ChatGPT is one of the most widely used applications of Large Language Models (LLMs), but users often wonder, "How accurate is ChatGPT?" Over time, they may notice that its responses become repetitive, vague, or even incorrect during extended interactions. This phenomenon, often referred to as “answer degradation,” raises concerns like, "What's wrong with ChatGPT?" or "Is ChatGPT bad for long conversations?"
In this article, we’ll explore why ChatGPT sometimes provides wrong answers, how its accuracy is influenced by model parameters like temperature and top_p, and practical solutions to keep ChatGPT responses sharp and relevant.
Glossary
Large Language Models (LLMs): Advanced AI systems capable of generating text, translating languages, and providing responses by processing inputs in tokenized formats.
Tokens: The smallest units of text in LLM processing, such as words, subwords, or characters, used for breaking down inputs into manageable pieces.
Context Window: The range of tokens that an LLM can process and retain during a conversation. Exceeding this range may result in information loss or degradation in response quality.
Hallucinations: Instances where an LLM generates inaccurate, fabricated, or nonsensical outputs not grounded in the input context.
Long-Chat Degradation: The decline in the quality and coherence of LLM responses over extended conversations due to context management challenges.
OpenAI Playground: An interactive tool for experimenting with LLMs by adjusting parameters like temperature and top_p without coding.
Sampling Strategy: The method by which an LLM selects the next token, influenced by parameters such as temperature and top_p.
Prompt Management: Techniques to structure and optimize user inputs for improved performance in LLMs, especially in long conversations.
How Large Language Models (LLMs) Work
To understand why ChatGPT sometimes provides inaccurate or irrelevant answers, it’s important to grasp the basics of how LLMs function.
When you pose a question to ChatGPT, the system receives it as a text input. The LLM behind ChatGPT breaks down the question into individual tokens, representing the basic units of meaning. This process is similar to how humans break down sentences into words and punctuation. Tokens can be words, characters, or parts of words. For example, the word "unbreakable" might be broken into three tokens: "un," "break," and "able."
Each LLM has a vocabulary of tokens that it understands. This vocabulary is created during the training process. The larger the vocabulary, the more nuances and domain-specific terms the model can understand. Llama 2 and Mistral both have a vocabulary of 32,000 tokens, while GPT has a vocabulary of 50,000 tokens.
Once the text is tokenized, the model creates embeddings for each token. Embeddings are mathematical representations of the tokens that capture their meaning and relationships to other tokens.
LLMs use a mechanism called attention to understand the relationships between different tokens in a sequence. This mechanism is implemented using a type of neural network architecture called a transformer.
The process of generating text from an LLM is called inference or generation. During inference, the model starts with a prompt (a piece of text) and then predicts the next token in the sequence, one token at a time.
For each token, the model generates a probability distribution over all the tokens in its vocabulary. To select the next token, the model uses a sampling strategy.
These parameters control the randomness of the output. A higher temperature or top P value will result in more creative and diverse outputs, while a lower value will result in more predictable outputs.
The process of predicting the next token and adding it to the prompt is repeated until the model reaches a stop token or the maximum length specified by the user.
LLMs struggle to recall information from the middle of long contexts accurately. They tend to perform better when information is located at the beginning or end of the context. This limitation is more pronounced in models with larger context windows. The performance of retrieving information also decreases substantially as the input contexts grow longer. For example, in tests where GPT 4 Turbo was asked to retrieve specific facts from different points in its context, its performance degraded drastically after 32,000 tokens. While GPT 4 Turbo can handle a context window of up to 128,000 tokens, its accuracy in retrieving information suffers when the context window grows beyond 64,000 tokens.
While parameters like temperature and top P can introduce randomness into the output, LLMs don't possess true creativity. Instead, they offer a proxy for creativity by injecting randomness into the generation process.
Long-chat degradation
Long-chat degradation refers to the decline in performance or quality of responses from a large language model (LLM) as the conversation extends over multiple turns. This issue arises due to challenges in managing and understanding the increasing context in lengthy discussions.
Here are reasons why long-chat degradation happens and ChatGPT give wrong answers.
