Large Language Models

Granite Guardian: Open-source risk detection models for LLMs, surpassing existing models in accuracy and offering comprehensive coverage across multiple risk dimensions, promoting safer AI.

Contextualized AI counterspeech significantly outperforms generic methods by adapting to the moderation context and user, improving persuasiveness without sacrificing other qualities.

LLMs are trained to reason using language, but COCONUT lets them reason directly in a continuous latent space, boosting performance on logical tasks requiring complex planning.

Moxin-LLM: A fully open-source 7B parameter LLM achieving superior zero-shot performance, promoting transparency and reproducibility in AI research.

LG AI Research unveils EXAONE 3.5, a series of instruction-tuned language models (2.4B, 7.8B, and 32B parameters) excelling in real-world tasks, long-context understanding, and general benchmarks.

CodeArena, a novel benchmark, evaluates code LLMs based on human preferences, revealing performance gaps between open-source and proprietary models, and a large-scale synthetic instruction corpus impr…

MONET improves Transformer interpretability by using Mixture-of-Experts (MoE) with 262K monosemantic experts per layer, achieving parameter efficiency and enabling knowledge manipulation without perfo…

Marco-LLM: A groundbreaking multilingual LLM significantly enhances cross-lingual capabilities via massive multilingual training, bridging the performance gap between high- and low-resource languages.

LLMs’ training quality is exponentially improving, enabling models with half the parameters to match state-of-the-art performance every 3 months, thus reducing inference costs.

WRPO: Implicitly fuse LLMs, boosting performance without complex alignment or merging!

Researchers developed a robust multi-bit text watermarking method using LLMs for paraphrasing, achieving over 99.99% detection accuracy while maintaining semantic information and resisting common atta…

AGORABENCH: A new benchmark reveals surprising strengths & weaknesses of LMs as synthetic data generators, showing that problem-solving ability isn’t the sole indicator of data quality.

Imperfect OCR hinders Retrieval-Augmented Generation (RAG). OHRBench, a new benchmark, reveals this cascading impact, showing current OCR solutions insufficient for high-quality RAG knowledge bases. …

Train high-performing Process Reward Models (PRMs) cheaply using only outcome-level labels, eliminating the need for costly step-by-step annotations!

O1-CODER replicates OpenAI’s o1 model for coding, integrating reinforcement learning and Monte Carlo Tree Search to enhance System-2 thinking and generate high-quality code with reasoning steps.

KV Shifting Attention: A novel attention mechanism significantly enhances language modeling by simplifying induction heads, leading to improved performance and faster convergence, even in large-scale …

New multilingual LLM benchmark, INCLUDE, tackles regional knowledge gaps by using 197K QA pairs from 44 languages, improving cross-lingual evaluation.

Boosting LLMs’ reasoning: A novel token-level contrastive estimation method automatically identifies and penalizes critical tokens leading to errors, significantly enhancing reasoning accuracy.

Boost LLM accuracy exponentially by using a two-stage algorithm with provable scaling laws: generate multiple candidate solutions then compare them in a knockout tournament!

Dynamic parallel processing boosts LLM inference speed on hybrid CPUs by over 90% memory bandwidth, resolving performance bottlenecks caused by imbalanced hardware capabilities.