Guilhem Fouilhé

Summary

We formalize and implement an LLM-based conversational explanation interface for planning, and test it with a user study.

Abstract

When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elicitation process, where the human’s role is to guide the AI planner according to their preferences and expertise. In this context, explanations that respond to users’ questions are crucial to improve their understanding of potential solutions and increase their trust in the system. To enable natural interaction with such a system, we present a multi-agent Large Language Model (LLM) architecture that is agnostic to the explanation framework and enables user- and context-dependent interactive explanations. We also describe an instantiation of this framework for goal-conflict explanations, which we use to conduct a user study comparing the LLM-powered interaction with a baseline template-based explanation interface.

Summary

This is a demo paper about a platform for iterative planning with interactive goal-conflict explanations. My most notable contribution is the design of a multi-agent LLM architecture to present explanations to users through a chat interface.

Abstract

When automating plan generation for a real-world sequential decision problem, the objective is often not to replace the human planner, but rather to facilitate an iterative reasoning and elicitation process, where the human’s role is to guide the planner according to their preferences and expertise. In this context, explanations that address users’ questions are crucial to improve their understanding of potential solutions and increase their trust in the system. We present a platform that implements this iterative planning approach and provides explanations to user questions based on conflicting goals and preferences. The platform supports both a classical template-based interface and a multi-agent Large Language Model (LLM) architecture that enables interactive explanations tailored to the user and context. The integration of online user studies allows for the evaluation of the effectiveness of the explanations and the impact of the communication interface.

Summary

We formalize and implement an LLM-based conversational explanation interface for planning.

Contribution

This is my first first-author paper!

Abstract

When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate the tedious work. In an iterative pro- cess, the human’s role is to guide the planner according to their preferences and expert experience. Explanations that re- spond to users’ questions are crucial to increase trust in the system and improve understanding of the sample solutions. To enable natural interaction with such a system, we present an explanation framework agnostic architecture for interac- tive natural language explanations that enables user and con- text dependent interactions. We propose conversational inter- faces based on Large Language Models (LLMs) and instan- tiate the explanation framework with goal-conflict explana- tions. As a basis for future evaluation, we provide a tool for domain experts that implements our interactive natural lan- guage explanation architecture.

Summary

We investigate how transformer models learn linear functions through in-context learning, challenging common assumptions about their algorithmic capabilities.

Contribution

This work provides new insights into the limitations of in-context learning by studying a controlled setup with synthetic data.

Abstract

We explore in-context learning (ICL), a popular paradigm for inference with Large Language Models (LLMs), in a controlled experimental setup using synthetic training data. Using a range of small transformer models trained from scratch, we focus on a mathematical task with simple yet precise prompts: learning a linear function f from a sequence of inputs and their corresponding function values . Our findings challenge the prevailing narrative that transformers adopt algorithmic approaches like linear regression to in-context learn (ICL) a linear function. We observe that all models have “boundary values” that limit generalizability. While we can extend boundary values with training distributions over a wider range, we lose the precision of models trained on distributions with more restricted ranges. Thus, we see a dilemma for ICL at least in some tasks: either models will lack generalizability or precision.

Summary

We present a mathematical framework to systematically investigate neural dynamics in a hierarchical perceptual system, showing how stability and wave propagation properties emerge from predictive coding principles.

Contribution

This paper provides a rigorous mathematical analysis of hierarchical neural networks with predictive coding dynamics, revealing conditions for stability and characterizing wave propagation properties.

Abstract

Sensory perception (e.g., vision) relies on a hierarchy of cortical areas, in which neural activity propagates in both directions, to convey information not only about sensory inputs but also about cognitive states, expectations and predictions. At the macroscopic scale, neurophysiological experiments have described the corresponding neural signals as both forward and backward-travelling waves, sometimes with characteristic oscillatory signatures. It remains unclear, however, how such activity patterns relate to specific functional properties of the perceptual apparatus. Here, we present a mathematical framework, inspired by neural network models of predictive coding, to systematically investigate neural dynamics in a hierarchical perceptual system. We show that stability of the system can be systematically derived from the values of hyper-parameters controlling the different signals (related to bottom-up inputs, top-down prediction and error correction). Similarly, it is possible to determine in which direction, and at what speed neural activity propagates in the system. Different neural assemblies (reflecting distinct eigenvectors of the connectivity matrices) can simultaneously and independently display different properties in terms of stability, propagation speed or direction. We also derive continuous-limit versions of the system, both in time and in neural space. Finally, we analyze the possible influence of transmission delays between layers, and reveal the emergence of oscillations.

Summary

We study the asymptotic behavior of nonlocal bistable reaction-diffusion equations.

Abstract

In this paper, we study the asymptotic behavior of the solutions of nonlocal bistable reaction-diffusion equations starting from compactly supported initial conditions. Depending on the relationship between the nonlinearity, the interaction kernel and the diffusion coefficient, we show that the solutions can either: propagate, go extinct or remain pinned. We especially focus on the latter regime where solutions are pinned by thoroughly studying discontinuous ground state solutions of the problem for a specific interaction kernel serving as a case study. We also present a detailed numerical analysis of the problem.

Summary

We review the application of predictive coding - a popular neuroscience framework - to deep neural networks.

Contribution

This work was produced for TIR (Travaux d’Initiation `a la Recherche) course in M1 Computer Science of UPS during year 2021/2022.

Abstract

Predictive Coding is a popular framework in neurosciences for explaining cortical function. In this model, higher-level cortical areas try to predict lower-level neural activity and prediction errors are passed back to higher layers. Deep Neural Networks (DNN) , which use brain-inspired architecture, could be augmented with such a model, providing robustness and a better understanding of spatio-temporal dependencies. We investigate research in this direction and give a quick review on tasks in which Predictive Coding (PC) for DNN has demonstrated its interest, with a strong emphasis on vision-related tasks.