On the Effect of Instruction Tuning Loss on Generalization
Dept. of Electrical Engineering（电气工程系）；Indian Institute of Technology Delhi（印度理工学院德里）；Media and Data Science Research（媒体与数据科学研究）；Adobe Inc., India（Adobe公司，印度）
Anwoy Chatterjee, H S V N S Kowndinya Renduchintala, Sumit Bhatia, Tanmoy Chakraborty
备注：
To appear in Transactions of the Association for Computational Linguistics (TACL)
Instruction Tuning has emerged as a pivotal post-training paradigm that enables pre-trained language models to better follow user instructions. Despite its significance, little attention has been given to optimizing the loss function used. A fundamental, yet often overlooked, question is whether the conventional auto-regressive objective - where loss is computed only on response tokens, excluding prompt tokens - is truly optimal for instruction tuning. In this work, we systematically investigate the impact of differentially weighting prompt and response tokens in instruction tuning loss, and propose Weighted Instruction Tuning (WIT) as a better alternative to conventional instruction tuning. Through extensive experiments on five language models of different families and scale, three finetuning datasets of different sizes, and five diverse evaluation benchmarks, we show that the standard instruction tuning loss often yields suboptimal performance and limited robustness to input prompt variations. We find that a low-to-moderate weight for prompt tokens coupled with a moderate-to-high weight for response tokens yields the best-performing models across settings and also serve as better starting points for the subsequent preference alignment training. These findings highlight the need to reconsider instruction tuning loss and offer actionable insights for developing more robust and generalizable models. Our code is open-sourced at https://github.com/kowndinya-renduchintala/WIT.
LongForm: Effective Instruction Tuning with Reverse Instructions
Center for Information and Language Processing, LMU Munich（慕尼黑大学信息与语言处理中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Language Technology Lab, University of Cambridge（剑桥大学语言技术实验室）
Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions. These approaches rely on vast amounts of human supervision in the form of crowdsourced datasets or user interactions. In this work, we introduce Unnatural Instructions: a large dataset of creative and diverse instructions, collected with virtually no human labor. We collect 64,000 examples by prompting a language model with three seed examples of instructions and eliciting a fourth. This set is then expanded by prompting the model to rephrase each instruction, creating …
We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022). Through careful ablation studies on the Flan Collection of tasks and methods, we tease apart the effect of design decisions which enable Flan-T5 to outperform prior work by 3-17%+ across evaluation settings. We find task balancing and enrichment techniques are overlooked but critical to effective instruction tuning, and in particular, training with mixed prompt settings (zero-shot, few-shot, and chain-of-thought) actually yields stronger (2%+) perfor…
University of Southern California（南加州大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；The Pennsylvania State University（宾夕法尼亚州立大学）；University of California, Davis（加利福尼亚大学戴维斯分校）
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；LibrAI；Monash University（莫纳什大学）；University of British Columbia（不列颠哥伦比亚大学）；The University of Melbourne（墨尔本大学）
SwitchCIT: Switching for Continual Instruction Tuning
Coordinated Science Laboratory（协调科学实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Mathematics（数学系）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；Arizona State University（亚利桑那州立大学）
Humans (e.g., crowdworkers) have a remarkable ability in solving different tasks, by simply reading textual instructions that define them and looking at a few examples. Despite the success of the conventional supervised learning on individual datasets, such models often struggle with generalization across tasks (e.g., a question-answering system cannot solve classification tasks). A long-standing challenge in AI is to build a model that learns a new task by understanding the human-readable instructions that define it. To study this, we introduce NATURAL INSTRUCTIONS, a dataset of 61 distinct t…
An Empirical Study of Instruction-tuning Large Language Models in Chinese
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；APUS AiLMe Lab（APUS AiLMe实验室）
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
Stanford University（斯坦福大学）；Emory University（埃默里大学）；University of California, Berkeley（加州大学伯克利分校）；University of Washington（华盛顿大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Department of Linguistics and Translation, City University of Hong Kong（香港城市大学语言学及翻译学系）
Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning
School of Cyber Science and Engineering Nanjing University of Science and Technology China（南京理工大学信息科学与工程学院）；Meituan China（美团）；Zhipu.AI China（智谱AI）；Institute of Information Engineering Chinese Academy of Sciences China（中国科学院信息工程研究所）
Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning
DataArc Tech Ltd.（DataArc科技有限公司）；Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；IDEA Research, International Digital Economy Academy（IDEA研究所，国际数字经济学院）；Hithink RoyalFlush Information Network Co., Ltd（Hithink RoyalFlush信息网络有限公司）
