Let’s Connect at Generative AI Summit London 2024
The progress in Generative AI technologies is ceaseless, bolstered by fresh data sources, meticulously curated training and testing datasets, and model refinement via reinforcement learning from human feedback (RLHF) procedures.
RLHF in generative AI leverages human insights, including domain-specific expertise, for behavioral optimization and accurate output generation. Fact-checking from domain experts ensures the model’s responses are not only contextually relevant but also trustworthy. Shaip provides accurate data labeling, credential domain experts, and evaluation services, enabling seamless integration of human intelligence into the iterative fine-tuning of Large Language Models.
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Optimizing Gen AI Models with Curated Data & Human Feedback
Dataset
Generation
Utilize prompt generation with LLMs to augment existing datasets & improve model coverage on diverse topics, ensuring robust performance.
Data
Annotation
Engage subject matter experts to refine, and annotate unstructured data sources into structured formats suitable for ML algorithms.
Model Refinement with RLHF
Fine-tune AI models by integrating ongoing human review into model development through an iterative process of evaluation & refinement to optimize output.
Quality Output Assessment
Experts perform audit and quality control to validate and ratify the outputs of Generative AI systems.
Shaip offers Generative AI services tailored to advance your business solutions:
Data Collection for Fine-Tuning LLMs
We gather and curate data to refine language models for precision and accuracy.
Domain-Specific Text Creation
Our service creates specialized text for sectors like legal and medical to train your domain-focused AI.
Toxicity Assessment
Our approach uses flexible scales to measure and reduce toxic content in AI-generated communications accurately.
Model Validation & Tuning Services
We assess gen AI results for quality across markets and languages to fine-tune AI to align with market-specific needs through RLHF.
Prompt Creation/Fine-Tuning
We craft and optimize natural language prompts to mirror diverse user interactions with your AI.
Answer Quality Comparison
Our extensive network enables a thorough comparison of AI answers to enhance model accuracy and dependability.
Likert Scale Appropriateness
Our tailored feedback ensures that AI responses have the appropriate tone & brevity for specific user scenarios.
Correctness Evaluation
We rigorously evaluate AI-generated content to ensure it is factual and realistic to prevent the spread of misinformation.
Generative AI Use Cases
Question & Answering Pairs
Create Question-Answer pairs by thoroughly reading large documents (Product Manuals, Technical Docs, Online forums & Reviews, Industry Regulatory Documents) to enable companies to develop Gen AI by extracting the relevant info from a large corpus. Our experts create high-quality Q&A pairs such as:
» Q&A pairs with multiple answers
» Creation of surface level questions (Direct data extraction from reference Text)
» Create deep level questions (Correlate with facts & insights not given in reference text)
» Query Creation from Tables
Text Summarization
Our experts can summarize the entire conversation or long dialogue by inputting concise and informative summaries of large volumes of text data.
Image Captioning
Transform how you interpret images with our advanced AI-powered Image Captioning service. We breathe life into images by generating precise and contextually rich descriptions, opening up new ways for your audience to interact and engage with your visual content more effectively.
Audio Generation
Train models with a large dataset of audio recordings with various sounds, such as music, speech, and environmental sounds, to generate audio, such as music, podcasts, or audio books.
Caption
The main soundtrack of an arcade game. It is fast-paced and upbeat, with a catchy electric guitar riff. The music is repetitive and easy to remember, but with unexpected sounds, like cymbal crashes or drum rolls.
Generated audio
Speech Recognition
Train models that understand spoken language, i.e., applications, such as voice-activated assistants, dictation software, and real-time translation based on a large dataset of audio recordings of speech with corresponding transcripts.
Training Text-to-Speech Services
We offer a large dataset of audio recordings of human speech to train AI models to create natural, engaging voices for your applications, offering your users a unique and immersive auditory experience.
LLM Datasets Evaluation with Human Rating & QA Validation
In the world of machine learning, ensuring that a model understands and generates human-like text based on given prompts is paramount. This process involves rigorous dataset evaluation through human rating and quality assurance (QA) validation. Evaluators critically assess the prompt-response pairs in a dataset and rate the relevance and quality of the responses generated by a Language Learning Model (LLM).
LLM Datasets Comparison with Human Rating & QA Validation
Dataset comparison involves meticulous analysis of various response options for a single prompt. The objective is to rank these responses from best to worst based on their relevance, accuracy, and alignment with the context of the prompt.
Synthetic Dialogue Creation
Synthetic Dialogue Creation harnesses the power of Generative AI to revolutionize chatbot interactions and call center conversations. By leveraging AI’s capacity to delve into extensive resources such as product manuals, technical documentation, and online discussions, chatbots are equipped to offer precise and relevant responses across a myriad of scenarios. This technology is transforming customer support by providing comprehensive assistance for product inquiries, troubleshooting issues, and engaging in natural, casual dialogues with users, thereby enhancing the overall customer experience.
Image Summarization, Rating & Validation
Image Summarization, Rating & Validation within the realm of Generative AI involves sophisticated machine learning models that curate and assess images, generating accurate summaries and quality ratings. Human feedback is crucial in this process as it helps to fine-tune the AI’s accuracy, ensuring the generated content meets the nuanced expectations and standards that only human judgment can provide, thereby enhancing the reliability of AI outputs.
