Australian National University

Vaibhav Khobragade

Unlocking the Future of AI: The Transformative Journey of Large
Language Models

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*YRl2-vMjMkrU_3vt" />
<figcaption>Image generated using DALL.E-2</figcaption>
</figure>

### Author

· Vaibhav Khobragade (**ORCID:**
[0009--0009--8807--5982](https://orcid.org/0009-0009-8807-5982))

### **Introduction**

Human language development is innate and evolves throughout life.
Machines lack this ability to evolve without advanced AI algorithms.
Since the Turing Test was proposed in the 1950s, efforts to master
machine understanding of language have led from statistical to neural
language models. Recently, scaling up pre-trained language models like
Transformer models has significantly advanced AI's ability in natural
language processing tasks by training on large datasets, enhancing model
capacity and performance. The advancement of LLMs (Large Language
Models) has profoundly influenced both the AI and broader public
communities, promising a transformative shift in AI algorithm
development and utilization. This article explores the evolutionary
journey of LLMs, their diverse applications, and inherent limitations,
and outlines potential future directions for this technology.

Language modeling (LM) enhances machine language understanding by
predicting the likelihood of word sequences based on previous text. For
example: LM predicts "The cat is \_\_\_\_" with a high likelihood for
"sleeping", based on previous text data.

LM research has evolved through four stages, focusing on predicting
future tokens (token means a single unit of text, typically a word or a
piece of punctuation) in the text.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1019/0*EHrN9qEtHufQT3sl" />
<figcaption>Figure 1: Depicts the evolution of language models (LM) over
four generations in terms of task solving capacity. (Zhao et al., 2023)
Article link: <a
href="https://arxiv.org/abs/2303.18223">https://arxiv.org/abs/2303.18223</a></figcaption>
</figure>

**Statistical Language Models (SLMs):** SLMs developed in the 1990s,
utilize the Markov assumption to guess the next word based on a few
words before it. These are called n-gram models, like 'bigrams' (looking
at two words like 'I am') and 'trigrams' (looking at three words like
'All is well').

While SLMs were popular for tasks like finding information and
understanding language, they had a major drawback. Predicting based on
many past words (high-order models) became difficult because it
necessitated estimating an exponential number of transition
probabilities, which are numerical values between 0 and 1 that represent
the probability of a specific word (**B**) appearing after another word
(**A**) in a sequence, leading to data sparsity issues. To overcome
this, researchers used smoothing techniques like [backoff and
Good--Turing
estimation](https://www.scaler.com/topics/nlp/backoff-in-nlp/) to
improve their accuracy.

**Neural Language Models (NLMs):** Early language models were good at
predicting word order, but they struggled to understand the deeper
meaning of language. Newer models called Neural Language Models (NLMs)
use powerful tools like neural networks like **MLPs** (Multilayer
perceptron) and **RNNs** (Recurrent Neural Networks) to capture the
relationships between words. Shallow networks like **word2vec** even
create unique codes (vector representations) for each word, helping them
understand the bigger picture. This shift towards understanding word
meaning, not just order, has revolutionized how computers process
language, allowing them to tackle a wider range of tasks like generating
text and translating languages

**Pre-trained Language Models (PLMs):** PLMs refer to a model that has
been trained on a large corpus of text data using unsupervised learning
techniques. During pre-training, the model learns to understand and
generate natural language by processing vast amounts of text data such
as books, articles, websites, and other textual data available on the
internet, typically without specific task labels or annotations before
being fine-tuned for specific downstream tasks such as classification
and text generation.

<figure>
<img src="https://cdn-images-1.medium.com/max/940/0*8zz6W3GJx8-Zfrpf" />
<figcaption>Figure 2: Fine-tuning on flower classification task by using
pretrained model Resnet16</figcaption>
</figure>

Pioneering models like **ELMo** (Embeddings from Language Models) and
**BERT** (Bidirectional Encoder Representations from Transformers)
revolutionized NLP by introducing context-aware word representations.
These models leveraged pre-training with bidirectional architectures
like Long Short-Term Memory (**LSTM**) and Transformers on vast text
corpora. This pre-training allowed for learning contextual word
embeddings. Subsequently, fine-tuning these pre-trained models on
specific tasks significantly improved NLP performance. This
'pre-training and fine-tuning' paradigm became the foundation for
subsequent models like **GPT-2** (Generative Pre-trained Transformer 2)
and **BART** (Bidirectional and Auto-Regressive Transformers).

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*Bnzx3jSKSWO2FHt7" />
<figcaption>Figure 3: The cumulative counts of arXiv papers featuring
the terms “language model” (from June 2018) and “large language model”
(from October 2019) show distinct trends over time. (Zhao et al., 2023)
Article link: <a
href="https://arxiv.org/abs/2303.18223">https://arxiv.org/abs/2303.18223</a></figcaption>
</figure>

After ChatGPT was released shown in figure 3, there was a notable surge:
the average daily number of arXiv papers featuring "large language
model" in their title or abstract increased from 0.40 to 8.58.

