Natural Language Processing

RE-Control: Aligning LLMs via dynamic representation editing using optimal control theory, achieving superior alignment with significantly fewer resources than fine-tuning.

ALI-Agent uses LLM-powered agents for in-depth, adaptive assessment of LLMs’ alignment with human values, overcoming limitations of existing static benchmarks.

Language model algorithms have improved drastically, halving compute needs every 8 months since 2012, surpassing Moore’s Law; however, compute scaling, not algorithms, drove most recent performance ga…

Randomly initialized transformers, with only embedding layers optimized, surprisingly excel at various algorithmic tasks, revealing inherent capabilities even before training.

AlchemistCoder enhances code LLMs by pioneering hindsight tuning on multi-source data, harmonizing conflicting styles via AlchemistPrompts, and achieving state-of-the-art performance.

AGILE, a novel reinforcement learning framework, significantly enhances LLM agents’ performance on complex conversational tasks by integrating memory, tools, expert interactions, and reflection, outpe…

AGENTPOISON: A novel backdoor attack compromises LLM agents by poisoning their memory or knowledge bases, achieving high success rates with minimal performance impact.

This paper introduces a parametric World Knowledge Model (WKM) to improve AI agent planning by integrating both global task knowledge and dynamic state knowledge, thereby overcoming current LLMs’ limi…

Adversarial Representation Engineering (ARE) offers a unified, interpretable approach for editing large language models (LLMs) by using a representation sensor as an editing oracle, enhancing model sa…

Boosting LLM efficiency, this study introduces adversarial moment-matching distillation, outperforming existing methods by matching action-value moments for superior knowledge transfer and achieving s…

TP-LLaMA boosts tool-augmented LLMs by optimizing inference trajectories using preference learning from both successful and failed attempts, achieving superior performance and efficiency.

RAIL, a novel continual learning method for vision-language models, tackles catastrophic forgetting and maintains zero-shot abilities without domain-identity hints or reference data. Using a recursiv…

Adaptive Layer Sparsity (ALS) revolutionizes large language model (LLM) compression by intelligently pruning less important layers, achieving significant size reduction without performance loss. It o…

Ctrl-G: A neuro-symbolic framework enables adaptable control of LLM generation by combining any LLM with a Hidden Markov Model (HMM), ensuring outputs adhere to logical constraints specified as determ…

AdaNovo tackles data biases in de novo peptide sequencing by using Conditional Mutual Information, significantly improving PTM identification and overall accuracy.

This paper introduces segment auctions, maximizing logarithmic social welfare, for integrating ads into LLM outputs via Retrieval Augmented Generation, balancing ad revenue and output quality.

LLM compression accuracy hides crucial behavioral changes; use % flips and KL-divergence for better evaluation.

Probe sampling accelerates Greedy Coordinate Gradient (GCG) and other prompt optimization methods by up to 5.6x, achieving equal or better attack success rates, making LLM safety research faster and m…

Boost LLM inference speed by 3x! This paper refines blockwise parallel decoding (BPD) by cleverly refining draft predictions, resulting in faster text generation for large language models.

Transformers exhibit abrupt learning: training loss plateaus, then suddenly drops. This study uses matrix completion to demonstrate this phenomenon, providing insights into the model’s algorithmic sh…