Authors: Nilesh Barla
Published on: August 16, 2023 | Originally posted on LinkedIn
In the rapidly evolving world of language models, one concept stands out as crucial: alignment. Alignment is one of the most recurring words in the LLM community these days. A lot of papers have been published that exploit the topic of "Aligning" Large Language Model (LLM). But more specifically Aligning LLM with human preferences.
Aligning LLM with human preferences is not just a technical feat, but a pivotal factor in ensuring the effectiveness and ethical application of these powerful AI systems.
But why it is necessary? Why Aligning LLM is important?
In this article, we will understand why we need to align LLMs with human preferences and some of the commonly used methods.
Understanding Alignment in LLMs
Alignment in LLMs refers to the process of training these models to generate outputs that align with human preferences, intentions, and values. Essentially, it's about teaching machines to think and communicate in ways that resonate with us, making their outputs more useful and relatable. Large-scale language models undergo a dual-phase training process:
Unsupervised Pretraining: They engage in unsupervised pretraining using unprocessed text, aimed at acquiring versatile understandings.
Fine-tuning: They undertake extensive fine-tuning and reinforcement learning methods, on a massive scale, to enhance their synchronization with specific tasks and user preferences.
Now a large of extraction happens during the pretraining stage. During this stage, the model extracts general-purpose representations and patterns which itself makes the model an intelligent agent. But to yield task-specific results is where we require them to be fine-tuned on a specific set of data or instructions. This is what makes the agent smart.
An agent can smartness largely depends on the data it is fine-tuned. If the data is not curated well then the agent though intelligent can still be garbage. This is where alignment comes into the picture.
Why Aligning LLM is important? The Need for Alignment
Language models, while astonishingly capable, can sometimes produce outputs that deviate from human expectations or norms making them yield wrong information that is untruthful, toxic, or biased, invent hallucinated facts, or facts that aren't helpful to the user at all.
But let me explain why alignment is necessary for LLMs. Allow me to use one of the most intriguing and concerning phenomena in LLM – "hallucination." Picture this: LLMs weaving captivating narratives that, upon closer examination, turn out to be imaginative but far from factual. This is the realm of hallucination, where these models conjure content that seems plausible yet lacks a basis in reality. It's akin to a vivid dream that fades upon waking. This can be extremely dangerous if proper measures are not taken.
The solution to this challenge comes through alignment. We align LLMs to reality, imparting them the ability to discern between authentic information and pure fabrication. By fine-tuning them meticulously and incorporating user feedback, we guide these models to generate content that's creative yet grounded in facts. This alignment ensures that the outputs aren't just engaging but also reliable.
Essentially, we want to plant the agent in the ground of reality such that when it generates results and responses by rooting itself within this ground. Now, imagine an LLM that not only crafts captivating stories but also respects the boundaries of authenticity. Aligning LLMs allows us to tap into their creative prowess while ensuring that the content they produce is both imaginative and factual.
Aligning LLMs helps bridge this gap, ensuring that the language generated is coherent, contextually accurate, and aligns with what humans intend to convey.
Challenges in Aligning LLMs
The journey towards alignment isn't without its challenges. Ensuring that LLMs consistently produce desirable outputs involves navigating complex issues like bias, ambiguity, and context. Striking the right balance between generating creative responses and staying within ethical bounds is a challenge that demands both technical finesse and ethical considerations.
One of the reasons why Aligning LLMs is challenging is because of the objective function. The objective function used for many LLMs is different. For instance, LLMs are modeled to predict the next token in a sequence which differs from the objective of “follow the user’s instructions helpfully and safely”.
Even when you read the statement and take time to contemplate you will undoubtedly come to the conclusion that "the language models are heavily misaligned".
Keeping that in mind here are some of the challenges in aligning LLMs:
Managing Instruction Data: Different sources provide training instructions, making it hard to compare methods. We need to figure out how to select, sequence, and combine these instructions effectively.
Multilingual Alignment: Most alignment research focuses on English, leaving non-English languages behind. We must explore how well alignment methods work in various languages, especially less-resourced ones.
Better Training Methods: Current alignment techniques lack the ability to truly understand human preferences. We need smarter training methods that involve human preferences more explicitly.
Human Involvement: Human assistance significantly improves alignment quality. Models like ShareGPT let humans guide alignment instead of just following instructions. We need to explore more ways humans can enhance alignment.
Collaborative Evaluation: We often use LLMs to evaluate other LLMs. Modern LLMs perform exceptionally well, so why not include them and human evaluators in the alignment assessment? This collaborative approach can yield efficient and high-quality results.
Methods for Achieving Alignment
Achieving alignment between large language models (LLMs) and human preferences involves employing various methods and techniques. In this article we will briefly touch on four methods. These methods play a crucial role in ensuring that the generated outputs from LLMs are in line with what humans desire.
Collecting curated data: Aligning LLMs with human expectations requires the collection of high-quality training data that authentically reflects human needs and expectations. In this endeavor, an instruction is denoted as I_k = (x_k, y_k), where x_k signifies the instruction input and y_k represents the corresponding response. This data can be sourced from various channels, encompassing both instructions created by humans and those generated by robust LLMs. When it comes to human-provided instructions they mainly originate from two main sources: pre-existing human-annotated NLP benchmarks and meticulously handcrafted instructions.
