Natural Language Processing

Contrary to popular belief, longer reasoning chains don’t always boost Large Language Model (LLM) accuracy; this research reveals that parallel scaling with shorter solutions outperforms sequential sc…

PhysReason benchmark evaluates physics-based reasoning in LLMs, revealing critical limitations and guiding future improvements.

Large language models struggle with mathematical word problems, demonstrating flaws in reasoning despite achieving high accuracy; a new study highlights these persistent gaps in generalization abiliti…

LLMs’ performance on language complexity tasks (LIX & ADD) reveals a strong correlation with general capabilities, suggesting complexity metrics as noisy zero-shot proxies for model evaluation.

RDLM: A novel continuous diffusion model for language modeling leverages the geometry of categorical distributions, outperforming existing discrete approaches and approaching autoregressive model perf…

PoPilot, a novel proof-oriented programming LLM, outperforms GPT-40 by 64% under data scarcity by using synthetic data augmentation.

Atom of Thoughts (AOT) revolutionizes LLM test-time scaling by decomposing complex reasoning into independent sub-questions, drastically reducing computation while maintaining high accuracy.

TalkHier, a novel framework for LLM multi-agent systems, uses structured communication and hierarchical refinement to achieve state-of-the-art performance on various tasks, improving collaboration and…

NSA: a novel sparse attention mechanism achieves efficient long-context modeling by combining algorithmic innovations with hardware-aligned optimizations, surpassing full attention models across vario…

LLMs’ knowledge acquisition is unveiled through the lens of evolving knowledge circuits, revealing how new knowledge integration depends on relevance to existing knowledge, exhibiting distinct phases …

FinMTEB: A new benchmark reveals that general-purpose embedding models struggle in the finance domain; domain-specific models excel, and surprisingly, simple BoW outperforms sophisticated models on ce…

Dyve: A novel dynamic process verifier boosts LLM reasoning accuracy by cleverly combining fast, immediate checks with deeper, slower analyses for complex steps, achieving significant performance gain…

Cuckoo: a novel information extraction (IE) model leverages LLM pre-training data, achieving superior performance in few-shot settings by reframing next-token prediction as token extraction.

XLM-SWCM: A novel framework efficiently adapts multilingual encoders for text generation in extremely low-resource languages by cleverly sharing weights between encoder and decoder, achieving superior…

This survey paper comprehensively analyzes methods for injecting domain-specific knowledge into LLMs, categorizing them into four key approaches and evaluating their trade-offs to enhance performance …

Boosting Transformer performance, Layer-Integrated Memory (LIMe) enhances representation capacity by enabling access to earlier layers’ hidden states, significantly improving performance across variou…

Typhoon T1: Open Thai reasoning model improves complex task performance by generating long chains of thought, detailed methodology, and open-source resources are provided.

LLMs often fail to demonstrate true understanding of concepts, acting as ‘stochastic parrots’ – a phenomenon quantitatively proven by the PHYSICO benchmark.

SQUARE, a novel prompting technique, enhances LLM reasoning by prompting self-interrogation through sequential question answering, significantly outperforming traditional methods.

Fact-checkers need explainable AI: This study reveals how AI tools can better support fact-checkers by providing explanations tailored to their workflows, addressing unmet needs, and improving the eff…