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Generative AI Solutions

Navigating the Landscape: Strategies to Conquer Risks and Challenges in Developing Generative AI Solutions

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In the dynamic world of artificial intelligence (AI), generative models have surfaced as revolutionary instruments, offering prospects for creativity, problem-solving, and innovation. Capable of generating new content such as images, text, and even music, these models exhibit immense potential across diverse domains, spanning from art and entertainment to healthcare and finance. However, the development of generative AI solutions is not without its risks and challenges. Navigating this landscape requires a nuanced understanding of the technology, along with effective strategies to mitigate risks and overcome obstacles.

Risks and Challenges in Developing GenAI Solutions

1. LLM responses being sensitive to Prompts

A prompt, which can just be a few words or a whole paragraph, serves as an input to the LLM. The primary goal of the prompt is to furnish the AI model with adequate information to generate output that aligns with the prompt’s context. The way a prompt is formulated dictates the range of outputs an AI model can generate. Syntax of a prompt includes length of the prompt and ordering of examples that are given for ensuring that the LLM responds properly, and semantics of a prompt include choice and order of words, selection of examples and instructions specified. Both the syntax and the semantics of the prompt can greatly influence the model’s output. Therefore, the specific wording and arrangement of examples within a prompt have been observed to notably affect the behaviour of the model.

2. Hallucinations

Hallucination denotes a phenomenon in which the LLM model generates text that is erroneous, illogical, or fictitious. Unlike databases or search engines, LLMs do not disclose the source of their responses. Instead, they generate text by extrapolating from the provided prompt. LLMs hallucinate in different ways. Sometimes, the text generated by the LLM models might directly contradict with the input data given to the model such as prompts which may contain examples or retrieved context from the training data like shown in the figure below. These types of hallucinations are called as intrinsic hallucinations. In case of extrinsic hallucinations, we cannot verify the correctness of the output based on the provided source material because the source content may lack sufficient information to evaluate the output, resulting in an underdetermined situation which is also depicted in the figure below.

A white rectangular box with black text

Description automatically generated
A diagram of a robot

Description automatically generated

  Image Source-

3. Computational Challenges in Fine Tuning 

Pre-training large language models (LLMs) on extensive and varied textual datasets may pose a challenge as these models might encounter difficulty in explicitly capturing the distributional characteristics of task-specific datasets. To address this issue, fine-tuning methods are used which involves adapting the pre-trained model parameters by training it on smaller, task-specific datasets or by incorporating individual layers into the output representations. This proves fine-tuning to be highly effective for downstream tasks. 

However, LLMs with billions of parameters have large memory requirements to save model statistics like the model parameters, the gradients, the activations etc., This constraint restricts the LLM model fin-tuning process to institutions with substantial compute resources, hindering widespread access.

Strategies to Conquer the Risks and Challenges

1. Prompt Engineering

A prompt is a text input that is used to request the Large Language Model to perform a specific task. Prompt engineering is the process of asking the right question to the LLM model in order to get the best output from it. Despite its aim to emulate human behaviour, generative AI relies on precise instructions to generate outputs that are both high-quality and relevant.

Effective prompt engineering involves clear communication of essential content, structuring prompts meticulously by defining their role, context, and instructions. Specific, varied examples should be employed to guide the model’s focus and ensure accurate outcomes, while constraints help prevent deviations into factual inaccuracies. Complex tasks should be broken down into simpler prompts for better comprehension, and models should be instructed to evaluate their responses before generating them, using criteria like sentence limits or self-assessment scales. Creativity plays a pivotal role in prompt engineering and LLM interaction, recognizing the ongoing evolution of these technologies.

There are many different types of Prompt Engineering techniques that can be used to reduce biases and hallucinations of the Large Language Models and below are the 3 broad types of the same:

1. Direct Prompting: This method, also called Zero-shot prompting, involves providing only the instruction or question to the model without any examples or context.

2. Prompting with Examples (One-, Few-, and Multi-shot): One-shot prompting presents the model with a single descriptive example to imitate, while few- and multi-shot prompting offer the model multiple examples to learn from. This approach is useful for complex tasks and structured outputs that are challenging to describe solely through instruction.

3. Chain-of-Thought Prompting: Chain of Thought (CoT) prompting prompts the LLM to elucidate its thought process, particularly effective when combined with few-shot prompting for tasks requiring reasoning before generating a response.

In addition to the above techniques, explicitly prompting the LLM with specific instructions to eliminate hallucinations in the generated response has proven to be effective reducing hallucinations in many experiments/scenarios.

2. RAG implementation

RAG (Retrieval-Augmented Generation) is an AI framework designed to enhance large language models (LLMs) by accessing facts from appropriate knowledge bases. Its purpose is to ground LLMs in the most accurate and current information, offering insights into their generative process. It is a strategy that helps address both LLM hallucinations and out-of-date training data. RAG enables the optimization of LLM outputs with targeted information without altering the model itself. This information can be organization-specific, industry-specific, and more up-to-date than the LLM’s own knowledge. Initially introduced in a 2020 paper titled “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” by Patrick Lewis and Facebook AI Research, RAG has gained traction among both academic and industry researchers to enhance the contextual relevance and value of generative AI systems.

3. Opting new methodologies to enhance Fine tuning Data Quality

Quality of the training data has a direct impact on the performance of any and every machine learning model. Hence, ensuring the selection of good quality data has always been a necessity to reduce bias and over-fitting. When it comes to large language models, the impact of data quality holds true too but it is very difficult to assess the quality of the training data because of its sheer size! 

Research has been going on to find out suitable techniques to assess and select the appropriate training data and a very few have proven to be successful. Instructmining is one such novel technique that can be used to automatically select high-quality instruction data to refine large language models (LLMs). This entails the development of a data evaluator capable of assessing instruction data quality without human intervention.  The essential component of this method that helps in efficiently estimating  dataset quality is a data selector that can automatically identify the most appropriate subset of instruction data. Instructmining’s validity and scalability are affirmed through comparisons with other leading methods across diverse benchmarks. Notably, it boosts LLAMA-2-7B’s performance by 4.93 like specified on the Huggingface OPENLLM benchmark. Results indicate InstructMining’s proficiency in selecting high-quality samples from diverse instruction-following datasets, with models fine-tuned on InstructMining-selected datasets surpassing those on unfiltered datasets by 42.5% in specific instances.

In conclusion, the landscape of generative AI brims with promise and complexity. As we navigate its challenges, a nuanced understanding of technology and effective risk mitigation strategies are paramount. Embracing innovation while upholding ethical standards, we pave the way for transformative advancements across diverse domains. With continued diligence and collaboration, we can harness generative AI’s potential to shape a future that thrives on creativity, innovation, and responsible AI development.

Author Bio:
Vaishali is an AI/ML lead at Indium Software, a leading digital engineering company. She has 9+ years of experience in the advanced analytics domain. She manages a large data science team, oversees project planning and builds enterprise grade analytics models for various real-world usecases. She has been a speaker at many tech conferences. As a technology evangelist, Vaishali also coaches aspiring professionals on data science and machine learning topics like natural language processing, computer vision, deep learning, LLMs etc., She holds a professional postgraduate in Artificial Intelligence & Machine Learning. She is also a Google Women TechMakers Ambassador.


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