Tag: gpt

• Tell Your Model Where to Attend Post-hoc Attention Steering for LLMs (08 Nov 2023)
  

  This is my reading note for Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs. This paper proposes to improve LLM instruction follow performance by changes the attention weight to emphasize contents highlighted by user. The attention head to model is found by profiling the model on a small scale set of data.

• TEAL Tokenize and Embed ALL for Multi-modal Large Language Models (06 Nov 2023)
  

  This is my reading note for TEAL: Tokenize and Embed ALL for Multi-modal Large Language Models. This paper proposes a method of adding multi modal input and output capabilities to the existing LLM. To this end, it utilizes VQVAE and whisper to tokenize the image and audio respectively. Only The embedded and projection layer is trained . The result is not SOTA.

• GPT-Fathom Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (04 Nov 2023)
  

  This is my reading note for GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond. This paper evaluates several LLMs and found 1) openAI’s GPT significantly outperformed all other competitors and Claude 2 is #2; 2) techniques like SFT and RLHF benefits smaller models most; 3) as the model evolves, some metric may slightly degrade.

• NExT-GPT Any-to-Any Multimodal LLM (16 Sep 2023)
  

  This is my reading note for NExT-GPT: Any-to-Any Multimodal LLM. This paper proposes a multiple modality model which could takes multiple modalities as input and output in multiple modalities as well. The paper leverage existing large language model, multiple modality encoder image bind) and multiple modality diffusion model. To Amish the spice of those components, a simple linear projection is used for input and transformer to the output.

• Language Is Not All You Need Aligning Perception with Language Models (25 Jun 2023)
  

  This is my reading note for Language Is Not All You Need: Aligning Perception with Language Models. This paper proposes a multimodal LLM which feeds the visual signal as a sequence of embedding, then combines with text embedding and trains in a GPT like way.

• SEED-Bench Benchmarking Multimodal LLMs with Generative Comprehension (23 Jun 2023)
  

  This is my reading note for SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension. This paper proposes a benchmark suite of modality LLM. It introduces how is the data created and how is the task derived. For evaluation, it utilizes the model’s output of likelihood of answers instead of directly on text answers.

• Scaling Laws for Generative Mixed-Modal Language Models (22 Jun 2023)
  

  This is my reading note for Scaling Laws for Generative Mixed-Modal Language Models. This paper provides a study of scaling raw on dataset size and model size in multimodality settings.

• MEGAVERSE Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks (20 Jun 2023)
  

  This is my reading note for MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks. This paper proposes a new multilingual benchmark to test LLM and provides very limited dataset for multimodality. The language distribution is also strange which houses to much on south, Asia. Overall GPT and Palm get the best performance.