Investigating Hallucination in Conversations for Low Resource Languages

ProRAC (Progression-based Reasoning about Actions and Change) is a neuro-symbolic framework that utilizes large language models (LLMs) to address reasoning about actions and changes (RAC) problems. The framework extracts essential elements from RAC problems, executes actions progressively to determine the final state, and evaluates queries against this state. Evaluations on various RAC benchmarks indicate that ProRAC demonstrates strong performance across diverse tasks and domains.

The paper presents Group Relative Policy Optimization for Representation Model (GRPO-RM), a reinforcement learning method aimed at fine-tuning large language models (LLMs). It establishes a predefined output set to replace token sequence sampling, facilitating the generation of an output group essential for GRPO's optimization. A specialized reward function is also introduced to cater to representation models, with extensive experiments validating the method's effectiveness across various real-world datasets.

Large Language Models (LLMs) are advanced linguistic tools that can produce outputs that may sound plausible but are often factually incorrect, a phenomenon known as hallucination. This study introduces a mathematical framework to analyze, quantify, and mitigate these hallucinations. It employs probabilistic modeling and Bayesian uncertainty estimation to develop refined metrics and strategies, including contrastive decoding and retrieval-augmented grounding, aimed at enhancing the reliability of LLMs.

MedBench v4 introduces a comprehensive benchmarking framework for evaluating Chinese medical language models, multimodal models, and intelligent agents. This cloud-based infrastructure features over 700,000 expert-curated tasks across various medical specialties. The evaluation process includes multi-stage refinement and clinician reviews, with results indicating that while base LLMs score an average of 54.1/100, safety and ethics ratings remain low at 18.4/100.

Recent advancements in Large Reasoning Models (LRMs) have shown impressive performance in specialized reasoning tasks. However, a systematic evaluation reveals that acquiring deliberative reasoning capabilities significantly reduces foundational capabilities, leading to declines in helpfulness and harmlessness, along with increased inference costs. Adaptive reasoning methods can alleviate these drawbacks, highlighting the need for more versatile LRMs.

Large language models (LLMs) have shown impressive capabilities across various tasks, but their extensive size complicates real-world applications. Traditional pruning methods, like Wanda, require significant manual effort and expert knowledge, leading to high costs. This study introduces AutoPrune, a self-pruning method that allows LLMs to autonomously design optimal pruning algorithms, addressing the challenges of expert dependency and performance degradation due to uniform sparsity.

ConInstruct is a benchmark designed to evaluate Large Language Models (LLMs) on their ability to detect and resolve conflicts in user instructions. While many existing assessments focus on adherence to instructions, ConInstruct addresses the often-overlooked scenarios where conflicting constraints arise. Initial evaluations show that proprietary LLMs generally perform well in conflict detection, with DeepSeek-R1 and Claude-4.5-Sonnet achieving the highest F1-scores.

The paper introduces TASA (Teaching According to Students' Aptitude), a personalized mathematics tutoring framework that utilizes Large Language Models (LLMs) to adapt instruction based on students' evolving knowledge and cognitive retention. TASA integrates a structured student persona and event memory to enhance learning by addressing individual proficiency levels and forgetting patterns, aiming to improve the effectiveness of mathematics education.