Hallucination Attenuated Language and Vision Assistant(Halva) from Google
DSPy, TorchChat, Pearl, Lux, PyCaret, Pallas
Articles
HALVA (Hallucination Attenuated Language and Vision Assistant) approach involves specific modifications to the model architecture and the objective function to address hallucinations in multimodal large language models (LLMs).
Hallucination is itself a big problem in the LLM as they are prone to hallucinate and producing erroneous results for a given prompt. The new approach that Google proposed tries to mitigate this large limitation of the vanilla LLMs.
And Google wrote a detailed blog post on how this model architecture provides various advantages over other architectures mainly in suppressing the incorrect outputs and preventing the hallucination of the model.
HALVA operates within the framework of multimodal LLMs, which are designed to process and integrate both textual and visual inputs. These models typically consist of two main components:
Vision Encoder: This component processes visual inputs, such as images, to extract meaningful features. It often employs convolutional neural networks (CNNs) or vision transformers to capture spatial and semantic information from images.
Language Decoder: This component generates textual outputs based on the features extracted by the vision encoder. It usually involves transformer-based architectures that are adept at handling sequential data and generating coherent text.
No change in these components where it differentiates itself in contrastive learning and attention mechanism. It uses contrastive learning to distinguish accurate and hallucinated outputs in the system and attention mechanism to attenuate the hallucinated content and reduce the probability of the hallucinated content to occur.
Contrastive Layers: Additional layers that help in distinguishing between accurate and hallucinated outputs by comparing different representations.
Attention Mechanisms: Enhanced attention mechanisms that focus on aligning visual and textual features more precisely, reducing the chances of generating hallucinated content.
If we were to expand the contrastive learning objective, which is designed to minimize hallucinations by refining the model's output generation process. The objective function typically involves:
Positive and Negative Samples: During training, the model is exposed to both positive samples (accurate outputs) and negative samples (hallucinated outputs). The goal is to maximize the similarity between the model's predictions and the positive samples while minimizing the similarity with the negative samples.
Contrastive Loss: A contrastive loss function, such as the InfoNCE (Noise Contrastive Estimation) loss, is employed to optimize the model's ability to differentiate between positive and negative samples. This loss function encourages the model to produce outputs that are more aligned with the input data.
In order to ensure that model's general performance is not compromised, regularization techniques may be integrated into the objective function. These techniques help maintain the model's versatility and prevent overfitting to the training data which is common in LLM training techniques as well:
Dropout: Randomly dropping units from the neural network during training to prevent overfitting.
Weight Decay: Adding a penalty to the loss function based on the magnitude of the model's weights, encouraging simpler models that generalize better.
The training process involves iterative refinement on data, where the model is continuously exposed to new samples and updated based on its performance. This iterative approach ensures that the model gradually improves its accuracy and reduces hallucinations over time as well.
By reducing hallucinations, HALVA ensures that AI systems provide more accurate and reliable responses to input queries. This is crucial in customer service applications where incorrect or misleading information can lead to customer dissatisfaction. With HALVA, businesses can deploy chatbots and virtual assistants that deliver precise information, enhancing customer trust and engagement.
The improved accuracy in understanding and generating multimodal data allows businesses to offer more personalized experiences. For instance, in e-commerce, HALVA can help in generating personalized product recommendations based on both text and image inputs from customers, thereby improving conversion rates and customer satisfaction.
More information can be found in the paper more.
If problems are nails, and an LLM is your hammer, DSPy is like having an aimbot to hit the nails.
Isaac Miller provides an in-depth exploration of the DSPy framework, an open-source tool designed to enhance the use of large language models (LLMs) by structuring and optimizing their deployment in solving real-world problems. This summary will delve into the main thesis of the article and explore various themes that illustrate why DSPy is considered superior to alternative approaches.
DSPy stands for "Declarative Self-improving Language Programs" and represents a shift from traditional prompting techniques to a more structured programming paradigm. This approach allows for dynamic recompilation of LLM pipelines, tailored to the specific nuances of the task at hand, eliminating the need for continuous manual prompt adjustments.
