A Survey on Multimodal Large Language Models

Multimodal Large Language Model (MLLM) recently has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of MLLM, such as writing stories based on images and OCR-free math reasoning, are rare in traditional methods, suggesting a potential path to artificial general intelligence. In this paper, we aim to trace and summarize the recent progress of MLLM. First of all, we present the formulation of MLLM and delineate its related concepts. Then, we discuss the key techniques and applications, including Multimodal Instruction Tuning (M-IT), Multimodal In-Context Learning (M-ICL), Multimodal Chain of Thought (M-CoT), and LLM-Aided Visual Reasoning (LAVR). Finally, we discuss existing challenges and point out promising research directions. In light of the fact that the era of MLLM has only just begun, we will keep updating this survey and hope it can inspire more research. An associated GitHub link collecting the latest papers is available at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.Comment: Project page:https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model

Woodpecker: Hallucination Correction for Multimodal Large Language Models

Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content. In order to mitigate hallucinations, existing studies mainly resort to an instruction-tuning manner that requires retraining the models with specific data. In this paper, we pave a different way, introducing a training-free method named Woodpecker. Like a woodpecker heals trees, it picks out and corrects hallucinations from the generated text. Concretely, Woodpecker consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Implemented in a post-remedy manner, Woodpecker can easily serve different MLLMs, while being interpretable by accessing intermediate outputs of the five stages. We evaluate Woodpecker both quantitatively and qualitatively and show the huge potential of this new paradigm. On the POPE benchmark, our method obtains a 30.66%/24.33% improvement in accuracy over the baseline MiniGPT-4/mPLUG-Owl. The source code is released at https://github.com/BradyFU/Woodpecker.Comment: 16 pages, 7 figures. Code Website: https://github.com/BradyFU/Woodpecke

Local Tournament Incentives and Firm Risk

Using the compensation gap between a CEO and the highest-paid CEO in the same Metropolitan Statistical Area (MSA) as a proxy for local tournament incentives, I document a positive relation between local tournament incentives and firm risk. Specifically, CEOs who face higher local incentives implement riskier policies, including higher R&D expenditures and less diversification. Exploiting quasi-shocks to local incentives and cross-sectional variation in the probability of winning, I show that the incentive effects vary systematically with theoretical predictions. The results are robust to alternative local tournament incentives measures, sample periods, and firm risk proxies

Theoretical and Experimental Investigations of Tunable Microwave Signal Generation Based on a 1-GHz All-Polarization-Maintaining Mode-Locked Fiber Laser

Photonics-based microwave generation brings the advantages of photonic oscillators, such as high stability, wide bandwidth, and low loss, to the microwave domain. In this paper, the generation of tunable microwave signals was investigated both theoretically and experimentally based on an all-polarization-maintaining 1-GHz mode-locked fiber laser. Based on beating between two highly chirped optical pulse trains with a relative time delay at the photodetector, tunable microwave signals could be obtained. The numerical simulations show that 40 GHz or higher microwave signals could be obtained by tuning the time delay and dispersion. To experimentally validate the theoretical model, the generation of tunable microwave signals from 2–4 GHz was demonstrated. Due to the utilization of polarization-maintaining devices, the optical output has a high degree of linear polarization of more than 99%, which verifies the enhanced system stability. These demonstrations are imperative for solidifying the advancements of recent years and could promote the utilization of photonics-based microwave generation in microwave photonics

Theoretical and Experimental Investigations of Tunable Microwave Signal Generation Based on a 1-GHz All-Polarization-Maintaining Mode-Locked Fiber Laser

Photonics-based microwave generation brings the advantages of photonic oscillators, such as high stability, wide bandwidth, and low loss, to the microwave domain. In this paper, the generation of tunable microwave signals was investigated both theoretically and experimentally based on an all-polarization-maintaining 1-GHz mode-locked fiber laser. Based on beating between two highly chirped optical pulse trains with a relative time delay at the photodetector, tunable microwave signals could be obtained. The numerical simulations show that 40 GHz or higher microwave signals could be obtained by tuning the time delay and dispersion. To experimentally validate the theoretical model, the generation of tunable microwave signals from 2–4 GHz was demonstrated. Due to the utilization of polarization-maintaining devices, the optical output has a high degree of linear polarization of more than 99%, which verifies the enhanced system stability. These demonstrations are imperative for solidifying the advancements of recent years and could promote the utilization of photonics-based microwave generation in microwave photonics