Context Window Limit. LLMs have a fixed context window (e.g., GPT-4's window might be around 8,000 tokens or more, depending on the model version). When conversations exceed this limit, older information is truncated, leading to gaps in understanding and sometimes wrong answers.
Loss of Focus. As conversations grow longer, the model may struggle to prioritize relevant information from earlier turns. This can result in responses that are less coherent, repetitive, or irrelevant.
Cumulative Noise. In long chats, minor inaccuracies or misinterpretations from earlier responses can accumulate, degrading the overall quality. For example, the model might misinterpret context or forget crucial details.
Prompt Management. If earlier messages are not summarized or managed effectively, the model may waste tokens on unnecessary details, impacting its ability to respond well.
Entropy and Repetition. As conversations grow longer, ChatGPT may repeat itself or provide generic answers because it loses track of unique conversational context.
The good news is that there are symptoms that can indicate long-chat degradation:
- Repetitive answers or phrasing.
- Loss of key details from earlier in the conversation.
- Responses become generic, vague, or irrelevant.
- Decreasing coherence or logical consistency in answers.
To mitigate long-chat degradation, I recommend:
Summarize the Conversation: Periodically summarize the conversation to preserve key points within the context window. Summaries use fewer tokens and help the model focus on relevant details.
Chunking: Divide long conversations into distinct sections or topics. Reset the context with a brief recap when transitioning to a new section.
Refine Prompts: Use structured prompts or explicitly include important details to ensure the model retains focus.
External Context Management: Store and manage the conversation context externally (e.g., in a database). Provide a concise and relevant subset of the context to the model as needed.
Use Models with Larger Context Windows: Switch to an LLM with a larger context window if available (e.g., models designed for extended conversations).
Fine-Tune for Long Chats: Custom fine-tuning can improve how a model handles extended conversations.
Two key settings (temperature and top_p) directly influence the randomness and creativity of ChatGPT’s responses, which can sometimes lead to wrong answers.
Temperature
Temperature is a parameter used in large language models (LLMs) that controls the randomness or creativity of the generated text. It influences the probability distribution the model uses when selecting the next token (word, character, or sub-word) in a sequence.
When generating text, an LLM assigns a probability to each possible token in its vocabulary. Tokens with higher probabilities are more likely to be chosen as the next element in the sequence.
The temperature setting acts as a multiplier on these probabilities.
Low Temperature (close to 0): A low temperature amplifies the differences in probabilities, making the model highly likely to select the most probable token each time. This results in predictable and consistent outputs, similar to a "greedy" approach where the model always chooses the "best" option.
Temperature of 1: This is often the default setting. It uses the model's probability outputs directly, resulting in a balance between predictability and randomness.
High Temperature (above 1): A high temperature compresses the probabilities, making it more likely that less probable tokens are selected. This leads to more creative, diverse, and unexpected outputs. However, it also increases the risk of nonsensical or off-topic responses.
Temperature of 0 is useful for tasks where consistency and accuracy are paramount, such as:
- Classifiers: Generating specific categories or labels.
- Reproducible Outputs: Ensuring consistent results for testing and evaluation in software development.
- Strict Formatting: Tasks like writing JSON code where precise syntax is essential.
Higher Temperatures are beneficial when exploring creative possibilities, such as:
- Storytelling and Brainstorming: Generating more imaginative and unexpected narratives or ideas.
- Exploring Diverse Outputs: Obtaining a wider range of potential responses from the model.
- The optimal temperature setting depends on the specific task and desired level of creativity. Experimentation is often needed to find the right balance.
Top_p (Nucleus Sampling)
Top_p, also known as nucleus sampling, is another parameter used in LLMs to control the text generation process. It works in conjunction with temperature to refine the selection of tokens. While temperature affects the entire probability distribution, top_p focuses on selecting a subset of tokens whose cumulative probability exceeds the specified top_p value.
Here's how top_p works.
- Probability Calculation: The LLM calculates the probability of each token in its vocabulary appearing next in the sequence, just like it does with temperature.
- Sorting and Cumulative Probability: The tokens are then sorted in descending order of their probabilities. The model calculates the cumulative probability, adding up the probabilities of each token starting from the highest.