**Large language models (LLMs):**

Researchers have found that making Pre-trained Language Models (PLMs)
bigger, either by increasing model size or training data size, often
improves their ability to perform various tasks. These large models,
like **GPT-3** and **PaLM**, show different behaviors compared to
smaller ones like **BERT** and **GPT-2** and they can solve complex
tasks surprisingly well (called emergent ability). The research
community calls these large models "large language models (LLMs)," and
they're getting a lot of attention. For instance, ChatGPT2, based on GPT
models, impressively converse with humans.

Nowadays, LLMs are making a big impact on the AI community. With
developments like ChatGPT and GPT-4, people are starting to think more
about the potential of artificial general intelligence
([AGI](https://openai.com/blog/planning-for-agi-and-beyond)), a
hypothetical AI with human-level or even surpassing intelligence, which
holds the potential to enhance humanity by increasing resources,
propelling economies, and accelerating scientific breakthroughs. OpenAI
recently wrote an article called "Planning for AGI and beyond,"
discussing how to approach AGI development responsibly, both in the
short and long term. Some experts even say that GPT-4 could be an early
version of AGI.

**LLM Emergent Ability:**

Emergent abilities of LLMs are described as capabilities that appear in
large models but are not found in smaller ones. This is a key difference
that sets LLMs apart from previous PLMs.

**In-context learning (ICL):** In-context learning refers to the ability
of a language model to generate output based on given instructions or
task demonstrations without requiring additional training or gradient
updates. The focus is on the model's ability to generate output based on
contextual information. Instead of needing extra training, they can
learn new things just by seeing examples and getting instructions.

For instance, consider the following context:

*Context: "You are a virtual assistant helping a user with math
problems."*

*Instruction: "Calculate the square root of 25."*

In-context learning lets the assistant understand your request based on
what you instructed, without needing to be specifically programmed for
square roots.

**Instruction Following:** In Instruction Following, the language model
is fine-tuned with a mixture of multi-task datasets formatted as natural
language instructions comprising various tasks (e.g., text
summarization, question answering, translation) described in a way that
mimics how humans might request the task to be done. This allows the
model to perform well on unseen tasks that are also described in the
form of instructions.

For instance: *"Calculate the average of three numbers: 10, 15,
and 20."*

With instruction following ability, the model should be able to
understand this instruction and perform the task without further
training. It would calculate the average of the given numbers (10, 15,
and 20) and provide the output, which is 15.

**Step-by-step reasoning:** Chain-of-thought (CoT) prompting helps large
language models solve complex problems, like math word problems, by
breaking them down into smaller, easier steps. This approach encourages
the model to think through a problem step-by-step to reach the final
answer, making it easier to handle tasks that require more than one
reasoning step.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1006/0*duWhWQWLinWZmkEG" />
</figure>

<figure>
<img
src="https://cdn-images-1.medium.com/max/1008/0*JPAYU31Jegl2dIzG" />
<figcaption>Table 1: Statistics of large language models (having a size
larger than 10B in this survey) in recent years (Zhao et al., 2023)
Article link: <a
href="https://arxiv.org/abs/2303.18223">https://arxiv.org/abs/2303.18223</a></figcaption>
</figure>

The above table includes:

- Capacity evaluation, pre-training data scale (either in the number of
  tokens or storage size) and hardware resource costs
- Release Time: indicates the date when the corresponding paper was
  officially released
- Publicly Available: means that the model checkpoints(core components
  used for making predictions) can be publicly accessible while "Closed
  Source" means the opposite.
- Adaptation: indicates whether the model has been trained further and
  fine-tuned after its initial creation. **IT**(instruction tuning)
  denotes the model was given extra training on specific instructions,
  like how to follow directions and **RLHF** denotes Reinforcement
  Learning with Human Feedback, in which the model is trained by
  rewarding it for good responses based on human input.
- Evaluation: indicates how well the model performed on specific tasks
  in the research paper that introduced it: ICL denotes in-context
  learning which checks if the model can learn new things based on
  instructions within a specific context and CoT denotes
  chain-of-thought which indicates if the model can explain its
  reasoning process, similar to showing its work in math. "\*" denotes
  the largest publicly available version

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*WNss8XH6psGogdeU" />
<figcaption>Figure 4: A timeline of existing large language models
(having a size larger than 10B) in recent years. We mark the LLMs with
publicly available model checkpoints in yellow colour. (Zhao et al.,
2023) Article link: <a
href="https://arxiv.org/abs/2303.18223">https://arxiv.org/abs/2303.18223</a></figcaption>
</figure>

This figure shows a timeline of the development of large language models
(LLMs) that have more than 10 billion parameters, highlighting
significant advancements and releases over recent years. This timeline
visually represents the progress in the field, showing the rapid growth
and evolution of LLMs, marked by key models and milestones that have
pushed the boundaries of what these models can achieve.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*iX3Jr_V7VwTnXjtI" />
<figcaption>Figure 5: A brief illustration of the technical evolution of
GPT-series models (Zhao et al., 2023) Article link: <a
href="https://arxiv.org/abs/2303.18223">https://arxiv.org/abs/2303.18223</a></figcaption>
</figure>

The figure given above illustrates the progression of OpenAI's models,
showing how each subsequent version has built upon the last, improving
capabilities such as multitasking, in-context learning, code generation,
instruction following, human alignment, comprehensive ability,
reasoning, and multimodal interaction.

