Search Results
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that appears factual but is ungrounded.
SynCode: LLM Generation with Grammar Augmentation
We present SynCode, a novel framework for efficient and general syntactical decoding with LLMs, to address this challenge.
BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer
BatGPT is a large-scale language model designed and trained jointly by Wuhan University and Shanghai Jiao Tong University.
Granite Guardian
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM).
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e. g., coming up with the right arguments for calling routines), requiring a deeper comprehension of complex file interactions.
Cognitive Mirage: A Review of Hallucinations in Large Language Models
Our contribution are threefold: (1) We provide a detailed and complete taxonomy for hallucinations appearing in text generation tasks; (2) We provide theoretical analyses of hallucinations in LLMs and provide existing detection and improvement methods; (3) We propose several research directions that can be developed in the future.
Agent AI: Surveying the Horizons of Multimodal Interaction
To accelerate research on agent-based multimodal intelligence, we define "Agent AI" as a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data, and can produce meaningful embodied actions.
A Survey on Hallucination in Large Vision-Language Models
In this comprehensive survey, we dissect LVLM-related hallucinations in an attempt to establish an overview and facilitate future mitigation.
PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
This paper contributes a ChatGPT-Prompted visual hallucination evaluation Dataset (PhD) for objective VHE at a large scale.
Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
This procedure is not perfect and can cause the model to hallucinate - provide answers that do not accurately reflect the image, even when the core LLM is highly factual and the vision backbone has sufficiently complete representations.
