Hallucination

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 752 papers with code (1,816 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
  8 Mar 2020 16 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.
• HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
  23 Oct 2023 9 repositories listed Syntology ran 2 of 8 samples · 6 unverified · 1 pointer-only (licence)
  Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs.
• ReAct: Synergizing Reasoning and Acting in Language Models
  6 Oct 2022 9 repositories listed Syntology ran 15 of 34 samples · 19 unverified · 5 pointer-only (licence)
  While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.
• Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
  28 Nov 2023 7 repositories listed Syntology ran 9 of 14 samples · 5 unverified · 1 pointer-only (licence)
  Large Vision-Language Models (LVLMs) have advanced considerably, intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned.
• Evaluating Object Hallucination in Large Vision-Language Models
  17 May 2023 6 repositories listed Syntology ran 6 of 12 samples · 6 unverified · 4 pointer-only (licence)
  Despite the promising progress on LVLMs, we find that LVLMs suffer from the hallucination problem, i.
• RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
  27 May 2024 5 repositories listed Syntology ran 14 of 22 samples · 8 unverified · 8 pointer-only (licence)
  Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models.
• Retrieval-Augmented Generation for Large Language Models: A Survey
  18 Dec 2023 4 repositories listed
  Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.
• RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback
  1 Dec 2023 4 repositories listed
  Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding, reasoning, and interaction.
• Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
  26 Jun 2023 4 repositories listed
  To efficiently measure the hallucination generated by LMMs, we propose GPT4-Assisted Visual Instruction Evaluation (GAVIE), a stable approach to evaluate visual instruction tuning like human experts.
• Projected Distribution Loss for Image Enhancement
  16 Dec 2020 4 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  More explicitly, we show that in imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on…
• Pushing the Limits of Low-Resource Morphological Inflection
  16 Aug 2019 4 repositories listed
  Recent years have seen exceptional strides in the task of automatic morphological inflection generation.
• Im2Flow: Motion Hallucination from Static Images for Action Recognition
  12 Dec 2017 4 repositories listed
  Second, we show the power of hallucinated flow for recognition, successfully transferring the learned motion into a standard two-stream network for activity recognition.
• Evolutionary thoughts: integration of large language models and evolutionary algorithms
  9 May 2025 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Large Language Models (LLMs) have unveiled remarkable capabilities in understanding and generating both natural language and code, but LLM reasoning is prone to hallucination and struggle with complex, novel scenarios,…
• A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges
  4 Jan 2025 3 repositories listed
  Multimodal Vision Language Models (VLMs) have emerged as a transformative topic at the intersection of computer vision and natural language processing, enabling machines to perceive and reason about the world through…
• Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making
  9 Oct 2024 3 repositories listed Syntology ran 7 of 7 samples · 0 unverified
  We aim to evaluate Large Language Models (LLMs) for embodied decision making.
• SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning
  10 Aug 2024 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  With support of over 300+ LLMs and 50+ MLLMs, SWIFT stands as the open-source framework that provide the most comprehensive support for fine-tuning large models.
• AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models
  16 Jun 2024 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  This motivates the development of AutoHallusion, the first automated benchmark generation approach that employs several key strategies to create a diverse range of hallucination examples.
• Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
  12 Feb 2024 3 repositories listed Syntology ran 1 of 5 samples · 4 unverified · 4 pointer-only (licence)
  Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning; adoption that has fueled a wealth of new models such as…
• MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
  29 Jan 2024 3 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  In this work, we propose a simple yet effective training strategy MoE-Tuning for LVLMs.
• RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
  31 Dec 2023 3 repositories listed Syntology ran 3 of 5 samples · 2 unverified
  Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs).
• Evaluating Hallucinations in Chinese Large Language Models
  5 Oct 2023 3 repositories listed Syntology ran 4 of 4 samples · 0 unverified
  We analyze the primary types of hallucinations in different types of models and their causes.
• Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
  15 Jul 2023 3 repositories listed Syntology ran 11 of 16 samples · 5 unverified · 15 pointer-only (licence)
  Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning.
• HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
  19 May 2023 3 repositories listed Syntology ran 3 of 12 samples · 9 unverified
  Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.
• Multimodal Chain-of-Thought Reasoning in Language Models
  2 Feb 2023 3 repositories listed Syntology ran 6 of 11 samples · 5 unverified · 1 pointer-only (licence)
  Experimental results on ScienceQA and A-OKVQA benchmark datasets show the effectiveness of our proposed approach.
• Dataset Distillation via Factorization
  30 Oct 2022 3 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  In this paper, we study \xw{dataset distillation (DD)}, from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD…
• On hallucinations in tomographic image reconstruction
  1 Dec 2020 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.
• DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
  18 Jun 2025 2 repositories listed Syntology ran 1 of 19 samples · 18 unverified · 19 pointer-only (licence)
  To address this, we introduce a new task, Discourse-level text Scene Graph parsing (DiscoSG), supported by our dataset DiscoSG-DS, which comprises 400 expert-annotated and 8, 430 synthesised multi-sentence caption-graph…
• LettuceDetect: A Hallucination Detection Framework for RAG Applications
  24 Feb 2025 2 repositories listed
  Retrieval Augmented Generation (RAG) systems remain vulnerable to hallucinated answers despite incorporating external knowledge sources.
• PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language Model
  21 Jan 2025 2 repositories listed
  To address this, we identify two critical sets of visual tokens that facilitate the transfer of visual information from the vision encoder to the LLM.
• FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
  17 Oct 2024 2 repositories listed
  Summarization is one of the most common tasks performed by large language models (LLMs), especially in applications like Retrieval-Augmented Generation (RAG).

Syntology lines on 21 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.