Large Language Models

MixLLM achieves state-of-the-art LLM compression by using mixed-precision quantization between output features, improving accuracy and system efficiency.

M-ALERT, a new multilingual benchmark, reveals significant safety inconsistencies across languages in top LLMs.

Token-level editing prevents language model collapse from synthetic data by theoretically bounding test error and empirically improving model performance.

Fietje: an open-source, efficient Dutch language model outperforming larger models.

AceMath achieves state-of-the-art results in mathematical reasoning by introducing highly effective instruction-tuned models and reward models.

AI agents are tested in a simulated company, revealing their capability to automate tasks and shortcomings with complex workflows and interfaces.

First benchmark for RAG reward models reveals their limitations and the need for preference-aligned training.

Mix-LN boosts deep layer power in LLMs.

Auto-built benchmark with up-to-date knowledge ensures contamination-free LLM evaluation.

G-Pass@k & LiveMathBench: Evaluating the stability of LLMs.

Self-play method SPAR enhances LLMs instruction following abilities, beating GPT-4 on IFEval

SepLLM shrinks LLMs, speeding them up by over 50% without losing much accuracy.

Smaller is better: SLMs outperform LLMs in evolving complex & diverse instructions for AI training.

New benchmark for evaluating long-context models finds sub-O(n) methods lacking in real-world use cases.

BLT: tokenizer-free LLM for efficiency and robustness

Norwegians show that using copyrighted material improves LLMs, but raises legal and ethical issues.

RULEARENA, a new benchmark, rigorously evaluates large language models’ ability to apply complex, real-world rules across diverse scenarios, revealing significant shortcomings in current LLMs’ rule-gu…

Phi-4: a 14B parameter LLM surpassing its teacher model (GPT-4) in STEM-focused QA through innovative synthetic data generation and post-training techniques.

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!