Dissecting the Ledger: Locating and Suppressing "Liar Circuits" in Financial Large Language Models

TL;DR

This paper identifies and suppresses specific circuits causing hallucinations in financial LLMs, improving their reliability in arithmetic reasoning by targeting late-layer aggregation mechanisms.

Contribution

It introduces a mechanistic approach using causal tracing to locate and mitigate liar circuits in GPT-2 XL for financial tasks, revealing a universal geometry of deception.

Findings

Suppressing Layer 46 reduces hallucinations by 81.8%

A linear probe generalizes to unseen topics with 98% accuracy

Identifies a dual-stage mechanism for arithmetic reasoning in LLMs

Abstract

Large Language Models (LLMs) are increasingly deployed in high-stakes financial domains, yet they suffer from specific, reproducible hallucinations when performing arithmetic operations. Current mitigation strategies often treat the model as a black box. In this work, we propose a mechanistic approach to intrinsic hallucination detection. By applying Causal Tracing to the GPT-2 XL architecture on the ConvFinQA benchmark, we identify a dual-stage mechanism for arithmetic reasoning: a distributed computational scratchpad in middle layers (L12-L30) and a decisive aggregation circuit in late layers (specifically Layer 46). We verify this mechanism via an ablation study, demonstrating that suppressing Layer 46 reduces the model's confidence in hallucinatory outputs by 81.8%. Furthermore, we demonstrate that a linear probe trained on this layer generalizes to unseen financial topics with 98% accuracy, suggesting a universal geometry of arithmetic deception.