TLDR of the article:
DSPy offers a structured, programming-centric approach to using LLMs, which significantly enhances their reliability and effectiveness in solving complex tasks. Unlike traditional methods that rely heavily on manual prompt engineering, DSPy automates and optimizes the process, allowing for more efficient and scalable programming approach than some of the other competition.
If you are exploring different solutions and you are seeking for the last push to decide on the DSPy, this could be the last article that could make the cut for you.
DSPy forces users to employ verifiable feedback mechanisms to evaluate the effectiveness of LLM outputs. This can involve comparing outputs to a ground truth or using LLMs to assess the quality of responses. By providing a structured way to evaluate performance, DSPy ensures that LLMs are used effectively to solve real problems, rather than generating unstructured or unsustainable outputs.
If you forego some of the flexibility when you are building and deploying LLMs, and create more “repeatable” patterns, the programming approach might be a good tradeoff in developing the LLMs.
Advantages
DSPy introduces a Pythonic syntax for instructing LLMs, making it intuitive for developers familiar with Python programming. This syntax allows for the composition of modules that are both declarative and composable, streamlining the development of LLM-based applications.
Other key advantage of DSPy is its ability to automate prompt tuning and optimize LLM performance. By translating user-defined natural language signatures into complete instructions and examples, DSPy reduces the need for intricate prompt crafting and manual adjustments, enhancing the adaptability and efficiency of LLM-based solutions.
DSPy distinguishes itself from other frameworks like LangChain and LlamaIndex by focusing on a programming-first mentality. While LangChain excels at breaking down complex problems into manageable chunks and LlamaIndex enhances data retrieval capabilities, DSPy shines by minimizing manual prompt crafting and allowing for fine-tuning of models to achieve specific goals.
The DSPy programming model is built around three fundamental components: Signatures, Modules, and Teleprompters. These elements abstract prompting and fine-tuning techniques, automate prompting for arbitrary pipelines and improves reproducibility, which also improves engineering iteration speed and engineering experience.
Libraries
# Output
usage: torchchat [-h] {chat,browser,generate,export,eval,download,list,remove,where,server} ...
positional arguments:
{chat,browser,generate,export,eval,download,list,remove,where,server}
The specific command to run
chat Chat interactively with a model via the CLI
generate Generate responses from a model given a prompt
browser Chat interactively with a model in a locally hosted browser
export Export a model artifact to AOT Inductor or ExecuTorch
download Download model artifacts
list List all supported models
remove Remove downloaded model artifacts
where Return directory containing downloaded model artifacts
server [WIP] Starts a locally hosted REST server for model interaction
eval Evaluate a model via lm-eval
options:
-h, --help show this help message and exittorchchat is a small codebase showcasing the ability to run large language models (LLMs) seamlessly. With torchchat, you can run LLMs using Python, within your own (C/C++) application (desktop or server) and on iOS and Android.
Pearl is a new production-ready Reinforcement Learning AI agent library open-sourced by the Applied Reinforcement Learning team at Meta. Furthering our efforts on open AI innovation, Pearl enables researchers and practitioners to develop Reinforcement Learning AI agents. These AI agents prioritize cumulative long-term feedback over immediate feedback and can adapt to environments with limited observability, sparse feedback, and high stochasticity. We hope that Pearl offers the community a means to build state-of-the-art Reinforcement Learning AI agents that can adapt to a wide range of complex production environments.
Lux is a Python library that facilitate fast and easy data exploration by automating the visualization and data analysis process. By simply printing out a dataframe in a Jupyter notebook, Lux recommends a set of visualizations highlighting interesting trends and patterns in the dataset. Visualizations are displayed via an interactive widget that enables users to quickly browse through large collections of visualizations and make sense of their data.
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.
In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, Hyperopt, Ray, and few more.
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models.
Lazy Predict helps build a lot of basic models without much code and helps understand which models works better without any parameter tuning.
Pyforest you can use all your favorite Python libraries without importing them before. If you use a package that is not imported yet, pyforest imports the package for you and adds the code to the first Jupyter cell. If you don't use a library, it won't be imported.
Pallas is an extension to JAX that enables writing custom kernels for GPU and TPU. It aims to provide fine-grained control over the generated code, combined with the high-level ergonomics of JAX tracing and the jax.numpy API.