Large Language Models (LLMs) have static knowledge, making updates costly and time-consuming. Retrieval-augmented generation (RAG) helps, but irrelevant info can degrade performance.
Solution: Retrieval Augmented Iterative Self-Feedback (RA-ISF) refines RAG by breaking tasks into subtasks. It uses: 1. Task Decomposition: Splits tasks into subtasks. 2. Knowledge Retrieval: Fetches relevant info for each subtask. 3. Response Generation: Integrates info to generate accurate answers. What’s Next: RA-ISF reduces hallucinations and boosts performance, enhancing LLMs for complex tasks. As it evolves, expect more powerful, knowledge-enhanced LLMs.
Read the full research paper.
The Transformer model – Explained
The Transformer model, introduced in the paper “Attention Is All You Need,” revolutionised natural language processing (NLP) by enabling highly efficient training and inference using attention mechanisms. Here’s an explanation focusing on both training and inference phases, with particular emphasis on inference.
Transformer Training
1. Model Architecture:
• Encoder-Decoder Structure: The Transformer consists of an encoder and a decoder, each composed of multiple layers.
• Attention Mechanisms:
• Self-Attention: Each position in the sequence attends to all other positions in the same sequence to capture dependencies.
• Multi-Head Attention: Multiple self-attention mechanisms run in parallel to capture different types of dependencies.
• Feed-Forward Neural Networks: Positioned after attention mechanisms to further process the attended information.
2. Training Process:
• Input Preparation:
• Tokenization: Splitting text into tokens (words or subwords).
• Embedding: Converting tokens into dense vectors.
• Positional Encoding: Adding positional information to embeddings to account for the order of tokens.
• Forward Pass:
• Encoder: Processes the input sequence, generating a set of context-aware representations.
• Decoder: Uses the encoder’s output along with the target sequence (shifted right) to generate predictions.
• Loss Calculation: Comparing the model’s predictions to the actual target sequence using a loss function, typically cross-entropy.
• Backpropagation: Updating the model parameters to minimize the loss.
• Optimization: Using optimization algorithms like Adam to adjust weights based on gradients.
Transformer Inference
Inference in the Transformer model is the process of using the trained model to generate predictions or translations from new input data. This is particularly crucial in applications like machine translation, text generation, and summarization.
Key Steps in Transformer Inference
1. Input Encoding:
• The input sequence is tokenized and embedded, similar to the training process.
• Positional encodings are added to the embeddings.
2. Encoder Pass:
• The input embeddings are processed through the encoder layers to generate encoded representations.
• Self-attention mechanisms capture dependencies within the input sequence.
3. Decoder Initialization:
• The decoder starts with a special start-of-sequence token (e.g., ).
• Initial hidden states are set up, often including context from the encoder’s output.
4. Iterative Decoding:
• Step-by-Step Generation: The decoder generates the output sequence one token at a time.
• Self-Attention and Encoder-Decoder Attention:
• The decoder’s self-attention focuses on previously generated tokens.
• The encoder-decoder attention layer attends to the encoder’s output, incorporating contextual information from the input sequence.
• Output Token Prediction: At each step, the decoder outputs a probability distribution over the vocabulary.
• Token Selection: The next token is selected based on the highest probability (greedy search) or using techniques like beam search to explore multiple paths and select the most likely sequence.
5. Termination:
• The process continues until a special end-of-sequence token (e.g., ) is generated or a maximum length is reached.
Inference Techniques
• Greedy Search: Selects the token with the highest probability at each step. Simple and fast but may not always yield the best results.
• Beam Search: Keeps multiple hypotheses at each step, exploring several paths to find the most likely sequence. Balances quality and computational efficiency.
• Sampling: Randomly samples tokens based on their probabilities. Useful for generating diverse and creative outputs.
Advantages of Transformer Inference
• Parallelization: Unlike RNNs, the Transformer’s architecture allows for parallel processing of tokens, making both training and inference faster.
• Handling Long Dependencies: The self-attention mechanism effectively captures long-range dependencies in the data.
• Scalability: Transformers scale well with increased data and model sizes, improving performance on large datasets.
Applications of Transformer Inference
• Machine Translation: Translating text from one language to another.
• Text Generation: Generating coherent and contextually relevant text.
• Summarization: Creating concise summaries of longer documents.
• Question Answering: Providing accurate answers to questions based on given contexts.
Transformers have become the foundation for many state-of-the-art NLP models, such as BERT, GPT, and T5, due to their powerful attention mechanisms and scalability.
Alibaba Presents AlphaMath
Alibaba Group introduces AlphaMath, an AI approach using Monte Carlo Tree Search (MCTS) to enhance large language models’ capabilities in mathematical reasoning. The method automates the generation of training data and improves model accuracy on complex mathematical problems without manual annotation. Read the full article.