Large Language Models

This paper introduces an innovative iterative prompting method for estimating epistemic uncertainty in LLMs, enabling reliable detection of hallucinations.

Time-reversed language models provide unsupervised feedback for improving LLMs, offering a cost-effective alternative to human feedback and enhancing LLM safety.

This paper introduces ‘Thought of Search,’ a novel, efficient planning approach using LLMs that prioritizes soundness and completeness. It leverages LLMs to generate Python code for search components,…

SPRY: A memory-efficient federated learning algorithm for finetuning LLMs on resource-constrained devices, achieving high accuracy and speed.

LLMs use different internal structures for few-shot learning and fine-tuning, showing a transition in the middle network layers that impacts information encoding and task solving strategies.

This research dramatically accelerates and improves hybrid language models by distilling large Transformers into linear RNNs, achieving performance comparable to the original Transformer with signific…

Hybrid Preference Optimization (HyPO) outperforms existing offline methods for fine-tuning LLMs by leveraging both offline and online data, achieving better performance and efficiency.

LoRA’s initialization significantly impacts finetuning; initializing matrix A randomly and B to zero yields better performance than vice-versa due to enabling larger learning rates.

New research precisely defines the computational limits of training large language models, revealing a sharp threshold based on parameter matrix entries, paving the way for faster algorithms.

Large language models (LLMs) struggle with factual inconsistencies (‘hallucinations’) and the ‘reversal curse,’ where information recall depends heavily on the input order. This work reframes the cur…

State-space models (SSMs) rival transformers in language modeling, but their capabilities remain unclear; this paper rigorously analyzes SSM expressivity, revealing unique strengths and limitations, i…

Transformers learn to perform in-context learning of Markov chains hierarchically, progressing from simpler unigram strategies to more complex bigram solutions, with the presence of simpler solutions …

Softmax regression reveals in-context learning’s surprising similarity to gradient descent in self-attention Transformers, showing the models’ remarkable learning capabilities.

Smart prompt engineering is key to unlocking LLMs’ full potential. This paper reveals that cleverly selecting examples (exemplar optimization) can outperform optimizing instructions alone, even with S…

Transformer Language Models’ (LMs) sensitivity to seemingly arbitrary prompt changes is explained by identifying low-rank communication channels between layers. By decomposing attention heads, resear…

LLMs struggle with downstream tasks using mismatched data. TAIA, a novel inference-time method, solves this by selectively using only attention parameters during inference after training all parameter…

LLMs struggle with very low-resource programming languages. SPEAC, a novel synthetic programming elicitation and compilation approach, uses an intermediate language to enable LLMs to generate syntact…

The Synthesize-Partition-Adapt (SPA) framework leverages synthetic data to generate diverse, high-quality responses from foundation models, enriching user experience.

Synatra synthesizes high-quality digital agent training data from online tutorials and web pages, significantly improving agent performance on complex web-based tasks at a fraction of the cost of huma…

LASR, a novel symbolic regression method, uses zero-shot LLM queries to discover and evolve abstract concepts, substantially outperforming state-of-the-art approaches and discovering a new LLM scaling…