The first source draws upon established datasets that have been curated and annotated by human experts, serving as a valuable foundation for instruction. On the other hand, meticulously handcrafted instructions involve the intentional creation of prompts by individuals to guide LLMs toward desired outputs. Both these sources contribute to refining LLM alignment, offering a spectrum of input that enhances the model's responsiveness to human expectations.
With that being said, let us understand the various methods for model alignment.
Reinforcement Learning with Human Feedback: Reinforcement Learning with Human Feedback (RLHF) involves a two-step process where pre-trained LLMs are fine-tuned to produce more accurate and contextually appropriate responses.
Pretraining: A base model is initially trained using a vast corpus of text from the internet, enabling it to understand language and generate coherent sentences. However, this base model might not always produce responses that align perfectly with human intentions or preferences.
Finetuning: The model's responses are fine-tuned using human feedback. Human AI trainers review and rank different response options generated by the model, providing reward signals that indicate which responses are more aligned with the desired behavior. By repeatedly fine-tuning the model based on these reward signals, it gradually learns to generate more accurate, relevant, and contextually appropriate responses that better match human expectations.
In essence, RLHF leverages human guidance to iteratively refine a language model's responses, allowing it to better align with the desired user interactions and generate high-quality outputs that cater to specific contexts and user preferences.
The image above shows the process of how GPT-3 was trained.
Fine-Tuning and Reward Shaping: One common approach is to fine-tune the pre-trained LLMs using specific reward functions that guide the model toward desired behavior. By shaping the rewards, LLMs can be encouraged to generate outputs that align with human preferences. This method involves training the model on specific tasks and adjusting the rewards based on how well the model performs.
Prompt Engineering and Chain-Of-Thoughts: Crafting well-designed prompts is another method for achieving alignment. By providing clear and detailed prompts, LLMs can be guided to generate content that directly addresses the desired topic or task. Prompt engineering is crucial for obtaining specific and accurate outputs. One of the interesting methods is Chain-Of-Thoughts.
The concept of chain-of-thought prompting offers several enticing advantages when integrated into the context of aligning large language models (LLMs).
To begin, the chain of thought approach allows LLMs to break down complex problems into manageable intermediate steps. This aligns with the principle of LLM alignment, enabling the models to better understand and process multi-step reasoning tasks efficiently.
Furthermore, incorporating a chain of thought within the prompts offers a transparent view of the model's decision-making process. This insight is valuable in LLM alignment, as it helps pinpoint areas where the model's reasoning might deviate from human expectations, thereby aiding in the refinement of alignment strategies.
The application of chain-of-thought reasoning isn't limited; it extends to various tasks such as math word problems, commonsense reasoning, and symbolic manipulation. In the realm of LLM alignment, this versatility is crucial as it caters to aligning models across a broad spectrum of tasks.
Importantly, the integration of chain-of-thought reasoning into LLM alignment is uncomplicated, and achievable by simply incorporating examples of chain-of-thought sequences into the prompts of off-the-shelf LLMs. This straightforward approach enhances alignment efforts and nurtures the development of reasoning capabilities within the models.
LIMA (Less is more for alignment): LIMA or less is more for alignment is a recent research done for aligning LLMs. The research mostly focuses on how carefully curated prompts and responses with a sample size of 1000, without any reinforcement learning or human preference modeling can be leveraged to align a LLM. At its core, LIMA delves into the delicate dance between fine-tuning and reinforcement learning, seeking to strike the perfect balance. This finding challenges the traditional notion that exhaustive fine-tuning and complex modeling are prerequisites for effective LLM alignment.
With LIMA the authors suggest that thoughtful curation of these compact datasets can wield impressive results, guiding LLMs to align with human intents and preferences more organically. This groundbreaking approach not only enhances the efficiency of alignment processes but also opens doors to a new era of resourceful and targeted AI alignment strategies.
LIMA underscores the crucial role alignment plays in tailoring responses to user interactions while ensuring engagement. The method emphasizes the supremacy of data quality over sheer quantity, echoing the paper's assertion that well-curated training data leads to superior outcomes.
In the dynamic realm of language models, alignment has emerged as a pivotal concept, steering the course of Large Language Models (LLMs) toward effectiveness and ethics.
Alignment's Crucial Role: As LLMs evolve, aligning them with human preferences becomes pivotal for their reliability and ethical application.
Why Alignment Matters: Alignment ensures that LLM outputs resonate with human intent, mitigating risks of false narratives and inaccuracies.
Tackling Alignment Challenges: Navigating issues like bias, context, and ethical concerns requires innovative methods such as Reinforcement Learning with Human Feedback and prompt engineering.
LIMA's Paradigm Shift: The LIMA approach underscores the value of thoughtful data curation over exhaustive fine-tuning, redefining alignment strategies.
Alignment's Power: Alignment bridges human communication and AI-generated responses, making LLMs informed and responsible communicators.
In this landscape of blurred human-AI interaction, alignment serves as the guiding light, leading us towards a future where AI's potential enhances human endeavors responsibly.
Aligning Large Language Models with Human: A Survey
Training language models to follow instructions with human feedback
Fundamental Limitations Of Alignment In Large Language Models
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
LIMA: Less Is More for Alignment