We consider a broad range of subject areas focused on language modeling for the first iteration of COLM. We consider the term "language model" in the broadest way. A non-exhaustive list of topics of interests includes:

* All about alignment: fine-tuning, instruction-tuning, reinforcement learning (with human feedback), prompt tuning, and in-context alignment

* All about data: pre-training data, alignment data, and synthetic data --- via manual or algorithmic analysis, curation, and generation

* All about evaluation: benchmarks, simulation environments, scalable oversight, evaluation protocols and metrics, human and/or machine evaluation

* All about societal implications: bias, equity, misuse, jobs, climate change, and beyond

* All about safety: security, privacy, misinformation, adversarial attacks and defenses

* Science of LMs: scaling laws, fundamental limitations, emergent capabilities, demystification, interpretability, complexity, training dynamics, grokking, learning theory for LMs

* Compute efficient LMs: distillation, compression, quantization, sample efficient methods, memory efficient methods

* Engineering for large LMs: distributed training and inference on different hardware setups, training dynamics, optimization instability

* Learning algorithms for LMs: learning, unlearning, meta learning, model mixing methods, continual learning

* Inference algorithms for LMs: decoding algorithms, reasoning algorithms, search algorithms, planning algorithms

* Human mind, brain, philosophy, laws and LMs: cognitive science, neuroscience, linguistics, psycholinguistics, philosophical, or legal perspectives on LMs

* LMs for everyone: multi-linguality, low-resource languages, vernacular languages, multiculturalism, value pluralism

* LMs and the world: factuality, retrieval-augmented LMs, knowledge models, commonsense reasoning, theory of mind, social norms, pragmatics, and world models

* LMs and embodiment: perception, action, robotics, and multimodality

* LMs and interactions: conversation, interactive learning, and multi-agents learning

* LMs with tools and code: integration with tools and APIs, LM-driven software engineering

* LMs on diverse modalities and novel applications: visual LMs, code LMs, math LMs, and so forth, with extra encouragements for less studied modalities or applications such as chemistry, medicine, education, database and beyond