100k token size in LLM (large language models) like GPT-3 have the following advantages:
Greater representational capacity - With more parameters, the model can learn more complex representations of language and knowledge. This allows it to generate more accurate and coherent text.
Better long-term coherence - With a larger context window of 100k tokens, the model can maintain coherence over longer timescales in the generated text. Shorter models tend to lose coherence faster.
Ability to handle more complex prompts - The larger model size allows it to understand and generate text for more complex and nuanced prompts.
Better factual and semantic consistency - With more context and parameters, the model has a better grasp of facts, entities and their relationships. This helps it generate more factually and semantically consistent text.
Ability to handle more diverse tasks - The large size enables the model to be distilled and fine-tuned for more diverse NLP tasks like summarization, question answering, translation, etc.
However, there are also some limitations of very large LLM sizes like 100k tokens:
Higher costs - Both in terms of training costs and inference latency/costs in production.
Higher carbon footprint - Due to the large computational requirements.
Difficulty in optimization and training - Very large models are difficult to train efficiently.
Increased risk of bias and toxicity - Due to being trained on a very large and diverse web corpus.
So in summary, while 100k token sizes offer significant advantages over smaller LLM sizes, organizations need to weigh the trade-offs based on their specific needs and constraints. The optimal size depends on the intended use cases and performance requirements.
Some tasks that would benefit significantly from a 100k token large language model like GPT-3 compared to smaller LLMs include:
Complex text summarization - Summarizing lengthy articles, research papers, legal documents, etc. The larger context window allows the model to understand the full context and generate accurate and coherent summaries.
Advanced code generation - Generating code for complex tasks in languages like Python, Java, etc. The increased representational capacity lets the model "learn" the syntax and paradigms of the language better.
Answering complex multi-fact questions - Answering questions that require pulling facts from multiple sources and putting them together in a coherent response. The larger model has a better grasp of the relevant facts and how to combine them.
Generating realistic dialogue - Creating realistic multi-turn conversations between characters. The 100k token size allows the model to maintain consistency over longer spans of dialogue.
Open-domain question answering - Answering complex questions that require knowledge spanning multiple domains. The model has learned representations for a wide range of knowledge.
Advanced text editing tasks - Like rewriting text to change the tone, style, or simplify the language, while maintaining the core meaning. The larger model has learned more nuanced representations of language.
Text-to-text translation - Accurately translating lengthy documents between languages. The increased context window helps preserve the nuances and full meaning of the original text.
So in general, tasks that require integrating and utilizing information from a wide context, complex reasoning over long spans of text, and generating coherent multi-sentence or multi-paragraph text would benefit significantly from the increased capabilities of a 100k token LLM.
Here's an example of a complex text summarization task that would benefit from a 100k token LLM like GPT-3:
Summarizing a lengthy research paper in the biological sciences. For example, a paper on a new genetic discovery related to cancer. These papers are often 10-20 pages long and contain:
• A detailed introduction that provides the scientific and medical background for the research. This context helps establish why the research is important.
• An in-depth methods section describing the experimental setup, techniques and protocols used. This includes things like the protein samples studied, genes sequenced, types of assays performed, etc.
• Results described across multiple sections and figures presenting things like gene expression changes, protein interactions, cell behavior, etc. across different conditions and over time.
• A discussion section that synthesizes and interprets the results in the context of prior knowledge, outlines limitations and proposes future work.
To accurately summarize this paper in a concise 3-5 paragraph summary would require:
• Understanding the scientific background and context over multiple sentences and paragraphs.
• Interpreting the complex methods and different result types presented.
• Identifying the key results and trends across different sections of the paper.
• Integrating the results with the background and interpreting their significance and implications.
• Synthesizing the main conclusions and future directions in a coherent manner.
The larger context window of a 100k token LLM allows it to maintain coherence while incorporating relevant information spread over long spans of text. This helps it generate a summary that captures the key findings and significance of the research at a high level in a factually consistent manner.
Does this example help illustrate the type of complex text summarization task that would require a larger language model size? Let me know if you have any other questions.
How does the LLM compare to other models in summarizing complex texts?
LLMs (Large Language Models) like GPT-3 have some advantages and disadvantages compared to other model architectures for complex text summarization:
Advantages:
• They can leverage the large context window to understand long documents in full and identify the most important information. This helps generate more comprehensive summaries.
• They tend to generate more fluent and coherent summaries since they have been trained on a huge amount of text.
• They are more generalized and can be fine-tuned to summarize texts from various domains and genres.
• They can generate either abstractive or extractive summaries depending on the fine-tuning objective.
Disadvantages:
• They tend to hallucinate and generate incorrect or irrelevant details since they were not explicitly trained for summarization.
• They struggle to condense information and generate concise summaries. They often reproduce parts of the original text.
• They have lower factuality and accuracy compared to models specifically trained for summarization.
In contrast, models specifically trained for summarization like transformer-based encoder-decoder architectures tend to:
• Generate more concise and factually correct summaries since that was their training objective.
• Be better at identifying and reproducing only the most salient portions of the text.
• Have higher accuracy on metrics like ROUGE which measure summary quality.
However, they typically have a shorter context window (512-1024 tokens) and thus struggle with longer and more complex texts. They also tend to be less generalized.
So in summary, while LLMs have some advantages for complex text summarization due to their large context window, they also have drawbacks. Models specifically trained for summarization currently generate more accurate and concise summaries, though they struggle with longer texts.
Hope this comparison helps! Let me know if you have any other questions.
HHow can LLMs be fine-tuned for summarization?
