Representing Rule-based Chatbots with Transformers

• URL: http://arxiv.org/abs/2407.10949v2
• Date: Wed, 12 Feb 2025 15:18:32 GMT
• Title: Representing Rule-based Chatbots with Transformers
• Authors: Dan Friedman, Abhishek Panigrahi, Danqi Chen,
• Abstract summary: We propose using ELIZA as a setting for formal, mechanistic analysis of Transformers. We first present a theoretical construction of a Transformer that implements ELIZA. We then conduct a set of empirical analyses of Transformers trained on synthetically generated ELIZA conversations.
• Score: 35.30128900987116
• License:
• Abstract: What kind of internal mechanisms might Transformers use to conduct fluid, natural-sounding conversations? Prior work has illustrated by construction how Transformers can solve various synthetic tasks, such as sorting a list or recognizing formal languages, but it remains unclear how to extend this approach to a conversational setting. In this work, we propose using ELIZA, a classic rule-based chatbot, as a setting for formal, mechanistic analysis of Transformer-based chatbots. ELIZA allows us to formally model key aspects of conversation, including local pattern matching and long-term dialogue state tracking. We first present a theoretical construction of a Transformer that implements the ELIZA chatbot. Building on prior constructions, particularly those for simulating finite-state automata, we show how simpler mechanisms can be composed and extended to produce more sophisticated behavior. Next, we conduct a set of empirical analyses of Transformers trained on synthetically generated ELIZA conversations. Our analysis illustrates the kinds of mechanisms these models tend to prefer--for example, models favor an induction head mechanism over a more precise, position-based copying mechanism; and using intermediate generations to simulate recurrent data structures, akin to an implicit scratchpad or Chain-of-Thought. Overall, by drawing an explicit connection between neural chatbots and interpretable, symbolic mechanisms, our results provide a new framework for the mechanistic analysis of conversational agents.

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