Distributed Rule Vectors is A Key Mechanism in Large Language Models' In-Context Learning

• URL: http://arxiv.org/abs/2406.16007v1
• Date: Sun, 23 Jun 2024 04:29:13 GMT
• Title: Distributed Rule Vectors is A Key Mechanism in Large Language Models' In-Context Learning
• Authors: Bowen Zheng, Ming Ma, Zhongqiao Lin, Tianming Yang,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable abilities, one of the most important being In-Context Learning (ICL) Previous work hypothesized that the network creates a "task vector" in specific positions during ICL. We discover that such "task vectors" do not exist in tasks where the rule has to be defined through multiple demonstrations.
• Score: 3.1775609005777024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable abilities, one of the most important being In-Context Learning (ICL). With ICL, LLMs can derive the underlying rule from a few demonstrations and provide answers that comply with the rule. Previous work hypothesized that the network creates a "task vector" in specific positions during ICL. Patching the "task vector" allows LLMs to achieve zero-shot performance similar to few-shot learning. However, we discover that such "task vectors" do not exist in tasks where the rule has to be defined through multiple demonstrations. Instead, the rule information provided by each demonstration is first transmitted to its answer position and forms its own rule vector. Importantly, all the rule vectors contribute to the output in a distributed manner. We further show that the rule vectors encode a high-level abstraction of rules extracted from the demonstrations. These results are further validated in a series of tasks that rely on rules dependent on multiple demonstrations. Our study provides novel insights into the mechanism underlying ICL in LLMs, demonstrating how ICL may be achieved through an information aggregation mechanism.

Related papers

• Vector-ICL: In-context Learning with Continuous Vector Representations [75.96920867382859]
  Large language models (LLMs) have shown remarkable in-context learning capabilities on textual data. We explore whether these capabilities can be extended to continuous vectors from diverse domains, obtained from black-box pretrained encoders. In particular, we find that pretraining projectors with general language modeling objectives enables Vector-ICL.
  arXiv  Detail & Related papers  (2024-10-08T02:25:38Z)
• What Do Language Models Learn in Context? The Structured Task Hypothesis [89.65045443150889]
  Large language models (LLMs) learn a novel task from in-context examples presented in a demonstration, termed in-context learning (ICL) One popular hypothesis explains ICL by task selection. Another popular hypothesis is that ICL is a form of meta-learning, i.e., the models learn a learning algorithm at pre-training time and apply it to the demonstration.
  arXiv  Detail & Related papers  (2024-06-06T16:15:34Z)
• Show, Don't Tell: Aligning Language Models with Demonstrated Feedback [54.10302745921713]
  Demonstration ITerated Task Optimization (DITTO) directly aligns language model outputs to a user's demonstrated behaviors. We evaluate DITTO's ability to learn fine-grained style and task alignment across domains such as news articles, emails, and blog posts.
  arXiv  Detail & Related papers  (2024-06-02T23:13:56Z)
• Implicit In-context Learning [37.0562059811099]
  In-context Learning (ICL) empowers large language models to adapt to unseen tasks during inference by prefixing a few demonstration examples prior to test queries. We introduce Implicit In-context Learning (I2CL), an innovative paradigm that addresses the challenges associated with traditional ICL by absorbing demonstration examples within the activation space. I2CL achieves few-shot performance with zero-shot cost and exhibits robustness against the variation of demonstration examples.
  arXiv  Detail & Related papers  (2024-05-23T14:57:52Z)
• In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering [37.334374583093165]
  Large language models (LLMs) demonstrate emergent in-context learning capabilities. We propose an alternative approach that recasts in-context learning as in-context vectors (ICV) ICV achieves better performance compared to standard in-context learning.
  arXiv  Detail & Related papers  (2023-11-11T21:19:44Z)
• Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks [54.153914606302486]
  In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs) We propose a new paradigm called Hint-enhanced In-Context Learning (HICL) to explore the power of ICL in open-domain question answering.
  arXiv  Detail & Related papers  (2023-11-03T14:39:20Z)
• Ambiguity-Aware In-Context Learning with Large Language Models [27.20414960164616]
  In-context learning (ICL) i.e. showing LLMs task-specific demonstrations has led to downstream gains with no task-specific fine-tuning required. This study investigates how to select good demonstrations for ICL. We find that it is beneficial to not only choose semantically similar ICL demonstrations but also to choose those that help resolve the inherent label ambiguity surrounding the test example.
  arXiv  Detail & Related papers  (2023-09-14T17:48:34Z)
• Iterative Forward Tuning Boosts In-Context Learning in Language Models [88.25013390669845]
  In this study, we introduce a novel two-stage framework to boost in-context learning in large language models (LLMs) Specifically, our framework delineates the ICL process into two distinct stages: Deep-Thinking and test stages. The Deep-Thinking stage incorporates a unique attention mechanism, i.e., iterative enhanced attention, which enables multiple rounds of information accumulation.
  arXiv  Detail & Related papers  (2023-05-22T13:18:17Z)
• Explaining Emergent In-Context Learning as Kernel Regression [61.57151500616111]
  Large language models (LLMs) have initiated a paradigm shift in transfer learning. In this paper, we investigate the reason why a transformer-based language model can accomplish in-context learning after pre-training. We find that during ICL, the attention and hidden features in LLMs match the behaviors of a kernel regression.
  arXiv  Detail & Related papers  (2023-05-22T06:45:02Z)
• What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning [24.395288160951118]
  Large language models (LLMs) exploit in-context learning (ICL) to solve tasks with only a few demonstrations. We characterize two ways through which ICL leverages demonstrations. We show that models can achieve non-trivial performance with only TR, and TR does not further improve with larger models or more demonstrations.
  arXiv  Detail & Related papers  (2023-05-16T18:05:19Z)

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.