geoscience, multi-agent systems for collaborative reasoning, detecting AI flaws such as hallucinations and misinformation, ethical moral considerations, and efficiency optimizations. Notable findings: Multi-agent debates improve accuracy on reasoning tasks by significant margins, offering transparent explanations. Lightweight methods like steering vectors boost performance in mental health assessments without heavy retraining. Benchmarks reveal biases in moral reasoning, showing that model size doesn't guarantee better ethics. Small language models efficiently analyze vast domain-specific corpora, democratizing knowledge access. Neuro-symbolic approaches create trustworthy agents resistant to attacks. Highlighted papers: Zhyar Rzgar K. Rostam et al.'s systematic review on pre-trained models for domain-specific text classification, revealing how adaptations like BioBERT excel in biomedicine. Zhixuan He et al.'s DiMo framework uses multi-agent debates with diverse thinking modes to enhance LLM reasoning transparency and accuracy. Yu Ying Chiu et al.'s MoReBench evaluates procedural moral reasoning, exposing biases and pushing for pluralistic AI ethics. Future directions point to neuro-symbolic hybrids, real-time personalization, expanded ethical benchmarks, and addressing challenges like biases and computational costs. Takeaways emphasize collaboration, efficiency, trustworthiness, and domain adaptations for real-world AI impact. This synthesis was created using AI tools: content generation with GPT Grok using model Grok-4-0709, TTS synthesis using OpenAI, and image generation using Google tools. 1. Zhyar Rzgar K. Rostam et al. (2025). Advances in Pre-trained Language Models for Domain-Specific Text Classification: A Systematic Review. http://arxiv.org/pdf/2510.17892v1 2. Zhixuan He et al. (2025). Unleashing Diverse Thinking Modes in LLMs through Multi-Agent Collaboration. http://arxiv.org/pdf/2510.16645v1 3. Francisco Jose Cortes Delgado et al. (2025). Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach. http://arxiv.org/pdf/2510.16604v1 4. Muhammad Ammar et al. (2025). AI-Generated Text Detection in Low-Resource Languages: A Case Study on Urdu. http://arxiv.org/pdf/2510.16573v1 5. Richard J. Young et al. (2025). When Models Can't Follow: Testing Instruction Adherence Across 256 LLMs. http://arxiv.org/pdf/2510.18892v1 6. Alkis Koudounas et al. (2025). Hallucination Benchmark for Speech Foundation Models. http://arxiv.org/pdf/2510.16567v1 7. Seungho Cho et al. (2025). Language over Content: Tracing Cultural Understanding in Multilingual Large Language Models. http://arxiv.org/pdf/2510.16565v1 8. Haoxuan Zhang et al. (2025). ReviewGuard: Enhancing Deficient Peer Review Detection via LLM-Driven Data Augmentation. http://arxiv.org/pdf/2510.16549v1 9. Michelle Yuan et al. (2025). Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection. http://arxiv.org/pdf/2510.16499v1 10. Vamshi Krishna Bonagiri et al. (2025). Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safety. http://arxiv.org/pdf/2510.16492v1 11. Pingjun Hong et al. (2025). Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations. http://arxiv.org/pdf/2510.16458v1 12. Deyi Ji et al. (2025). RAVEN: Robust Advertisement Video Violation Temporal Grounding via Reinforcement Reasoning. http://arxiv.org/pdf/2510.16455v1 13. Bin Yu et al. (2025). TrajSelector: Harnessing Latent Representations for Efficient and Effective Best-of-N in Large Reasoning Model. http://arxiv.org/pdf/2510.16449v1 14. Syed Rifat Raiyan et al. (2025). FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution. http://arxiv.org/pdf/2510.16439v2 15. Fu-An Chao et al. (2025). Probing the Hidden Talent of ASR Foundation Models for L2 English Oral Assessment. http://arxiv.org/pdf/2510.16387v1 16. David Peer et al. (2025). ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents. http://arxiv.org/pdf/2510.16381v1 17. Yu Ying Chiu et al. (2025). MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes. http://arxiv.org/pdf/2510.16380v1 Disclaimer: This video uses arXiv.org content under its API Terms of Use; AI Frontiers is not affiliated with or endorsed by arXiv.org.