### **Applications of LLM**

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*0RjUiWa5RWmES2or" />
<figcaption>Figure 6: Illustrates the various research directions and
downstream domains where Large Language Models (LLMs) find applications
(Zhao et al., 2023) Article link: <a
href="https://arxiv.org/abs/2303.18223">https://arxiv.org/abs/2303.18223</a></figcaption>
</figure>

The applications of Large Language Models (LLMs) across research
directions and domains are wide-ranging:

- **Classic NLP Tasks**: LLMs handle traditional natural language
  processing tasks like word/sentence-level tasks, sequence tagging,
  information extraction, and text generation, offering robust solutions
  for diverse linguistic challenges.
- **Information Retrieval (IR)**: LLMs are used both as models for
  retrieving information and to enhance existing IR models, improving
  the accuracy and relevance of search results.
- **Recommendation Systems**: They serve in recommendation engines, both
  as primary models and as enhancements to existing systems, simulating
  recommendations, and aligning closely with user preferences. For
  example recommendations in Netflix and YouTube.
- **Multimodal LLMs**: These models integrate and process information
  from different modalities, such as text and images, enhancing
  interaction and understanding across various formats.
- **Knowledge Graph (KG) Enhanced LLMs**: LLMs are augmented with
  knowledge from structured data sources like KGs, improving their
  ability to understand and generate contextually rich content.

### **New Scenarios**

- **LLM-based Agents**: These agents, equipped with capabilities for
  memory, planning, and execution, can autonomously perform tasks,
  adapting to user requests and environmental feedback.
- **LLM for Evaluation**: LLMs are used to evaluate and score other
  models or content, employing techniques like language-based evaluation
  and meta-evaluation.

### **Specific Domains**

LLMs find applications in specialized areas as follows:

- **Healthcare**: Med-PaLM models achieved expert-level performance on
  the US Medical Licensing Examination, gaining approval from physicians
  for answering medical questions​.
- **Education**: ChatGPT helped students perform better in computer
  security courses by generating or refining answers​.
- **Law**: GPT-4 scored in the top 10% on a simulated bar exam,
  demonstrating its powerful legal interpretation and reasoning
  abilities​.
- **Finance**: BloombergGPT showed remarkable performance across various
  financial tasks, maintaining competitive performance in
  general-purpose tasks​.
- **Scientific Research**: LLMs assisted in various stages of the
  scientific research pipeline, including literature surveys, hypothesis
  generation, data analysis, and paper writing​.

### **The limitations of LLMs**

- **Efficiency Issues**: As LLMs get bigger, they become harder to use
  widely, especially when lots of them need to work together. They also
  need better ways to communicate as they grow.
- **Technical Challenges for Agent Development**: Making LLMs that can
  do tasks on their own is hard. They struggle with following
  instructions over long texts and aren't ready made for acting on their
  own without more work.
- **Biases in LLM-based Evaluators:** When LLMs are used to judge other
  models or content, they can be biased, preferring certain patterns or
  lengths of text. They don't always understand their own writing, which
  can make it hard to trust their evaluations of complex tasks.
- **Knowledge Recency and Hallucination:** LLMs can make mistakes or
  come up with false information because they don't always know the most
  recent facts. This is a problem even for advanced models like ChatGPT,
  which means there's a need to adjust them and use additional tools to
  ensure they provide accurate information.

One realization after writing this post is that the limit of my
knowledge means the limit of my world, which is why every human has the
goal of ***"cultivation of mind should be the ultimate aim of human
existence" (Dr. B. R. Ambedkar)***. The same principle applies to LLM:
the more knowledge you feed; the better machines understand.

### **Conclusion**

The evolution of Large Language Models marks a transformative era in AI,
expanding capabilities from basic language understanding to complex
problem-solving across diverse domains. Despite their remarkable
progress and potential, LLMs face limitations and challenges that
necessitate ongoing research, ethical considerations, and technological
advancements to fully realize their impact and pave the way for future
innovations in artificial intelligence.

### References

- Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., ... & Wen,
  J.-R. (2023). A Survey of Large Language Models. arXiv.
  <https://arxiv.org/abs/2303.18223>
- DePeau-Wilson, M. (2023, March 14). Google AI performs at 'expert'
  level on U.S. medical licensing exam. MedPage Today.
  <https://www.medpagetoday.com/special-reports/exclusives/103522>

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Unlocking the Future of AI: The Transformative Journey of Large Language Models Author   · Vaibhav Khobragade (
<strong>
 ORCID:
</strong>
0009–0009–8807–5982)
<strong>
 Introduction
</strong>
Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced AI algorithms.

Messaggi di Rogue Scholar

The Journey of Large Language Models: Evolution, Application, and Limitations