TL;DR
This research project asks whether an open-weights model can be trained to cite references that actually exist, using RL against an automatic citation verifier.
Large language models (LLMs) and large multimodal models (LMMs) are increasingly applied in scientific dialogue, but it remains unclear whether they can reliably ground specific dialogue statements to paper-based evidence. A central challenge is paper-grounded hallucination under a paper-as-truth setting: statements th...
This work proposes CounterfactualLVLM, a training-free and plug-and-play framework that mitigates object hallucinations via small-model-assisted counterfactual reasoning and highlights the power of counterfactual guidance as a simple yet effective paradigm for enhancing factual grounding in LVLM-based multi-modal reaso...
Video question answering systems built on vision-language models often produce timestamped claims with high confidence even when unsupported by the cited frame. This deceptive hallucination arises because timestamps imply grounding without ensuring correctness, increasing user trust but not accuracy. We introduce a pip...
Probing can be a cost-effective triage mechanism for routing LLM answers to human review and quality control procedures in high-stakes financial applications and shows that among confident answers, those for which all eight resamples agree, 15-23% are wrong on FinQA.
HallDetect, a lightweight, reference-free, and black-box framework for hallucination detection, is presented, a lightweight, reference-free, and black-box framework for hallucination detection that is evaluated not only on summarization but across a broader range of source-grounded generation settings.
An integrated conceptual frame-work that couples attention- and perturbation-based explainability with lightweight hallucination-detection signals and token-efficient inference strategies is presented, and a set of cross-cutting consistency metrics are instrumented with a set of cross-cutting consistency metrics.
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