Natural Language Processing

Boosting LLM performance: This research shows how larger language models need bigger vocabularies for optimal efficiency and performance.

Direct Alignment Algorithms (DAAs) for LLM alignment suffer from over-optimization, even without explicit reward models; this paper empirically demonstrates this and proposes scaling laws to understan…

SaulLM-54B & SaulLM-141B achieve state-of-the-art performance on legal tasks by scaling up model size, employing a specialized instruction-following protocol, and aligning model outputs with human pre…

SAFEWORLD: a new benchmark reveals and fixes LLMs’ struggle with diverse safety standards.

S2FT: Structured Sparse Fine-Tuning achieves state-of-the-art LLM fine-tuning performance, training efficiency, and inference scalability by selecting sparsely and computing densely.

S-STE achieves efficient 2:4 sparse pre-training by introducing a novel continuous pruning function, overcoming the limitations of previous methods and leading to improved accuracy and speed.

RSA: Language unlocks metric depth from single images!

RouterDC: A query-based router trained via dual contrastive learning assembles multiple LLMs, significantly outperforming individual LLMs and existing routing methods on both in- and out-of-distributi…

Leveraging model-generated synthetic data for LLM finetuning significantly improves efficiency when using both positive and strategically constructed negative examples, resulting in an eight-fold incr…

Risk-Averse RLHF fine-tunes LLMs to minimize toxic outputs while maintaining performance.

Richelieu: a self-evolving LLM-based AI agent masters Diplomacy, a complex game requiring strategic planning and negotiation, without human data, by integrating self-play for continuous improvement.

Reverse the forget-retain objectives for efficient LLM unlearning!

PaRO boosts LLM training speed by up to 266% through refined model state partitioning and optimized communication.

Researchers propose Adversarial Compression Ratio (ACR) to assess LLM memorization, offering an adversarial, flexible, and computationally efficient method for monitoring data misuse and compliance.

ReST-MCTS*: A novel LLM self-training method using process reward guided tree search, outperforming existing methods by generating higher-quality reasoning traces for improved model accuracy.

This research unveils a compute-optimal recipe for fine-tuning language models into high-quality text embedding models, offering practical guidance and scaling laws for resource-constrained settings.

RepNoise: a novel defense against harmful fine-tuning of LLMs by removing information about harmful representations, generalizing across different harmful tasks, and maintaining LLM capabilities.

ReMoDetect leverages reward models to identify and classify LLM-generated text. By using continual preference fine-tuning and incorporating human/LLM mixed text, ReMoDetect achieves state-of-the-art p…

POAD enhances LLM agents by decomposing language agent optimization to the token level, achieving finer-grained credit assignment and improved learning efficiency and generalization.

Regularizing hidden states improves reward model generalization in RLHF for LLMs, boosting accuracy and mitigating over-optimization.