Why Larger Language Models Do In-context Learning Differently?

• URL: http://arxiv.org/abs/2405.19592v1
• Date: Thu, 30 May 2024 01:11:35 GMT
• Title: Why Larger Language Models Do In-context Learning Differently?
• Authors: Zhenmei Shi, Junyi Wei, Zhuoyan Xu, Yingyu Liang,
• Abstract summary: Large language models (LLM) have emerged as a powerful tool for AI, with the key ability of in-context learning (ICL) One recent mysterious observation is that models of different scales may have different ICL behaviors.
• Score: 12.554356517949785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLM) have emerged as a powerful tool for AI, with the key ability of in-context learning (ICL), where they can perform well on unseen tasks based on a brief series of task examples without necessitating any adjustments to the model parameters. One recent interesting mysterious observation is that models of different scales may have different ICL behaviors: larger models tend to be more sensitive to noise in the test context. This work studies this observation theoretically aiming to improve the understanding of LLM and ICL. We analyze two stylized settings: (1) linear regression with one-layer single-head linear transformers and (2) parity classification with two-layer multiple attention heads transformers (non-linear data and non-linear model). In both settings, we give closed-form optimal solutions and find that smaller models emphasize important hidden features while larger ones cover more hidden features; thus, smaller models are more robust to noise while larger ones are more easily distracted, leading to different ICL behaviors. This sheds light on where transformers pay attention to and how that affects ICL. Preliminary experimental results on large base and chat models provide positive support for our analysis.

Related papers

• Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model [8.06111903129142]
  This paper proposes a miniblock diagonal hessian-free (Mini-Hes) optimization for building an LFA model. Experiment results indicate that, with Mini-Hes, the LFA model outperforms several state-of-the-art models in addressing missing data estimation task.
  arXiv  Detail & Related papers  (2024-02-19T08:43:00Z)
• In-Context Language Learning: Architectures and Algorithms [73.93205821154605]
  We study ICL through the lens of a new family of model problems we term in context language learning (ICLL) We evaluate a diverse set of neural sequence models on regular ICLL tasks.
  arXiv  Detail & Related papers  (2024-01-23T18:59:21Z)
• Improving In-context Learning via Bidirectional Alignment [41.214003703218914]
  Large language models (LLMs) have shown impressive few-shot generalization on many tasks via in-context learning (ICL) We propose Bidirectional Alignment (BiAlign) to fully leverage the models' preferences for ICL examples to improve the ICL abilities of student models. Specifically, we introduce the alignment of input preferences between student and teacher models by incorporating a novel ranking loss.
  arXiv  Detail & Related papers  (2023-12-28T15:02:03Z)
• The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction [22.659005954676598]
  We show that it is possible to significantly improve the performance of Large Language Models by selectively removing higher-order components of their weight matrices. This simple intervention, which we call LAyer-SElective Rank reduction (LASER), can be done on a model after training has completed. We show extensive experiments demonstrating the generality of this finding across language models and datasets.
  arXiv  Detail & Related papers  (2023-12-21T03:51:08Z)
• Small Models are Valuable Plug-ins for Large Language Models [65.29370906766997]
  Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable. We propose Super In-Context Learning (SuperICL) which allows black-box LLMs to work with locally fine-tuned smaller models.
  arXiv  Detail & Related papers  (2023-05-15T17:59:01Z)
• eP-ALM: Efficient Perceptual Augmentation of Language Models [70.47962271121389]
  We propose to direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning.
  arXiv  Detail & Related papers  (2023-03-20T19:20:34Z)
• Training Trajectories of Language Models Across Scales [99.38721327771208]
  Scaling up language models has led to unprecedented performance gains. How do language models of different sizes learn during pre-training? Why do larger language models demonstrate more desirable behaviors?
  arXiv  Detail & Related papers  (2022-12-19T19:16:29Z)
• Dynamically-Scaled Deep Canonical Correlation Analysis [77.34726150561087]
  Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model.
  arXiv  Detail & Related papers  (2022-03-23T12:52:49Z)
• MoEfication: Conditional Computation of Transformer Models for Efficient Inference [66.56994436947441]
  Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost. We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon. We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
  arXiv  Detail & Related papers  (2021-10-05T02:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.