Instruction tuning plays a critical role in aligning large language models (LLMs) with human
preference. Despite the vast amount of open instruction datasets, naively training a LLM on …

Instruction tuning commonly means finetuning a language model on instruction-response
pairs. We discover two forms of adaptation (tuning) that are deficient compared to instruction …

Codebooks--documents that operationalize constructs and outline annotation procedures--
are used almost universally by social scientists when coding unstructured political texts …

Large language models (LLMs) may memorize sensitive or copyrighted content, raising
privacy and legal concerns. Due to the high cost of retraining from scratch, researchers …

Alignment is a crucial step to enhance the instruction-following and conversational abilities
of language models. Despite many recent work proposing new algorithms, datasets, and …

Direct Alignment Algorithms (DAAs), such as Direct Preference Optimisation (DPO) and
Identity Preference Optimisation (IPO), have emerged as alternatives to online …

Utilizing user profiles to personalize Large Language Models (LLMs) has been shown to
enhance the performance on a wide range of tasks. However, the precise role of user …

Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as
summarization and question-answering across large sets of documents. While LLMs have …

To induce desired behaviors in large language models (LLMs) for interaction-driven tasks,
the instruction-tuning stage typically trains LLMs on instruction-response pairs using the next …

Existing pruning techniques for large language models (LLMs) targeting domain-specific
applications typically follow a two-stage process: pruning the pretrained general-purpose …