World PulseNow
Newsletter

CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The recent introduction of Confidence-Aware Preference Optimization (CAPO) marks a significant advancement in the field of preference optimization for multilingual preferences. Traditional methods like Direct Preference Optimization (DPO) have been effective in English but often fail to generalize to other languages, leading to suboptimal performance. CAPO addresses this limitation by implementing a dynamic loss scaling mechanism based on the confidence in preference pairs, enhancing robustness against noisy comparisons. Empirical results demonstrate that CAPO outperforms existing baselines by at least 16% in reward accuracy and improves alignment by widening the gap between preferred and dispreferred responses across various languages. This development is crucial as it not only enhances the performance of large language models (LLMs) but also ensures that they can better cater to diverse linguistic contexts, ultimately leading to more accurate and human-like interactions.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended Readings
Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction
arXiv — cs.CL2 days ago
Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction
PositiveArtificial Intelligence
The article presents Thinker, a hierarchical thinking model designed to enhance the reasoning capabilities of large language models (LLMs) through multi-turn interactions. Unlike previous methods that relied on end-to-end reinforcement learning without supervision, Thinker allows for a more structured reasoning process by breaking down complex problems into manageable sub-problems. Each sub-problem is represented in both natural language and logical functions, improving the coherence and rigor of the reasoning process.
Read full article
via arXiv — cs.CL
From Fact to Judgment: Investigating the Impact of Task Framing on LLM Conviction in Dialogue Systems
arXiv — cs.CL2 days ago
From Fact to Judgment: Investigating the Impact of Task Framing on LLM Conviction in Dialogue Systems
NeutralArtificial Intelligence
The article investigates the impact of task framing on the conviction of large language models (LLMs) in dialogue systems. It explores how LLMs assess tasks requiring social judgment, contrasting their performance on factual queries with conversational judgment tasks. The study reveals that reframing a task can significantly alter an LLM's judgment, particularly under conversational pressure, highlighting the complexities of LLM decision-making in social contexts.
Read full article
via arXiv — cs.CL
Expert-Guided Prompting and Retrieval-Augmented Generation for Emergency Medical Service Question Answering
arXiv — cs.CL2 days ago
Expert-Guided Prompting and Retrieval-Augmented Generation for Emergency Medical Service Question Answering
PositiveArtificial Intelligence
Large language models (LLMs) have shown potential in medical question answering but often lack the domain-specific expertise required in emergency medical services (EMS). The study introduces EMSQA, a dataset with 24.3K questions across 10 clinical areas and 4 certification levels, along with knowledge bases containing 40K documents and 2M tokens. It also presents Expert-CoT and ExpertRAG, strategies that enhance performance by integrating clinical context, resulting in improved accuracy and exam pass rates for EMS certification.
Read full article
via arXiv — cs.CL
Can LLMs Detect Their Own Hallucinations?
arXiv — cs.CL2 days ago
Can LLMs Detect Their Own Hallucinations?
PositiveArtificial Intelligence
Large language models (LLMs) are capable of generating fluent responses but can sometimes produce inaccurate information, referred to as hallucinations. A recent study investigates whether these models can recognize their own inaccuracies. The research formulates hallucination detection as a classification task and introduces a framework utilizing Chain-of-Thought (CoT) to extract knowledge from LLM parameters. Experimental results show that GPT-3.5 Turbo with CoT detected 58.2% of its own hallucinations, suggesting that LLMs can identify inaccuracies if they possess sufficient knowledge.
Read full article
via arXiv — cs.CL
PustakAI: Curriculum-Aligned and Interactive Textbooks Using Large Language Models
arXiv — cs.CL2 days ago
PustakAI: Curriculum-Aligned and Interactive Textbooks Using Large Language Models
PositiveArtificial Intelligence
PustakAI is a framework designed to create interactive textbooks aligned with the NCERT curriculum for grades 6 to 8 in India. Utilizing Large Language Models (LLMs), it aims to enhance personalized learning experiences, particularly in areas with limited educational resources. The initiative addresses challenges in adapting LLMs to specific curricular content, ensuring accuracy and pedagogical relevance.
Read full article
via arXiv — cs.CL
LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models
arXiv — cs.CL2 days ago
LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models
PositiveArtificial Intelligence
LaoBench is a newly introduced large-scale benchmark dataset aimed at evaluating large language models (LLMs) in the Lao language. It consists of over 17,000 curated samples that assess knowledge application, foundational education, and bilingual translation among Lao, Chinese, and English. The dataset is designed to enhance the understanding and reasoning capabilities of LLMs in low-resource languages, addressing the current challenges faced by models in mastering Lao.
Read full article
via arXiv — cs.CL
ICL-Router: In-Context Learned Model Representations for LLM Routing
arXiv — cs.LG2 days ago
ICL-Router: In-Context Learned Model Representations for LLM Routing
PositiveArtificial Intelligence
The research paper titled 'ICL-Router: In-Context Learned Model Representations for LLM Routing' presents a novel routing method for large language models (LLMs) that utilizes in-context vectors to enhance model representation. This two-stage method first embeds queries into vectors and then profiles candidate models based on their performance. The approach aims to improve routing performance and allows for the integration of new models without the need for retraining, addressing scalability challenges in LLM applications.
Read full article
via arXiv — cs.LG
Empirical Characterization of Temporal Constraint Processing in LLMs
arXiv — cs.CL2 days ago
Empirical Characterization of Temporal Constraint Processing in LLMs
NeutralArtificial Intelligence
The study titled 'Empirical Characterization of Temporal Constraint Processing in LLMs' investigates how large language models (LLMs) handle temporal constraints in decision-making. It evaluates eight production-scale models with parameter counts ranging from 2.8 to 8 billion. The findings reveal significant risks, including a bimodal performance distribution, high sensitivity to prompt changes, and a 100% false positive rate in some models. Fine-tuning on synthetic examples improved performance by up to 37 percentage points, but the ability to satisfy temporal constraints remains unproven.
Read full article
via arXiv — cs.CL