Gemini

text, image · generative · anchor

Variants

Gemini Pro, Gemini 2.0 Flash, Gemini Flash, Gemini Nano 1, Gemini Pro Vision

Findings

• IC-128Released VLMs exhibit sycophancy, agreeing with incorrect user opinions while ignoring visual evidence, with LLaVA-1.5 showing the highest rate (94.6%) and InternLM-XComposer2-VL-1.8B the lowest (28.8%)
• IC-155All 13 evaluated MLLMs perform at or near random guessing on MediConfusion, with confusion scores often exceeding 90%, indicating they cannot distinguish visually dissimilar radiology image pairs
• IC-156Gemini models show substantially lower confusion scores than other MLLMs yet still perform at or near random guessing, suggesting their bottleneck is medical knowledge or reasoning rather than visual encoding
• IC-257327 DNNs approach or exceed human accuracy on object depth order but are near chance on VPT-basic, while humans show the opposite pattern
• IC-30756 LLMs on Sorry-Bench show fulfillment rates ranging from below 10% (Claude-2, Gemini-1.5) to above 90% (Mistral-7B-instruct-v0.1, Dolphin-2.6-mixtral-8x7b), with GPT-4o at 30% and Llama-3-70B at 35%
• IC-324Fine-tuning Gemini Nano 1 on 8 memorization examples causes it to override in-context predictions with in-weight predictions in 2 of 8 cases, while the base model always follows in-context predictions
• IC-415VLMs show a default shape bias (47.9-73.8%) that exceeds their vision encoders and vision-only models but falls short of human levels (96%), with the LLM component rather than the encoder responsible for suppressing one visual cue.
• IC-416Natural language prompts can steer the texture/shape bias in VLMs in both directions without significantly affecting accuracy, with texture-biased prompts more effective than shape-biased ones; this steering also generalizes to low/high-frequency bias.
• IC-485LLMs show a significant performance gap between Wikipedia-based factual multi-hop QA and counterfactual multi-hop QA, indicating reliance on memorized knowledge rather than reasoning from context
• IC-486LLMs achieve correct final answers through incorrect reasoning chains, inflating their apparent multi-step reasoning performance
• IC-487Including sub-questions in the prompt improves LLM performance on multi-hop QA tasks
• IC-488LLM performance degrades progressively as the number of reasoning hops increases, with error propagation from earlier sub-questions
• IC-518GPT-4o achieves 62.54% overall accuracy on MMWorld, the best among 15 MLLMs, while four open-source models perform below the 26.31% random-choice baseline
• IC-519MLLMs exhibit different skill sets than humans, correctly answering expert-level questions that all three human annotators miss while failing on easy questions humans answer correctly
• IC-520Temporal reasoning performance drops significantly across all 15 MLLMs when video frames are shuffled or reduced to one-fifth of the original count
• IC-521MLLMs show asymmetric modality-specific perception, with Gemini Pro achieving 69.97% on visual-only questions but only 24.45% on audio-only, while Video-Chat outperforms ChatUniVi on audio despite worse visual scores
• IC-571Open-source VLMs (LLaVA, MiniGPT-4, InstructBLIP) are substantially more vulnerable to multimodal jailbreak attacks than Gemini-1.5-flash, with BAP attack ASR of 58–62% versus 40–41%
• IC-586Symbolic distance (number of reasoning steps) is the primary bottleneck for relational reasoning in LLMs, not total context length
• IC-587Real-world knowledge acts as a shortcut in LLM relational reasoning, causing worse-than-chance performance on logically valid but factually incongruent statements
• IC-588Topologically ordered context improves relational reasoning over random ordering across nearly all LLMs
• IC-589Flavor text (non-essential descriptive language) degrades relational reasoning in most LLMs, but GPT-4o is robust to it
• IC-595Gemini-pro has knowledge gaps on specific topics (Permian extinction, Fordism) causing it to perform far below its average rank on existing benchmarks

