Light Dark 2024-04 · text · generative · anchor
Small language model family trained on heavily filtered and synthetic data, cited in the corpus at the Medium size and in the 3.5 point release at Mini and mixture-of-experts sizes.
Note the 3.5 releases are carried as variants rather than as a separate model, because the citing papers reference them against this same technical report Variants Phi-3 Medium , Phi-3.5 , Phi-3.5 Mini , Phi-3.5 MoE , Phi-3-Medium-128K-Instruct , Phi-3-Medium-4K-Instruct , Phi-3-mini Instruct , Phi-3-mini-128k-instruct , Phi-3-small , Phi-3-Vision-Instruct , Phi-3-3.8B-Instruct , Phi-3-3.8B-IT Findings IC-007 Most LLMs do not align closely with human moral preferences on multilingual trolley problems IC-009 LLM moral preferences show significant language sensitivity but not inequality toward low-resource languages IC-017 Truncating MLP weights improves few-shot Chain-of-Thought reasoning accuracy on GSM8K for Phi-3 and Llama-3.1-8B IC-110 Phi-3 (3.8B) and Llama-3 (8B) with triplet prompting outperform GPT-4 with pairwise prompting on causal graph orientation IC-245 Pretrained LLMs produce duration-dependent outputs that are incompatible with a discrete token interpretation IC-246 Pretrained LLMs assign coherent semantic meaning to linear interpolations between token embeddings, extending the linear embedding hypothesis to the output space IC-247 Pretrained LLMs are invariant to positional shifts but sensitive to duration scaling of the input IC-264 All 18 evaluated LLMs fail to abstain when the provided context lacks the answer, with performance gaps of 13.6% to 68.4% relative to the original context IC-265 Model families show extreme variation in detecting conflicting answers in inconsistent contexts, with phi-3 series at 5.8% average accuracy versus GPT-4 series at 89.35% IC-266 GPT-4o drops from 96.3% closed-book accuracy to 47.5% when given counterfactual context that contradicts its parametric knowledge, far below the 95% human accuracy on the same items IC-322 Past-tense reformulations of harmful requests bypass refusal training in eight released LLMs, while future-tense reformulations are substantially less effective IC-493 A linear direction in the input embedding space of Llama-2-7B-Chat, Llama-2-13B-Chat, Mistral-7B-Instruct-v0.3, and Phi-3-mini-128k predicts instruction-following success, generalizes across tasks but not instruction types, and can be used to improve adherence via representation engineering IC-549 All 18 evaluated LLMs show a 15-20% performance gap between linear (node chain) and graph (workflow) planning on WorfBench IC-565 Phi-3's residual stream encodes format instructions as linear directions, evidenced by cosine similarity and vocabulary-space projections IC-566 Adding instruction-specific steering vectors to the residual stream improves instruction-following accuracy for Phi-3, Gemma 2 2B IT, Mistral 7B IT, and Gemma 2 9B IT across format, length, and word-specific constraints IC-568 In Phi-3, word-exclusion steering vectors computed via difference-in-means project onto the vocabulary space with high logits for the excluded word, making them counterproductive Shared mechanisms Failure mode also 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 , 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-3.5 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 Linear representation also in BLOOM , Cambrian-1 , Chameleon , CLIP / CLIP-ViT (LC) , DINOv2 , EVA-CLIP , Falcon , Gemma , Gemma 2 , GPT-2 , GPT-J , HPSv2 , ImageBind , InstructBLIP , LanguageBind , LLaMA , Llama 2 / Llama 2 base , Llama 3 , Llama 3.1 , Llama-3.2-3B , LLaVA-1.5 / LLaVA-v1.5 , LLaVA-NeXT / LLaVA 1.6 , MAE , Mamba , Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1 , OLMo / OLMo base , OpenCLIP , PickScore , Pythia , Qwen2-VL , Qwen2.5 , ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN , SALMONN , SAM , SigLIP , TerraMind , Tulu 2 , Vicuna , ViT Positional bias also in BERT , ChatGPT , Claude 3 , Claude 3.5 , Falcon , Fuyu , Gemini , 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 , Pythia , Qwen1.5 , Qwen 2 , Qwen 2.5 72B Instruct , Stable Diffusion , Sybil , Vicuna Scale-dependent behaviour also in Aquila-7B , BEiT , BERT , BLOOM , Claude 2.1 , Claude 3 , Claude 3.5 , CLIP / CLIP-ViT (LC) , Code Llama , CodeGen , Cohere Command R , DeepSeek LLM , DeepSeekMoE , DeiT-III , DINO , DINOv2 , EquiformerV2 , ESCN , Falcon , FLAN-T5 , Gemini 1.0 Pro , 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 , GPT-Neo , I3D , Idefics , InternLM-2.5-7B , InternLM-XComposer2-VL , InternLM2 , InternVL-1.5 , InternVL2 , LLaMA , Llama 2 / Llama 2 base , Llama 3 , Llama 3.1 , Llama 3.2 , Llama-3.2-3B , LLaVA-1.5 / LLaVA-v1.5 , LLaVA-NeXT / LLaVA 1.6 , LongVA-7B , MAE , 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 , Moirai , MPT , MViT V2 , O1 / OpenAI-o1-preview , OLMo / OLMo base , OpenCLIP , OpenFlamingo , OpenLLaMA , OPT , PaLM 2 , Platypus2-Instruct-70B , Pythia , Qwen , Qwen1.5 , Qwen 2 , Qwen 2.5 72B Instruct , Qwen2-VL , Qwen-Audio , Qwen2.5 , RedPajama-INCITE , ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN , SlowFast , Solar 10.7B , Stable Diffusion , StableLM , Swin Transformer , T5 , TerraMind , text-ada-001 , TigerBot-70B-Chat , TimesFormer , TSM , Tulu 2 , Uniformer , Vicuna , VideoMAE , ViLA-8B , Wizardlm , X3D , XGLM , Xwin-LM-70B-v0.1 , Yi