- Threshold Setting: The top_p value, typically between 0 and 1, sets a threshold. The model selects the smallest set of tokens from the beginning of the sorted list whose cumulative probability meets or exceeds the top_p value.
- Token Pool: This selected set of tokens forms the "nucleus" or pool from which the next token will be sampled.
- Temperature and Sampling: Within this pool, the temperature parameter then influences the final selection of the token.
Example:
Imagine the model has calculated probabilities for 10 tokens, and you set top_p to 0.6.
The model will:
- Sort the tokens by probability.
- Add up the probabilities starting from the highest until the sum reaches or exceeds 0.6.
- Let's say the first five tokens have a cumulative probability of 0.65. These five tokens form the pool.
- The temperature setting then determines how the model chooses from within this pool.
Effects of Top_p:
Top_p = 1: All tokens are considered, allowing maximum flexibility but potentially increasing the risk of less probable and nonsensical outputs.
Lower Top_p Values: Restrict the selection to a smaller set of highly probable tokens, increasing the consistency and coherence of the output.
Combined Effects: Using top_p in conjunction with temperature provides a more nuanced control over the generation process.
Understanding and tweaking these settings can help reduce issues like repetitive or irrelevant answers.
Let’s try
To fine-tune your prompts without coding, you can use OpenAI Playground.
The OpenAI Playground is an interactive web-based tool that allows users to experiment with OpenAI's language models like GPT-4, GPT-3.5, and others. It provides a user-friendly interface to test model behavior by adjusting parameters such as temperature, top_p, and penalties, without requiring programming skills.
With OpenAI Playground you can:
- Enter text prompts and get immediate responses from the model.
- Adjust settings like temperature, top_p, frequency penalty, presence penalty
- Choose from different models (e.g., gpt-4, gpt-3.5-turbo) to compare capabilities.
- Mimic API requests and understand how the model will behave in your application.
- Generate code snippets, debug existing ones, or create scripts for various programming languages.
- Share your prompt and settings with others using a generated link.
- Simulate dialogues by adding multiple user and assistant turns.
Mail rules for your experiments:
Use a higher temperature for more creativity, but constrain the choices with a lower top_p to avoid extreme randomness.
Use a lower temperature for predictability while allowing a wider range of options with a higher top_p.
Here are results of my experiment.
High temperature + High top_p
High temperature + low top_p
Low temperature + High top_p
Low temperature + low top_p
Practical Tips
If you find yourself questioning, "Why is ChatGPT wrong?" or looking for ways to improve its accuracy, here are some tips:
1. Break It Down with Smaller Chunks
Simplify for Success: Instead of overwhelming ChatGPT with a large dataset, provide smaller pieces of information. Begin each new chunk with a quick recap to keep the model aligned with your goals.
2. Be Strategic with Prompts
Guide the Conversation: Use clear and precise instructions like, “Summarize this 10-page document, focusing on marketing insights.”
Stay on Track: Periodically remind the model of the purpose or main ideas to prevent it from losing focus.
3. Use Summaries and Checkpoints
Refresh the Context: For long conversations, start a new session and summarize prior discussions to maintain clarity.
Verify Key Information: Double-check critical details using external tools or re-confirm them in ChatGPT.
4. Adjust Model Settings for Your Needs
Fine-Tune Creativity: Lower temperature and top_p settings for accurate, fact-based writing, or increase them for brainstorming and creative tasks.
5. Leverage Plugins and APIs
Enhanced Functionality: Explore ChatGPT-compatible plugins to manage large datasets or ensure consistency.
Custom Workflows: If you’re tech-savvy, use APIs to pre-process and structure data before sending it to ChatGPT. This ensures more efficient and accurate responses.
While ChatGPT is a powerful tool, its accuracy can vary depending on the context length and model settings. Users often wonder, "Why does ChatGPT provide wrong answers?" or worry about its limitations in long conversations. However, by understanding how LLMs work and implementing practical strategies, you can make ChatGPT an effective AI assistant.
For those still asking, "Is ChatGPT bad at accuracy?" the answer is no—but it requires thoughtful interaction and optimization to unlock its full potential.
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