Jean-Pierre Falet

jean-pierre.falet@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e

Yoshua Bengio

Sujets de recherche

Apprentissage profond

Apprentissage sur graphes

Modèles probabilistes

Raisonnement

Sécurité de l'IA

Site web

Google Scholar

GitHub

Publications

Latent Veracity Inference for Identifying Errors in Stepwise Reasoning

Oliver E. Richardson

Sungsoo Ahn

Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can cont… (voir plus)ain inaccurate statements that reduce performance and trustworthiness. To address this, we propose to augment each reasoning step in a CoT with a latent veracity (or correctness) variable. To efficiently explore this expanded space, we introduce Veracity Search (VS), a discrete search algorithm over veracity assignments. It performs otherwise intractable inference in the posterior distribution over latent veracity values by leveraging the LM's joint likelihood over veracity and the final answer as a proxy reward. This efficient inference-time verification method facilitates supervised fine-tuning of an Amortized Veracity Inference (AVI) machine by providing pseudo-labels for veracity. AVI generalizes VS, enabling accurate zero-shot veracity inference in novel contexts. Empirical results demonstrate that VS reliably identifies errors in logical (ProntoQA), mathematical (GSM8K), and commonsense (CommonsenseQA) reasoning benchmarks, with AVI achieving comparable zero-shot accuracy. Finally, we demonstrate the utility of latent veracity inference for providing feedback during self-correction and self-improvement.

2026-01-25

International Conference on Learning Representations (poster)

doi.org

openreview.net

Delta-AI: Local Objectives for Amortized Inference in Sparse Graphical Models

Jean-Pierre Falet

Hae Beom Lee

Nikolay Malkin

Chen Sun

Dragos Secrieru

Dinghuai Zhang

Guillaume Lajoie