Attractor basins in transformer hidden states unify conflict and hallucination as basin competition or absence, with geometric margin outperforming entropy for detection and a scaling law governing confident hallucination rates.
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Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Canonical reference. 82% of citing Pith papers cite this work as background.
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abstract
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this paper, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research.
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representative citing papers
Foundation models serve as effective oracles for detecting refactoring correctness issues in Java programs, achieving up to 93.8% accuracy in zero-shot evaluations on 226 real bugs.
Counterfactual Routing awakens dormant experts in MoE models via layer-wise perturbation and a new CEI metric, raising factual accuracy 3.1% on average across TruthfulQA, FACTOR, and TriviaQA without extra inference cost.
LLMs achieve 81% coherent execution simulation on HumanEval but show mostly random or weak consistency across tests, with frontier models relying on natural language shortcuts instead of true program analysis.
A study of seven LLMs finds that realistic prompt variations such as one-character misspellings trigger library hallucinations in up to 26% of cases, fabricated names in up to 99%, and time-based prompts in up to 85%, and introduces LibHalluBench for evaluation.
Proposes CSR task and HalluSegBench using visual counterfactuals to diagnose segmentation hallucinations in VLMs, plus RobustSeg via counterfactual fine-tuning that reduces hallucinations by 30% on FP-RefCOCO.
NESA presents a neuro-symbolic framework that decomposes static analyses into policy-defined sub-problems solved by parsers and LLMs to enable compilation-free customizable analysis with reduced hallucinations.
Hallucinations are inevitable in LLMs because they cannot learn all computable functions according to learning theory.
HallusionBench shows GPT-4V reaches only 31.42% accuracy on paired questions testing language hallucination and visual illusion in LVLMs, with other models below 16%.
A dual hierarchical RL framework with two agents coordinates high-level dialogue strategy and low-level question generation to emulate judicial questioning and extract key information from Supreme Court arguments, outperforming baselines.
Task context suppresses factual correction in LLMs at the response-selection stage even when the model has encoded the error, and two training-free interventions raise correction rates substantially.
A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.
Large reasoning models show measurable hidden-state dynamics that a new statistic can use to distinguish correct reasoning trajectories without labels.
Adaptive Unlearning suppresses package hallucinations in code-generating LLMs by 81% while preserving benchmark performance, using model-generated data and no human labels.
LLM token rank-frequency distributions converge to a shared Mandelbrot distribution across models and domains, enabling a microsecond-scale statistical primitive for provenance verification and black-box anomaly triage.
FP16 KV caching in transformers causes deterministic token divergence versus cache-free inference due to non-associative floating-point accumulation orderings.
LTS-FS locates hallucination-relevant layers in LVLMs via causal attribution on a constructed dataset and applies sparse layerwise feature steering to mitigate hallucinations while preserving general task performance.
Embedding geometry on the unit hypersphere distinguishes detectable query-proximate unfaithfulness and confabulations from undetectable factual errors sharing vocabulary with correct answers.
ALL-FEM fine-tunes LLMs on a corpus of verified FEniCS scripts and uses multi-agent workflows to automate finite element code generation, achieving 71.79% success on 39 benchmarks across elasticity, flow, and coupled problems.
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
LoVeC uses RL to train LLMs to output verbalized numerical confidence scores for statements in long-form text, achieving better calibration than self-consistency baselines on QA datasets while being 20x faster.
Mixed-methods study maps downstream developers' concerns, practices, and challenges with AI failures in PTM-based software.
Process supervision via RAG-Gym produces more reliable and generalizable search agents, with gains driven by higher-quality queries on out-of-domain multi-hop tasks.
Search-o1 integrates agentic retrieval-augmented generation and a Reason-in-Documents module into large reasoning models to dynamically supply missing knowledge and improve performance on complex science, math, coding, and QA tasks.
citing papers explorer
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Hallucinations are inevitable in LLMs because they cannot learn all computable functions according to learning theory.
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HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
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Spatiotemporal Hidden-State Dynamics as a Signature of Internal Reasoning in Large Language Models
Large reasoning models show measurable hidden-state dynamics that a new statistic can use to distinguish correct reasoning trajectories without labels.
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LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning
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The Surprising Universality of LLM Outputs: A Real-Time Verification Primitive
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The Illusion of Equivalence: Systematic FP16 Divergence in KV-Cached Autoregressive Inference
FP16 KV caching in transformers causes deterministic token divergence versus cache-free inference due to non-associative floating-point accumulation orderings.
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Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation
LTS-FS locates hallucination-relevant layers in LVLMs via causal attribution on a constructed dataset and applies sparse layerwise feature steering to mitigate hallucinations while preserving general task performance.
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A Geometric Taxonomy of Hallucinations in LLMs
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ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods
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Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
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LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generations
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Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
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Aligning Large Multimodal Models with Factually Augmented RLHF
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Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization
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IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation
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Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning
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Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs
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