6. LLM Benchmarking - Intro

 

Undergrad's Guide to LLM Benchmarks: Grading the AI Superstars!

Hey Undergrads! Welcome back to the thrilling world of AI! We've explored some cool LLM (Large Language Model) concepts, but how do we measure their success? Today, we'll delve into LLM Benchmarking – imagine a way to test and compare different LLMs, like giving exams to different students to see who performs best! But unlike your usual exams, LLM benchmarks use specific metrics to measure how well LLMs perform different tasks.

Think of it this way:

• You're in a baking competition. Judges evaluate your entries based on taste, texture, and appearance. LLM benchmarking is similar – it uses metrics to evaluate different LLMs on specific tasks, like writing different creative text formats or translating languages.

Here's the LLM Benchmarking Breakdown:

• The LLM Arena: Imagine a competition where different LLMs showcase their abilities.
• The Benchmarking Tests: These are standardized tests designed to evaluate LLMs on specific tasks. There are benchmarks for tasks like question answering, text summarization, and even code generation.
• The All-Important Metrics: Just like scores in your exams, metrics are used to measure LLM performance in these benchmarks. These metrics can be:
  • Accuracy: How often does the LLM generate the correct answer or perform the task flawlessly?
  • Fluency: How natural and coherent is the text generated by the LLM?
  • Relevance: Does the LLM response stay on topic and address the user's query effectively?

Feeling Inspired? Let's See Real-World LLM Benchmarks:

• The GLUE Benchmark: This benchmark tests LLMs on their ability to understand and answer questions based on factual passages. A high accuracy metric on GLUE indicates the LLM can effectively comprehend information and answer questions in a well-structured format.

• The SuperGLUE Benchmark: This builds upon GLUE, adding more complex tasks like question entailment (does question B necessarily follow from question A?) Here, metrics focus on how well the LLM grasps the relationships between different pieces of information.

• The LAMBADA Benchmark: This benchmark focuses on how well LLMs can hold engaging and informative dialogues with humans. Metrics like fluency and relevance become crucial here, assessing how natural the conversation feels and how well the LLM stays on topic.

Important Note: There's no single "perfect" metric for LLM benchmarking. The choice of metrics depends on the specific task and the desired outcome. Additionally, benchmarks are constantly evolving as new challenges are presented to LLMs.

So next time you hear about a groundbreaking LLM, remember the power of LLM benchmarking! It's like creating a testing ground to evaluate these AI systems and push them to become better, more effective language models. (Although, unlike your exams, LLMs probably wouldn't get stressed about benchmark results... yet!).

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Some popular LLM benchmarks include the Open LLM Leaderboard, MMLU, HELM, and AlpacaEval 2.0, which are designed to evaluate various aspects of language models' abilities[1]. However, it's important to note that the relative performance of LLMs on these benchmarks can be sensitive to minor perturbations, such as changing the order of choices or the method of answer selection[2]. This highlights the need for more robust evaluation schemes.

For code generation tasks, existing benchmarks may not be comprehensive enough, as LLMs can show a significant drop in performance when evaluated on evolved benchmarks that cover different targeted domains[3]. This suggests that overfitting to existing benchmarks can occur, making it crucial to evaluate LLMs on a diverse range of problems.

In the context of STEM educational technology, LLMs have been evaluated on the standardized Physics GRE examination to understand their risks and limitations[4]. Additionally, the GitHub Recent Bugs (GHRB) dataset has been introduced to facilitate continued research on LLM-based debugging applications, as details about LLM training data are often not made public[5].

These benchmarks and datasets provide a diverse set of evaluation tools for LLMs, covering various aspects of their abilities and potential applications.

Citations:

[1] https://arxiv.org/abs/2402.14992

[2] https://arxiv.org/abs/2402.01781

[3] https://arxiv.org/abs/2403.19114

[4] https://arxiv.org/abs/2312.04613

[5] https://arxiv.org/abs/2310.13229

5. Hands on ChatBot

 

Building AUCSE Undergrad Student ChatBOT in Just 10 Minutes

https://ametodl.blogspot.com/p/openai-backendfrontend-using-python-in.html

4.33. Retrieval

 

Undergrad's Guide to LLM's Information Hunt: Retrieval - Finding the Facts to Power the Text

Hey Undergrads! Welcome back to the exciting world of LLMs (Large Language Models)! We've explored some cool LLM concepts like generating different creative text formats and translation. But where do LLMs get all that information? Today, we'll delve into Retrieval in LLMs – imagine an LLM with a built-in research assistant, able to find and access the information it needs to complete tasks, like a student hitting the library before writing a paper!

