Why Language Models Hallucinate

Why Language Models Hallucinate

The paper "Why Language Models Hallucinate" (arXiv:2509.04664) posits that hallucinations in large language models (LLMs) are not a mysterious phenomenon but a predictable outcome of their training and evaluation pipeline. The authors argue that these systems are incentivized to guess when uncertain, much like a student facing a difficult exam, because current procedures reward plausible-sounding answers over admissions of uncertainty. Hallucinations originate from simple binary classification errors during pretraining, where the model cannot distinguish incorrect statements from facts, leading to their creation through "natural statistical pressures." This behavior persists and is reinforced by evaluation benchmarks that are graded in a way that makes guessing a beneficial strategy for improving test scores. The authors contend that this "epidemic" of penalizing uncertainty requires a "socio-technical mitigation": modifying the scoring of dominant, misaligned industry benchmarks to steer the field toward developing more trustworthy and reliable AI systems.

Core Thesis: Hallucinations as an Incentivized Behavior

The central argument is that LLM hallucinations are a direct consequence of a system that rewards guessing and penalizes uncertainty. Current training and evaluation procedures actively reward guessing over acknowledging uncertainty, creating statistical pressure to guess. This behavior undermines trust in even state-of-the-art models. Think about this for a second, rightnow, pretty much every major AI evaluation setups reward for honesty or admitting you don't know something is zero.

Analogy: 

The authors compare LLMs to "students facing hard exam questions," who "sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty."

Root Cause: 

The training and evaluation procedures are identified as the primary drivers, effectively encouraging models to generate answers even when their confidence is low.

The Two-Stage Problem of Hallucinations

The paper outlines a two-part cause for the prevalence of hallucinations, covering both their initial creation and their subsequent reinforcement.

1. Statistical Origins in Pretraining: 

Hallucinations are framed as originating from fundamental statistical processes during the model's training, rather than being an esoteric flaw.

• Binary Classification Errors: The paper asserts that hallucinations "originate simply as errors in binary classification."

• Natural Statistical Pressure: If a model, during its training, is unable to effectively distinguish incorrect statements from factual ones, "hallucinations in pretrained language models will arise through natural statistical pressures."

2. Reinforcement Through Evaluation:
The persistence of hallucinations is attributed to the methods used to evaluate and rank LLMs. The current evaluation landscape optimizes models to be high-scorers on tests, where guessing is an effective strategy.

• Optimization for "Test-Taking": Models are "optimized to be good test-takers, and guessing when uncertain improves test performance."

• "Epidemic" of Penalizing Uncertainty: The common practice of grading benchmarks in a way that punishes uncertain or non-committal responses is described as an "epidemic." This evaluation structure is seen as a key reason why hallucinations persist.

Proposed Mitigation Strategy:
The authors advocate for a systemic change in how the AI community evaluates models, labeling their proposed solution a "socio-technical mitigation."

• Focus on Existing Benchmarks: The core recommendation is to modify the scoring of existing benchmarks that are "misaligned but dominate leaderboards."

• Rejection of Add-on Solutions: This approach is presented as a more effective alternative to simply "introducing additional hallucination evaluations."

• Ultimate Goal: By changing the incentive structure at the evaluation level, this change "may steer the field toward more trustworthy AI systems."
Publication Details


Paper Title: Why Language Models Hallucinate
Authors: Adam Tauman Kalai, Ofir Nachum, Santosh S. Vempala, Edwin Zhang
Identifier: arXiv:2509.04664 [cs.CL]
Subject: Computation and Language (cs.CL)
Submission Date: Thu, 4 Sep 2025 21:26:31 UTC