InstructEval
Leaderboard
Instructed language models (ILMs) play a crucial role in natural language processing, generating text that aligns with specific prompts or instructions and serving as a versatile tool for diverse applications.
Problem: Evaluating instructed language models presents challenges due to the abundance of available models in the market. The scattered nature of relevant information across various online sources further complicates the evaluation process.
Solution: To address this, we have developed the instructeval leaderboard—a solution designed to assist researchers in identifying outstanding instructed language models. These rankings facilitate informed decision-making in selecting the most suitable instructed language models.
| Rank | Model | Size | Architecture | Organization | Score  A weighted average of the adoption and capability score of the model. |
| #1 | FLAN-UL2 | 100B | T5 | Google AI | 51.6 |
| #2 | Alpaca Lora | 65B | Transformer-XL | Stanford University | 50 |
| #3 | Open Assistant | 30B | Switch Transformer | LAION | 46.5 |
| #4 | StableVicuna | 13B | LLaMA Transformer | StabilityAI | 40.9 |
| #5 | Guanaco | 65B | Instruction-following Language | UW NLP group | 39.2 |
| #6 | OPT-IML | 30B | OPT | Meta AI | 38.7 |
| #7 | ChatGLM | 6B | Transformer | Tsinghua University | 33.2 |
| #8 | Falcon-7b-instruct | 7B | Causal Decoder-only | TII | 22.2 |
| #9 | TK-Instruct | 11B | Encoder and Decoder | Allen Institute for Artificial Intelligence (AI2) | 26.8 |
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