Adaptation of Large Language Models (2025)

Abstract: This tutorial on adaptation of Large Language Models (LLMs) is designed to address the growing demand for models that go beyond the static capabilities of generic LLMs by providing an overview of dynamic, domain-specific, and task-adaptive LLM adaptation techniques. While general LLMs have demonstrated strong generalization across a variety of tasks, they often struggle to perform well in specialized domains such as finance, healthcare, and code generation for underrepresented languages. Additionally, their static nature limits their ability to evolve with the changing world, and they are often extremely large in size, making them impractical and costly to deploy at scale. As a result, the adaptation of LLMs has drawn much attention since the birth of LLMs and is of core importance, both for industry, which focuses on serving its targeted users, and academia, which can greatly benefit from small but powerful LLMs

## brief description this tutorial on adaptation of large language models (llms) is designed to address the growing demand for models that go beyond the static capabilities of generic llms by providing an overview of dynamic, domain-specific, and task-adaptive llm adaptation techniques. while general llms have demonstrated strong generalization across a variety of tasks, they often struggle to perform well in specialized domains such as finance, healthcare, and code generation for underrepresented languages. additionally, their static nature limits their ability to evolve with the changing world, and they are often extremely large in size, making them impractical and costly to deploy at scale. as a result, the adaptation of llms has drawn much attention since the birth of llms and is of core importance, both for industry, which focuses on serving its targeted users, and academia, which can greatly benefit from small but powerful llms. to our knowledge, there has been no similar tutorial on this topic in the past few years 1 . to address this gap, this tutorial aims to provide an overview of the llm adaptation techniques. we start with an introduction to llm adaptation, from both the data perspective, which has been widely accepted as one of the most important ingredients in llm training, and the model perspective, which focuses on the training strategies to adapt the llms. in session 2, we emphasize how the evaluation metrics and benchmarks are different from other techniques. there is a growing recognition that evaluating adapted llms should not focus solely on their performance within the target domains or tasks. while this remains important, an emerging consensus is that adaptation should not be not only specialized task performance but also the preservation of general knowledge. furthermore, as we will present in this tutorial, adapting an llm usually involves multiple training stages, where each stage contributes to the final outcome. this multi-stage process requires more comprehensive evaluation methods, as conventional metrics may not capture the cumulative impact of these stages. as such, researchers are increasingly focused on designing evaluation metrics and benchmarks that assess both the specialized knowledge gained and the general knowledge retained. after establishing the problems in the aforementioned sessions, we explore various adaptation techniques. when powerful general llms first emerged, many believed that prompt engineering alone, without any actual model training (i.e., updating the model's parameters), was sufficient to adapt them for new tasks. however, recently we have seen more researchers begin finetuning model wights. this shift is understandable not only because training typically leads to better results on specialized tasks, but also because training has become more affordable due to the growing number of techniques that enable efficient training. for example, the training cost increased from @xmath04 million to train the gpt-3 (davinci), but it has recently dropped to around $0.8 million to train a gpt-3 on-par model like phi3.5 @xcite . in light of this trend, this tutorial will focus on increasingly popular methods that adapt llms through training. we categorize adaptation techniques into two main families. the first is parametric knowledge adaptation, which focuses on updating the parametric knowledge within llms, including methods like domain-adaptive pre-training (dapt), instructional tuning (it), and preference learning (pl) via human or model feedback. additionally, we will discuss real-time adaptation techniques, including model editing, which allows llms to be updated dynamically in production environments. the second kind of adaptation is semi-parametric knowledge adaptation, where the goal is to update llm parameters to better leverage external knowledge or tools (e.g., documents or functions) through techniques like retrieval-augmented generation (rag) and agent-based systems. ## detailed outline this will be a three-hour tutorial devoted to the cutting-edge topic of the adaptation of llms. ## diversity consideration the topic of llm adaptation will be inclusive for a broad range of communities. it will be of interest to industrial practitioners as they aim to deploy the best adaptive models for their targeted customers. additionally, academic researchers will find value in this topic, as it essentially addresses a continual learning problem, enabling llms to adapt to new environments without forgetting-an essential step toward advancing artificial general intelligence. this topic also makes it easier for users to engage with llms, as it involves techniques to train smaller models that can be deployed efficiently. it naturally spans a wide range of domains and tasks, making it relevant to participants interested in various specific areas. our presenters include both junior and senior researchers from industry and academia, and we also bring geographical diversity, with instructors from both north america and asia. this diverse group of instructors will help attract a wide audience and encourage participation from various backgrounds. we will make our tutorial materials digitally accessible to all participants. all materials will be publicly available before the tutorial. we will provide slides and discussion questions before the tutorial to the audience. during the tutorial sessions, we will work with student volunteers to encourage open dialogue and promote active listening, allowing participants to share their thoughts and experiences without fear of judgment. after the tutorial, we will actively collect feedback to identify areas for improvement related to diversity and inclusion and share it with future tutorial presenters. our presenter team will share our tutorial with a worldwide audience by promoting it on social media, and to diverse research communities. we will work with acl/naacl/emnlp d&i teams, and consult resources such as the big directory to diversify our audience participation. ## ethics statement the tutorial will present general problems and algorithms related to llm adaptation in nlp. all evaluation datasets are public domain benchmarks. thus, the tutorial does not have any ethical issues. ## presenter biography zixuan ke is a research scientist at salesforce ai research, where he works with dr. shafiq joty on continual pre-training of llms. prior to that, he earned his ph.d. at the university of illinois, chicago, where he worked with dr. bing liu on continual learning. his research studies how to adapt the foundation models, particularly llms, for an ever-changing world characterized by emerging domains, events, topics, or information. he has published 25+ papers in top-tier conferences such as iclr, icml, neurips, naacl, acl, and emnlp with around 1.5k citations. he has served as an area chair and pc member for prestigious conferences such as iclr, neurips, acl, emnlp, and as a reviewer for leading journals such as neural networks, neurocomputing, artificial intelligence, tkde, and tpami, since 2021. he has given an invited short phd course on continual learning at aalborg university in the summer of 2022 and has also given several oral presentations at leading conferences. further information about him, including past talks and slides, can be found at https://vincent950129.github.io/ . yifei ming is a research scientist at salesforce ai research. he received his ph.d. in computer science from the university of wisconsin-madison in 2024. his research spans both empirical and theoretical studies, with a focus on understanding, benchmarking, and developing novel algorithms towards trustworthy foundation models (llm and vlm). he has a prolific publication record with 20+ papers in top-tier ai conferences and journals. he co-organized several workshops and has served on the program committees of leading machine learning conferences and journals since 2021, including neurips, iclr, icml, aaai, emnlp, ijcv, tpami, and nn. further information can be found at https://alvinmingsf.github.io/ . shafiq joty is a research director at salesforce research and an associate professor at the computer science department of the nanyang technological university. his research has contributed to over 30+ patents and 150+ papers in top-tier nlp and ml conferences and journals. he has served as a program chair of sigdial'23, a member of the best paper award committees for iclr'23 and naacl'22, and in the capacity of a (senior) area chair for many leading nlp and ml conferences (e.g. neurips, emnlp, and acl). he previously gave tutorials at emnlp'23, ieeevis'22, acl'19, coling'18 and icdm'18. further information about him, including past talks and slides, can be found at https://raihanjoty.github.io/ .