Humans or LLMs as the Judge? A Study on Judgement Biases

Adopting human and large language models (LLM) as judges (\textit{a.k.a} human- and LLM-as-a-judge) for evaluating the performance of existing LLMs has recently gained attention. Nonetheless, this approach concurrently introduces potential biases from human and LLM judges, questioning the reliability of the evaluation results. In this paper, we propose a novel framework for investigating 5 types of biases for LLM and human judges. We curate a dataset with 142 samples referring to the revised Bloom's Taxonomy and conduct thousands of human and LLM evaluations. Results show that human and LLM judges are vulnerable to perturbations to various degrees, and that even the most cutting-edge judges possess considerable biases. We further exploit their weakness and conduct attacks on LLM judges. We hope that our work can notify the community of the vulnerability of human- and LLM-as-a-judge against perturbations, as well as the urgency of developing robust evaluation systems.

ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model

Recent advancements in Large Vision-Language Models (LVLMs) have enabled processing of multimodal inputs in language models but require significant computational resources for deployment, especially in edge devices. This study aims to bridge the performance gap between traditional-scale LVLMs and resource-friendly lite versions by adopting high-quality training data. To do this, a synthetic dataset is created by leveraging GPT-4V's ability to generate detailed captions, complex reasoning instructions and detailed answers from images. The resulted model trained with our data, ALLaVA, achieves competitive performance on 12 benchmarks up to 3B LVLMs. This work highlights the feasibility of adopting high-quality data in crafting more efficient LVLMs. Our online demo is available at \url{https://allava.freedomai.cn}.

Silkie: Preference Distillation for Large Visual Language Models

This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual context. We first build a vision-language feedback (VLFeedback) dataset utilizing AI annotation. Specifically, responses are generated by models sampled from 12 LVLMs, conditioned on multi-modal instructions sourced from various datasets. We adopt GPT-4V to assess the generated outputs regarding helpfulness, visual faithfulness, and ethical considerations. Furthermore, the preference supervision is distilled into Qwen-VL-Chat through the direct preference optimization (DPO) method. The resulting model Silkie, achieves 6.9% and 9.5% relative improvement on the MME benchmark regarding the perception and cognition capabilities, respectively. Silkie also demonstrates reduced hallucination by setting a new state-of-the-art score of 3.02 on the MMHal-Bench benchmark. Further analysis shows that DPO with our VLFeedback dataset mainly boosts the fine-grained perception and complex cognition abilities of LVLMs, leading to more comprehensive improvements compared to human-annotated preference datasets.

MLLM-Bench, Evaluating Multi-modal LLMs using GPT-4V

In the pursuit of Artificial General Intelligence (AGI), the integration of vision in language models has marked a significant milestone. The advent of vision-language models (MLLMs) like GPT-4V have expanded AI applications, aligning with the multi-modal capabilities of the human brain. However, evaluating the efficacy of MLLMs poses a substantial challenge due to the subjective nature of tasks that lack definitive answers. Existing automatic evaluation methodologies on multi-modal large language models rely on objective queries that have standard answers, inadequately addressing the nuances of creative and associative multi-modal tasks. To address this, we introduce MLLM-Bench, an innovative benchmark inspired by Vicuna, spanning a diverse array of scenarios, including Perception, Understanding, Applying, Analyzing, Evaluating, and Creation along with the ethical consideration. MLLM-Bench is designed to reflect user experience more accurately and provide a more holistic assessment of model performance. Comparative evaluations indicate a significant performance gap between existing open-source models and GPT-4V. We posit that MLLM-Bench will catalyze progress in the open-source community towards developing user-centric vision-language models that meet a broad spectrum of real-world applications. See online leaderboard in \url{https://mllm-bench.llmzoo.com}.

HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs

Adapting a language model into a specific domain, a.k.a `domain adaption', is a common practice when specialized knowledge, e.g. medicine, is not encapsulated in a general language model like Llama2. The challenge lies in the heterogeneity of data across the two training stages, as it varies in languages, genres, or formats. To tackle this and simplify the learning protocol, we propose to transform heterogeneous data, from the both pre-training and supervised stages, into a unified, simple input-output pair format. We validate the new protocol in the domains where proprietary LLMs like ChatGPT perform relatively poorly, such as Traditional Chinese Medicine. The developed model, HuatuoGPT-II, has shown state-of-the-art performance in Chinese medicine domain on a number of benchmarks, e.g. medical licensing exams. It even outperforms proprietary models like ChatGPT and GPT-4 in some aspects, especially in Traditional Chinese Medicine. Expert manual evaluations further validate HuatuoGPT-II's advantages over existing LLMs. Notably, HuatuoGPT-II was benchmarked in a fresh Chinese National Medical Licensing Examination where it achieved the best performance, showcasing not only its effectiveness but also its generalization capabilities.