Summary Schema-learning and rebinding in in-context learning arxiv.org
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One Line
The paper suggests using clone-structured causal graphs as an effective tool for understanding in-context learning in large language models.
Slides
Slide Presentation (12 slides)
Key Points
- In-context learning (ICL) in large language models (LLMs) can be understood using clone-structured causal graphs (CSCGs).
- Schema-learning and rebinding are mechanisms of in-context learning.
- The Bayesian inference perspective is insufficient to explain the properties of ICL.
- The Context-Sensitive Clone-Graph (CSCG) model can learn and infer latent concepts in the GINC dataset.
- Overallocation of clones in the CSCG model improves performance and accuracy.
- The "dax" test evaluates a model's ability to absorb new words from a single presentation.
- The document includes references to various research papers and articles related to schema-learning, rebinding, and in-context learning.
- Tables and figures present the average in-context accuracy of different tasks based on CSCG overallocation.
Summaries
30 word summary
This paper examines in-context learning (ICL) in large language models (LLMs) and proposes using clone-structured causal graphs (CSCGs) for understanding ICL. CSCGs are shown to be effective in acquiring knowledge.
34 word summary
This paper explores the mechanisms of in-context learning (ICL) in large language models (LLMs) and proposes an alternative approach using clone-structured causal graphs (CSCGs) to understand ICL. The authors demonstrate that CSCGs can acquire
512 word summary
This paper explores the mechanisms of in-context learning (ICL) in large language models (LLMs) and proposes an alternative approach using clone-structured causal graphs (CSCGs) to understand ICL. The authors demonstrate that CSCGs can acquire
The excerpt discusses the concepts of schema-learning and rebinding in in-context learning. It introduces the Conditional Slot-and-Clone Graph (CSCG) model, which uses a transition tensor and emission matrix to represent action-conditional dynamics and observation probabilities.
Schema-learning and rebinding are mechanisms of in-context learning and emergence. The fast rebinding algorithm (Algorithm 1) is used to update the emission matrix in a Conditional Sequence Context Graph (CSCG). The algorithm identifies latent states and time steps
The Bayesian inference perspective on in-context learning (ICL) is insufficient to explain the properties of ICL discussed in the next sections. Context-sensitive and transitively generalizing storage and retrieval alone cannot account for these properties. In addition to learning the layout
The study focuses on the ability of a Context-Sensitive Clone-Graph (CSCG) model to learn and infer latent concepts in the GINC dataset. The model is trained with 50 clones per token and achieves accurate prompt completion by improving localization
The study used a test set with 100 prompts, consisting of instructions and tokens. The training process involved allocating clones to tokens based on the number of distinct contexts in the training data. Different overallocation ratios were tested. The results showed that CSCGs
In the study, the researchers conducted a "dax" test to evaluate a model's ability to absorb new words from a single presentation. They trained a CSCG model on the PreCo dataset for coreference resolution and tested it on word-replaced
This document contains a list of references to various research papers and articles related to schema-learning, rebinding, in-context learning, and other topics in artificial intelligence and machine learning. The references cover a wide range of subjects, including interpretability, attention mechanisms
This text excerpt includes references to various papers on schema-learning and rebinding in in-context learning. It discusses the EM algorithm for learning the emission matrix of a CSCG with a fixed transition matrix. The prompt completion algorithm is described, which considers a single
The excerpted text presents a table with numerical values for different scenarios. The table is divided into four sections, each representing a different number of clones (10, 50, 100, and 1000). Within each section, there are columns
The summary includes the following key points:
- Table 1 shows the in-context accuracy for a CSCG with different numbers of clones trained on the GINC dataset. - Tables 2 and 3 present the natural language instructions used for the list and
The document discusses schema-learning and rebinding in in-context learning. It presents tables and figures that show the average in-context accuracy of different tasks based on CSCG overallocation. The results indicate that overallocation improves performance and increases accuracy. The document also
The table shows the average in-context accuracy for different tasks and prompts. The accuracy is measured based on the overallocation ratio of the CSCG model. The table provides information on various tasks such as listing elements, reversing lists, repeating lists, shifting lists