LLMs (Large Language Models) like GPT-3 can be fine-tuned for summarization using the following approaches:
Supervised training - The simplest approach is to fine-tune the LLM using a large dataset of text-summary pairs. The model is trained to generate the corresponding summary given the input text.
This requires a sizable supervised dataset, which can be expensive to create. Public datasets like CNN/DailyMail can be used.
Self-supervised training - The LLM is trained using the original text as input and the first few sentences as the "summary". This creates weak supervision from the data itself.
The model is then fine-tuned on a smaller set of human-written summaries to improve accuracy. This approach requires less labeled data.
Reinforcement learning - The LLM is first trained autoencoding - to reproduce the input text. Then, rewards are given based on the quality and conciseness of the generated summary.
The model learns to generate better summaries through trial-and-error to maximize these rewards. However, this requires defining a good reward function.
Filtering and post-processing - Generated summaries from the LLM can be filtered and refined using techniques like:
• Extracting sentences with the highest similarity to human references
• Removing repetitive sentences
• Combining overlapping content into a single sentence, etc.
This requires minimal fine-tuning of the base LLM but provides less control over the summary style.
Prompting - The LLM can be "prompted" to generate a summary using natural language instructions. For example:
In 2-3 short paragraphs, summarize the main points of the following text:
This relies more on the pre-trained LLM abilities and requires less labeled data. But accuracy tends to be lower.
So in short, there are a variety of approaches to fine-tune LLMs for summarization - from fully supervised to minimally supervised. The choice depends on the available data and required accuracy.
How can I evaluate the quality of the generated summaries?
There are a few main ways you can evaluate the quality of summaries generated by fine-tuned language models:
ROUGE scores - The most common automatic evaluation metric for summarization. It measures the overlap of n-grams between the generated summary and human references. Higher ROUGE scores indicate better summaries.
Common ROUGE metrics include:
ROUGE-1: Measures overlap of unigrams
ROUGE-2: Measures bigram overlap
ROUGE-L: Measures longest common subsequence
Human evaluation - The gold standard for evaluation. Ask humans to rate the generated summaries based on:
Informativeness: How well the summary covers the key information in the original text.
Conciseness: The degree of redundancy reduction in the summary.
Coherence: How logically consistent and readable the summary is.
Ratings are averaged to determine the overall quality.
Fact checking - Automate or perform manual fact checking to verify if the summaries contain factual errors or hallucinations. The fewer errors, the better the summary.
Length metrics - Compare the lengths of the generated summaries to the original text and reference summaries. More concise summaries are usually higher quality.
You can calculate:
Compression ratio = Length of summary / Length of original text
Ideal compression ratio = Average length of reference summaries / Length of original text
The closer the generated compression ratio is to the ideal ratio, the better.
In summary, a combination of automatic metrics (ROUGE scores), fact checking, length metrics and human evaluation can give you a more comprehensive and reliable assessment of the generated summaries. This allows you to identify areas for improvement and optimize the fine-tuning of your language model.
I hope this helps! Let me know if you have any other questions.
Here are some tools I would recommend for automated fact checking:
• FAITI - This is a Google AI fact checking tool that can be integrated into various applications. It performs automated fact extraction from text and uses a knowledge graph to verify claims.
• ClaimReview - An API from Cortical.io that performs fact checking using techniques like semantic similarity analysis, named entity recognition and knowledge graph lookup. It can check claims in real time.
• CheckThat! - An AI model from the ADAPT Centre that performs end-to-end fact checking. It first detects check-worthy claims in a text and then verifies those claims using evidence from the web.
• Facebook's Factchecking Tools - Facebook has published several tools to help with automated fact checking, including:
ClaimBuster - Identifies factual claims within texts.
FastText Verify - Checks the veracity of claims using extracted evidence.
Factora - Uses machine reading to extract facts from a knowledge base to verify claims.
• Meedan Check - An AI model to detect check-worthy claims. It can classify claims into categories and detect potential corrections.
• Overleaf - An open source natural language processing library that includes tools for fact checking like claim detection, evidence retrieval and stance classification.
So in summary, there are several AI-based tools available that can help automate part of the fact checking process. They can identify claims, extract evidence, classify claims and verify claims against knowledge sources. Using one or more of these tools can give you an initial automated assessment of the factuality of generated summaries.
Hope this helps! Let me know if you have any other questions.
Meedan Check is an AI-based fact checking tool developed by the Meedan organization. Here's how it works in a nutshell:
Claim detection - Meedan Check uses machine learning models to identify claims within a text that are worth fact checking. It determines what the claim is and who is making the claim.
Claim filtering - Not all detected claims are check-worthy. Meedan Check filters out vague, subjective or trivial claims to focus on important factual claims.
Claim normalization - The claims are normalized into a standardized format to facilitate further processing. This includes things like:
Converting to lowercase
Removing punctuation
Expanding abbreviations
Lemmatization (converting words to base form)
Claim analysis - Meedan Check performs linguistic analysis on the normalized claims to extract useful features:
Named entity recognition to identify people, places, organizations, dates, etc.
Tense and modality to determine certainty
Dependency parsing to identify key verbs and relations
Claim classification - Based on the claim features, Meedan Check classifies the claims into categories like:
Numbers
Events
People
Places
Medical
Policies, etc.
Stance detection - Meedan Check tries to determine if the claim is supported, refuted or has mixed or no evidence based on its analysis. This gives an initial automated assessment of the claim.
This workflow allows Meedan Check to analyze textual claims, extract useful information and provide an initial automated fact check before a manual review is done. The claim categories and stances detected can help prioritize claims for further fact checking.
Hope this explanation helps! Let me know if you have any other questions.