Think of it this way:

• You're writing a research paper. Retrieval is like having a super-powered research assistant who can find all the relevant books, articles, and data you need to support your arguments.

• In the LLM world, Retrieval allows LLMs to access and retrieve information from vast external sources like text databases, code repositories, or even the real-world web (with proper safeguards). This information is crucial for LLMs to complete tasks that require factual knowledge or understanding the context of a situation.

Here's the Retrieval Breakdown:

• The LLM Core: At its core, an LLM is a powerful language model, but it doesn't inherently store all the world's information.
• The Information Highway: Retrieval allows the LLM to connect to external information sources. This connection can be through APIs (application programming interfaces) or by directly accessing and parsing web pages.
• Understanding the Search: The LLM doesn't just blindly search. It utilizes your instructions and the task at hand to formulate specific queries. Imagine giving your research assistant clear instructions about the topic and the type of information you need.

Feeling Inspired? Let's See Retrieval in Action:

• Building a Question Answering LLM: Imagine an LLM that can answer your questions in a comprehensive way. Retrieval allows it to:

  • Understand your question and identify the key information you're seeking.
  • Access relevant databases or websites through retrieval functionalities.
  • Process the retrieved information and formulate an answer that addresses your specific question.

• Developing a Chatbot with Real-World Knowledge: Imagine a chatbot you can interact with for various purposes. Retrieval allows it to:

• Understand your request (booking a restaurant reservation, checking movie showtimes).
• Access online databases or booking platforms through retrieval functionalities.
• Utilize the retrieved information to complete your request or provide relevant information.

LLM Retrieval Prompts: Fuelling the Fire with Information

Here are two example prompts showcasing Retrieval for Large Language Models (LLMs) that access and process information from external sources:

Prompt 1: Building a Summarization LLM for Research Papers (Target Domain + Retrieval Strategy + Information Synthesis):

• Target Domain: Develop an LLM that summarizes research papers in the field of medicine.

• Retrieval Strategy: The LLM would utilize Retrieval to:

  • Access online academic databases containing medical research papers.
  • Search for relevant papers based on keywords or topics provided by the user.

• Information Synthesis: After retrieving the relevant papers, the LLM would:

  • Analyze the retrieved information to identify key findings and supporting arguments.
  • Generate a concise summary that captures the essence of the research paper in a user-friendly format.

Prompt: "As an LLM summarizing medical research papers, access online academic databases and retrieve relevant papers based on the user's search query. Analyze the retrieved information to identify key findings and supporting arguments. Finally, synthesize this information into a concise and informative summary that highlights the main points of the research paper."

Prompt 2: Developing a Travel Assistant LLM with Real-Time Updates (Target Task + Retrieval Sources + Dynamic Information):

• Target Task: Develop an LLM that assists users with trip planning and real-time updates.

• Retrieval Sources: The LLM would utilize Retrieval to access:

  • Online travel databases for flight information, hotel availability, and tourist attractions.
  • Real-time traffic data APIs to provide users with up-to-date information on road conditions and travel times.

• Dynamic Information: The LLM would continuously retrieve and process information to:

  • Suggest the best travel options based on user preferences and real-time conditions (flight delays, traffic jams).
  • Provide users with alerts and updates throughout their trip, ensuring a smooth and informed travel experience.

Prompt: "As a travel assistant LLM, access online travel databases and retrieve information on flights, hotels, and attractions based on user preferences. Additionally, utilize real-time traffic data APIs to provide users with up-to-date information on road conditions. Continuously monitor and process retrieved information to suggest optimal travel options and provide users with relevant alerts and updates throughout their trip."

These prompts demonstrate how Retrieval allows LLMs to access and utilize information from external sources to complete tasks that require real-world data and dynamic updates. Remember, the effectiveness of Retrieval relies on the clarity of the prompt, the chosen information sources, and the LLM's ability to process and synthesize the retrieved information.

Important Note: The effectiveness of Retrieval depends on the quality and accessibility of the external information sources. Additionally, ensuring the retrieved information is reliable and unbiased is crucial.

So next time you interact with an LLM that seems to have access to a vast amount of knowledge, remember the power of Retrieval! It's like giving LLMs the ability to search and access information, allowing them to complete tasks that require real-world knowledge and understanding. (Although, unlike a human research assistant, an LLM probably wouldn't get lost in the library stacks!).