Natural Language Processing

JuStRank: LLM system ranker benchmark reveals critical judge qualities (decisiveness, bias) impacting ranking accuracy, highlighting instance-level performance doesn’t guarantee accurate system-level…

Fine-tuning small language models? Tweak the learning rate and batch size for a reasoning boost!

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…

DEMO benchmark revolutionizes dialogue modeling by focusing on fine-grained elements (Prelude, Interlocution, Epilogue), enabling comprehensive evaluation and superior agent performance.

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. …

Few-shot learning empowers cross-lingual audio abuse detection using pre-trained models, achieving high accuracy in low-resource Indian languages.

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.

Researchers developed a multilingual news topic classifier using a teacher-student framework and GPT-40 for automatic data annotation, achieving high performance without manual annotation.