Natural Language Processing

LIMO: Few examples unlock complex reasoning in LLMs, challenging assumptions about data-hungry models and achieving state-of-the-art results with minimal training.

AlphaGeometry2 surpasses average IMO gold medalists in solving geometry problems!

DreamDPO: Revolutionizing text-to-3D generation by directly aligning outputs with human preferences via innovative preference optimization.

Researchers unveil a data-free method to visualize and control feature flow in LLMs, enhancing interpretability and enabling targeted model steering.

Satori: A novel 7B LLM achieves state-of-the-art mathematical reasoning via autoregressive search.

QLASS boosts language agent inference by using Q-values to guide a stepwise search, improving efficiency and performance even with limited data.

Language model distillation suffers from ’teacher hacking’, where student models over-optimize flawed teacher models, degrading true performance. This paper identifies this issue and offers effective…

LLMs struggle with complex logical reasoning; ZebraLogic benchmark reveals a ‘curse of complexity’, highlighting inherent limitations and guiding future research.

Direct alignment algorithms are a blur, but this paper shows how a simple SFT phase and a scaling parameter significantly improve alignment quality, regardless of the specific reward function used.

PRIME (Process Reinforcement through IMplicit rEwards) revolutionizes LLM training by efficiently using implicit process rewards from online policy rollouts and outcome labels, significantly boosting …

PlotGen: A novel multi-agent LLM framework automates accurate scientific data visualization via multimodal feedback, boosting novice productivity and improving visualization accuracy.

New benchmark challenges LLMs with general knowledge puzzles, revealing reasoning gaps and suggesting improvements for future models.

ENCORE enables lifelong sequential knowledge editing in LLMs without performance loss, achieving 10,000 edits while maintaining downstream accuracy.

JUMP outperforms existing methods by optimizing universal multi-prompts for jailbreaking LLMs, offering a more efficient and generalizable approach to LLM adversarial attacks.

FastKV: A novel KV cache compression method speeds up long-context LLM processing 2x by selectively propagating tokens and using GQA-aware compression, maintaining accuracy.

DeepRAG enhances LLM reasoning by strategically integrating retrieval, modeled as an MDP, improving accuracy by 21.99% and retrieval efficiency.

ChartCitor: A multi-agent LLM framework combats LLM hallucination in ChartQA by providing fine-grained visual citations, enhancing user trust and productivity.

InferenceGuard ensures almost-sure safe LLM responses at inference time by framing safe generation as a constrained Markov Decision Process in the LLM’s latent space, achieving high safety rates witho…

Boosting Large Language Model (LLM) inference speed using probabilistic inference via particle-based Monte Carlo methods achieves 4-16x better scaling than deterministic search approaches.

WILDCHAT-50M: Largest public chat dataset refines LLM post-training, showing superior SFT performance with fewer samples.