Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models
- URL: http://arxiv.org/abs/2311.04131v5
- Date: Fri, 19 Jul 2024 13:57:52 GMT
- Title: Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models
- Authors: Michael Lan, Philip Torr, Fazl Barez,
- Abstract summary: This research aims to reverse engineer transformer models into human-readable representations that implement algorithmic functions.
By applying circuit interpretability analysis, we identify a key sub-circuit in both GPT-2 Small and Llama-2-7B.
We show that this sub-circuit has effects on various math-related prompts, such as on intervaled circuits, Spanish number word and months continuation, and natural language word problems.
- Score: 9.56229382432426
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While transformer models exhibit strong capabilities on linguistic tasks, their complex architectures make them difficult to interpret. Recent work has aimed to reverse engineer transformer models into human-readable representations called circuits that implement algorithmic functions. We extend this research by analyzing and comparing circuits for similar sequence continuation tasks, which include increasing sequences of Arabic numerals, number words, and months. By applying circuit interpretability analysis, we identify a key sub-circuit in both GPT-2 Small and Llama-2-7B responsible for detecting sequence members and for predicting the next member in a sequence. Our analysis reveals that semantically related sequences rely on shared circuit subgraphs with analogous roles. Additionally, we show that this sub-circuit has effects on various math-related prompts, such as on intervaled circuits, Spanish number word and months continuation, and natural language word problems. Overall, documenting shared computational structures enables better model behavior predictions, identification of errors, and safer editing procedures. This mechanistic understanding of transformers is a critical step towards building more robust, aligned, and interpretable language models.
Related papers
- Explaining Text Similarity in Transformer Models [52.571158418102584]
Recent advances in explainable AI have made it possible to mitigate limitations by leveraging improved explanations for Transformers.
We use BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, to investigate which feature interactions drive similarity in NLP models.
Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
arXiv Detail & Related papers (2024-05-10T17:11:31Z) - Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models [55.19497659895122]
We introduce methods for discovering and applying sparse feature circuits.
These are causally implicatedworks of human-interpretable features for explaining language model behaviors.
arXiv Detail & Related papers (2024-03-28T17:56:07Z) - Uncovering Intermediate Variables in Transformers using Circuit Probing [32.382094867951224]
We propose a new analysis technique -- circuit probing -- that automatically uncovers low-level circuits that compute hypothesized intermediate variables.
We apply this method to models trained on simple arithmetic tasks, demonstrating its effectiveness at (1) deciphering the algorithms that models have learned, (2) revealing modular structure within a model, and (3) tracking the development of circuits over training.
arXiv Detail & Related papers (2023-11-07T21:27:17Z) - Circuit Component Reuse Across Tasks in Transformer Language Models [32.2976613483151]
We present evidence that insights can indeed generalize across tasks.
We show that the process underlying both tasks is functionally very similar, and contains about a 78% overlap in in-circuit attention heads.
Our results provide evidence that it may yet be possible to explain large language models' behavior in terms of a relatively small number of interpretable task-general algorithmic building blocks and computational components.
arXiv Detail & Related papers (2023-10-12T22:12:28Z) - In-Context Convergence of Transformers [63.04956160537308]
We study the learning dynamics of a one-layer transformer with softmax attention trained via gradient descent.
For data with imbalanced features, we show that the learning dynamics take a stage-wise convergence process.
arXiv Detail & Related papers (2023-10-08T17:55:33Z) - Transformers as Statisticians: Provable In-Context Learning with
In-Context Algorithm Selection [88.23337313766353]
This work first provides a comprehensive statistical theory for transformers to perform ICL.
We show that transformers can implement a broad class of standard machine learning algorithms in context.
A emphsingle transformer can adaptively select different base ICL algorithms.
arXiv Detail & Related papers (2023-06-07T17:59:31Z) - Learning to Reason With Relational Abstractions [65.89553417442049]
We study how to build stronger reasoning capability in language models using the idea of relational abstractions.
We find that models that are supplied with such sequences as prompts can solve tasks with a significantly higher accuracy.
arXiv Detail & Related papers (2022-10-06T00:27:50Z) - Inductive Biases and Variable Creation in Self-Attention Mechanisms [25.79946667926312]
This work provides a theoretical analysis of the inductive biases of self-attention modules.
Our focus is to rigorously establish which functions and long-range dependencies self-attention blocks prefer to represent.
Our main result shows that bounded-norm Transformer layers create sparse variables.
arXiv Detail & Related papers (2021-10-19T16:36:19Z) - Inducing Transformer's Compositional Generalization Ability via
Auxiliary Sequence Prediction Tasks [86.10875837475783]
Systematic compositionality is an essential mechanism in human language, allowing the recombination of known parts to create novel expressions.
Existing neural models have been shown to lack this basic ability in learning symbolic structures.
We propose two auxiliary sequence prediction tasks that track the progress of function and argument semantics.
arXiv Detail & Related papers (2021-09-30T16:41:19Z) - Masked Language Modeling for Proteins via Linearly Scalable Long-Context
Transformers [42.93754828584075]
We present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR)
Our mechanism scales linearly rather than quadratically in the number of tokens in the sequence, is characterized by sub-quadratic space complexity and does not incorporate any sparsity pattern priors.
It provides strong theoretical guarantees: unbiased estimation of the attention matrix and uniform convergence.
arXiv Detail & Related papers (2020-06-05T17:09:16Z)
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.