Natural Language Processing

LUSIFER: a novel zero-shot approach empowers English-centric LLM embedding models for multilingual tasks without explicit multilingual training data, significantly enhancing performance, especially fo…

Polarizing SSMs’ state transition matrices enhances long-range dependency modeling by mitigating recency bias and over-smoothing.

TANGOFLUX: Blazing-fast, high-fidelity text-to-audio generation using novel CLAP-Ranked Preference Optimization.

MAPQATOR: a web app that streamlines creation of reproducible geospatial QA datasets, boosting annotation speed by 30x!

New benchmarks, HumanEval Pro and MBPP Pro, reveal LLMs struggle with self-invoking code generation, highlighting a critical gap in current code reasoning capabilities.

Researchers introduce Learned Embedding Propagation (LEP), a novel technique that efficiently adapts large language models (LLMs) to new languages using minimal training data, thus overcoming limitati…

Dynasor optimizes LLM reasoning by dynamically allocating compute based on a novel ‘certaindex’ metric, reducing compute by up to 50% and increasing query rates by 3.3x.

OneKE: a dockerized, schema-guided LLM agent system efficiently extracts knowledge from diverse sources, offering adaptability and robust error handling.

Xmodel-2: A 1.2B parameter LLM achieving state-of-the-art reasoning performance through efficient architecture and training, now publicly available!

Boost fine-tuned LLMs’ performance without sacrificing safety by merging pre- and post-tuning model weights!

TALE: A novel framework dynamically adjusts token budgets in LLM reasoning prompts, slashing costs by ~70% with minimal accuracy loss.

Molar: A novel multimodal LLM framework boosts sequential recommendation accuracy by cleverly aligning collaborative filtering with rich item representations from text and non-text data.

YuLan-Mini: An open, data-efficient 2.42B parameter LLM achieving top-tier performance with innovative training techniques.

LLM evaluation on multiple-choice questions is flawed; considering all options simultaneously, not individually, reveals much higher accuracy and challenges existing benchmark rankings.

Friends-MMC: A new dataset facilitates multi-modal multi-party conversation understanding by providing 24,000+ utterances with video, audio, and speaker annotations, enabling advancements in character…

FoPE enhances attention’s periodic extension for better length generalization in language models by addressing spectral damage in RoPE using Fourier Series and zeroing out destructive frequencies.

DRT-01 leverages long chain-of-thought reasoning to significantly boost machine translation quality, particularly for complex sentences with metaphors and similes, achieving substantial improvements o…

Frozen LLMs get a performance boost by augmenting their key-value cache with latent embeddings generated by a differentiable offline coprocessor.

B-STAR dynamically balances exploration and exploitation in self-taught reasoners, achieving superior performance in mathematical, coding, and commonsense reasoning tasks.

This study reveals that gist token-based context compression in LLMs, while effective for some tasks, suffers from key failure patterns. The authors propose fine-grained autoencoding and segment-wise…