Fine-Tuning LLaMA 3.2 for Positive Conversations: Should 'Bad' Examples Be Included in Training Data?"

[Manojkiran-G]

Hey guys , I'm currently working on fine-tuning llama 3.2 model for a use case involving various conversations. These conversations include both "good" (positive, respectful, and engaging) and "bad" (negative, disrespectful, or inappropriate) examples, and my goal is to train the model to maintain a positive tone and avoid generating harmful or inappropriate responses.
However, I’m unsure whether I should include the "bad" conversations in the training data. On one hand, including them might help the model learn to identify what makes a conversation go "wrong" and recognize patterns associated with negative tone, which could help it avoid making similar mistakes. On the other hand, I worry that including these "bad" conversations could lead the model to pick up undesirable patterns or behaviors, potentially causing it to generate responses with a negative tone, or even diluting the focus on positive behavior during training.
I’m curious if anyone here has worked on a similar challenge or has any advice on how to best handle this. Should I exclude the "bad" conversations entirely and focus only on good examples, or is it beneficial to incorporate them for the purpose of learning from both sides of the conversation? Would love to hear your thoughts!

[dkrizhanovskyi]

Quick Take:
Yes, include the “bad” examples, but clearly label and balance them with primarily positive examples. This helps the model learn what not to do, provided you guide it with appropriate training signals and a heavier emphasis on good behavior.

Detailed Explanation:
When fine-tuning a model like Llama 3.2 for maintaining a positive tone, the dilemma over whether to include negative (or “bad”) conversation examples is common. Here’s why carefully incorporating them can be beneficial:

1. Role of Negative Examples:
   Negative samples help the model learn boundaries. By exposing it to harmful, disrespectful, or inappropriate responses, you’re essentially showing the model real-world pitfalls. This can teach it to detect and avoid patterns that lead to bad behavior. Without any exposure to problematic content, the model may lack a clear notion of what’s off-limits.

2. Labeling and Differentiation:
   Simply throwing in bad examples without structure can cause confusion. Instead, label these bad samples clearly. Mark them as “undesirable” or “non-preferred.” If you’re using reinforcement learning or classification heads, these labels help teach the model that certain responses are less rewarded or should be filtered out.

3. Data Balance and Weighting:
   Your dataset should still lean heavily towards the kind of responses you want. For example, keep the majority (80-90%) of your training data positive and respectful, while sprinkling in negative examples (10-20%) just enough to highlight what should be avoided. This maintains a strong positive bias in the model’s final behavior.

4. Training Strategy:
   Consider a two-phase or curriculum approach. Start by training on primarily good data to establish a strong baseline. Then introduce the bad examples, along with a training mechanism (like RLHF or weighted losses) that clearly penalizes the model for mimicking those bad patterns. This way, the model learns both what’s right and what’s wrong—and crucially, how to prefer the right thing.

5. Iterative Evaluation:
   Continuously test the model. If it starts leaning negative, adjust. Perhaps reduce the proportion of bad examples, or tweak the reward signals. Ongoing evaluation ensures you can fine-tune your approach and maintain a constructive, respectful tone.

Some toughts:
Don’t entirely exclude bad conversations. Instead, carefully integrate and label them so the model gains a nuanced understanding of what it should not do, while still being guided toward producing positive, high-quality responses.

[mecodeatlas]

Hi @Manojkiran-G,

Thanks for being a part of the GitHub Community, we're glad you're here!

If you're looking for help for this specific topic, you might want to try asking for help somewhere that focuses on this project. It's possible that another GitHub user might have run into this same issue and can help, but the GitHub Community Discussions focuses primarily on topics related to GitHub itself or collaboration on project development and ideas. We want to make sure you’re getting the best support you can, but this space may not be the right place for this particular topic.

Best of luck!