Shared mechanisms

• Failure modealso in AASIST, ADM, Aegis-Guard-Defensive, Alpaca, AnyLoc, AutoTikZ / DataTikZ, Baichuan, Baichuan 2, Baichuan2-13B, BakLLaVA, Bard, BEiT, BERT, BingChat, BLIP, BLIP-2, BLOOM, BSRNet, CF2, Chat-UniVi-7B, ChatGLM-6B / ChatGLM-6b-2, ChatGLM2, ChatGPT, CLAP, Claude 1.3, Claude 2.0, Claude 2.1, Claude 3, Claude 3.5, CLEAR, CLIP / CLIP-ViT (LC), CLIP4Clip, CLIPBERT, CLIPCap, CLMBR-T-BASE, CloFNet, Code Llama, CodeGeex2, CodeGen, CodeLlama-13B, CodeLlama-34B, CogVLM2, Cohere Command R, CoMEt, Command R+, CONCH, CycleGAN, DALL-E, DALL·E 2, DALL·E 3, DASR, DECAF, DeepSeek-2-Chat, DeepSeek-2-Coder, DeepSeek-V2-0628, DeepSeek-VL, DeepSeek-VL2, DeiT, DeiT-III, Depth Anything, DETR-R50, DimeNet++, DINO, DINO-FocalNet-Large, DINOv2, EGNN, Emu2, EquiformerV2, ESCN, ESM-2, ESM3, ESRGAN, EVA-CLIP, EVE, Falcon, Faster R-CNN / Faster R-CNN R50 / Faster R-CNN X101, FLAN-T5, Florence-2, FLUX / FLUX1, FoveaBox, Fuyu, Galactica-6.7B, GAT, 3D Gaussian Splatting, GCN, Gemini 1.5 / Gemini Pro 1.5, Gemma, Gemma 2, GIN, GLIDE, GLM-4, GLM-4V, GloVe, GP-UNIT, GPT-2, GPT-3 / GPT base, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4.1, GPT-4o, GPT-J, GPT-NeoX-20B, GraphSAGE, Grounding DINO, Guanaco, GVP, Hawkeye, HiFaceGAN, HPS, HuggingGPT, IDDPM, Idefics, Idefics2, ImageBind, ImageBind-LLM-7B, Imagen Video, ImageReward, 12-in-1, InstructBLIP, InstructGPT, InternLM-2.5-7B, InternLM-XComposer2-VL, InternVideo, InternVL-1.5, InternVL2, Koala, LegalBERT, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, Llama 3.2, Llama-3.2-3B, Llama-3-2-Vision, LLaMA-Adapter v2, Llama Guard, Llama Guard 2, Llama-Guard 3, Llama-VID, LLaVA, LLaVA-1.5 / LLaVA-v1.5, LLaVA-Med, LLaVA-NeXT / LLaVA 1.6, LLaVA-OneVision, LongVA-7B, LOVT, LWM-1M-JAX, MACE, MAE, Med-Flamingo, Merlot Reserve, MGCA, MiDaS, MiniCPM-V, MiniGPT-4, Mip-Splatting, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mistral Large 2, Mistral Large V2, Mixtral, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, MobileNetV2, Molmo, MolmoE-7B, Momentor, Moondream2, Moonshot-v1-8k, mPLUG-2, mPLUG-Owl, mPLUG-Owl3, mPLUG-Owl2, MPT, MSA Transformer, MultiBERTs, Nova Canvas, Nova Lite, Nova Pro, O1 / OpenAI-o1-preview, O3, O4-mini, OLMo / OLMo base, OneLLM, OpenAI Moderation, OpenChat-3.5-0106, OpenCLIP, OpenFlamingo, OPT, Otter, Otter-7B, PaLM 2, PaLM 62B, PandaGPT-7B, PerSAM, Phi-3, Phi-3.5 Mini Instruct, PickScore, PLIP, Prismatic, ProGen-2, Pythia, Qwen1.5, Qwen 2, Qwen 2.5 72B Instruct, Qwen2-VL, Qwen-Audio, Qwen-VL, Qwen2.5, Qwen2-Audio, R2D2, RadFM, RCExplainer, RD-SR, RealESRNet, Reprover, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, RetinaNet-R50, RivaGAN, RS-LDS, RT-1, RT-2, SALMONN, SAM, SAM 2, SAULLM 54B, Scaffold-GS, SchNet, Seed-LLaMA-8B, SGC, SIREN, Sketch Transformer, SLD-max, SLD-medium, SLD-strong, SLDS, SLIMG, SpeechGPT, SphereNet, SRResNet, Stable Diffusion, StackLLaMA, Starcoder, StegaStamp, StyleGAN2-ADA, Swin Transformer, T5, TD-MPC, TerraMind, TimeChat, TranceptionEVE, TreeRing, Tulu 2, UnifiedQA, UniPerceiver, UNITER, UniVL, Van Gogh Diffusion, VERA, VGG / VGG13, Vicuna, Video-Chat-7B, Video-ChatGPT, Video-LLaMA, Video-LLaMA-2-13B, Video-LLaVA, VideoCLIP, ViLA-8B, ViLBERT, VindLU, VioLET, ViRTex, ViT, ViV1T, VTG-LLM, WildGuard, Wizardlm, X-CLIP, X-InstructBLIP-7B, XGen-MM, Xlm-R, Zephyr-7B-beta
• Positional biasalso in BERT, ChatGPT, Claude 3, Claude 3.5, Falcon, Fuyu, Gemini 1.5 / Gemini Pro 1.5, Gemma, Gemma 2, GLIDE, GPT-2, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4.1, GPT-4o, GPT-J, InstructGPT, LLaMA, Llama 2 / Llama 2 base, Llama 3, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mixtral, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, MPT, O1 / OpenAI-o1-preview, O3, O4-mini, PaLM 2, Phi-3, Pythia, Qwen1.5, Qwen 2, Qwen 2.5 72B Instruct, Stable Diffusion, Sybil, Vicuna
• Shortcutalso in BakLLaVA, BLIP-2, Claude 3, Claude 3.5, CLIP / CLIP-ViT (LC), CLIP4Clip, CLIPBERT, DALL·E 2, DALL·E 3, DeepSeek-VL2, Eurus-RM-7B, Falcon, FLUX / FLUX1, Gemini 1.5 / Gemini Pro 1.5, Gemma, Gemma 2, GPT-2, GPT-3 / GPT base, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4o, GPT-J, iFlytekSpark-13B, InstructBLIP, Internlm2-Reward, InternVideo, InternVL2, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama-3-2-Vision, LLaVA, LLaVA-1.5 / LLaVA-v1.5, LLaVA-Med, LLaVA-NeXT / LLaVA 1.6, Med-Flamingo, Merlot Reserve, MiniGPT-4, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mixtral, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, Molmo, mPLUG-2, mPLUG-Owl3, Nova Canvas, O1 / OpenAI-o1-preview, OpenCLIP, OPT, Otter, PaLM 62B, Pythia, Qwen, Qwen 2, Qwen-VL, Qwen2.5, RadFM, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, Stable Diffusion, Swin Transformer, Sybil, TerraMind, Tulu 2, UniPerceiver, UniVL, Vicuna, Video-LLaMA, VideoCLIP, VindLU, VioLET, X-CLIP