Enhance Model Evaluations: Allowing `gen_prefix` With Thinking

Nov 13, 2025 by Admin 63 views

Hey guys, let's dive into a critical discussion about improving model evaluations in the context of EleutherAI's lm-evaluation-harness. Specifically, we're going to talk about the gen_prefix parameter and its compatibility with enable_thinking=True. Currently, there's a snag: when gen_prefix is active, it prevents models from utilizing "think tokens." This can lead to poorer evaluation results, especially for models designed to operate with these tokens. Let's break down the problem and explore potential solutions.

The Problem with `gen_prefix` and Think Tokens

So, what's the big deal with gen_prefix and why does it matter if models can't use think tokens? Well, some models, like gpt-oss, are specifically built and trained to use these tokens as part of their reasoning process. When you prevent them from doing so, you're essentially tying their hands behind their backs. They can't perform at their best, and the evaluation doesn't accurately reflect their true capabilities. Let's look at a specific example using a task defined in lm_eval/tasks/arc/arc_challenge_chat.yaml.

Consider this prompt, which includes a system message defining the model's persona and instructions:

'<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2025-11-13\n\nReasoning: low\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>user<|message|>Given the following question and four candidate answers (A, B, C and D), choose the best answer.\nQuestion: An astronaut drops a 1.0 kg object and a 5.0 kg object on the Moon. Both objects fall a total distance of 2.0 m vertically. Which of the following best describes the objects after they have fallen a distance of 1.0 m?\nA. They have each lost kinetic energy.\nB. They have each gained the same amount of potential energy.\nC. They have each lost the same amount of potential energy.\nD. They have each gained one-half of their maximum kinetic energy.\nYour response should end with "The best answer is [the_answer_letter]" where the [the_answer_letter] is one of A, B, C or D.<|end|><|start|>assistant<|channel|>final<|message|>The best answer is'

With gen_prefix: 'The best answer is', the model directly outputs: ' D<|return|>'. This is a concise answer, but it lacks any reasoning or intermediate steps. It's just the final answer, and we don't get to see the model's thought process.

Now, let's see what happens when we remove gen_prefix:

'<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\nKnowledge cutoff: 2024-06\nCurrent date: 2025-11-13\n\nReasoning: low\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.<|end|><|start|>user<|message|>Given the following question and four candidate answers (A, B, C and D), choose the best answer.\nQuestion: An astronaut drops a 1.0 kg object and a 5.0 kg object on the Moon. Both objects fall a total distance of 2.0 m vertically. Which of the following best describes the objects after they have fallen a distance of 1.0 m?\nA. They have each lost kinetic energy.\nB. They have each gained the same amount of potential energy.\nC. They have each lost the same amount of potential energy.\nD. They have each gained one-half of their maximum kinetic energy.<|end|><|start|>assistant'

Without the prefix, the model generates a more detailed response:

'<|channel|>analysis<|message|>We need after falling 1m out of 2m. Initially at rest. Potential energy loss = m g h. For 1m, potential lost = m g *1. Kinetic gained = same. So each has gained kinetic equal to potential lost. They have each gained the same amount of kinetic energy? Not listed. They have each lost the same amount of potential energy? For 1m, potential lost proportional to mass, so not same. They have each gained one-half of their maximum kinetic energy. Max kinetic at bottom after 2m: KE = m g *2. So at 1m, KE = m g *1 = half of max. So D.<|end|><|start|>assistant<|channel|>final<|message|>The best answer is D<|return|>'

In this scenario, the model provides a step-by-step analysis before arriving at the final answer, 'D'. Even though the final answer is the same in both cases, the reasoning process is crucial for understanding how the model is working and for potentially identifying areas for improvement. In other scenarios, this thinking can dramatically improve the accuracy and robustness of the answers.

Why Thinking Helps: The Bigger Picture

The ability to "think" or reason through a problem is a hallmark of advanced language models. When we constrain models by preventing them from using think tokens, we might be missing out on the full potential of their capabilities. Think tokens allow models to:

Break down complex problems: By using intermediate steps, models can tackle intricate questions that would be impossible to answer directly.
Provide explanations: The reasoning process offers insights into why a model arrived at a particular conclusion, making it easier to debug and improve.
Handle ambiguity: Think tokens can help models explore different interpretations and clarify ambiguous questions.
Improve accuracy: In many cases, a well-reasoned answer is more likely to be correct than a direct guess.

By allowing models to engage in this thinking process, we can achieve more reliable and transparent results. This is especially crucial in tasks where the reasoning itself is as important as, or more important than, the final answer.

A Potential Solution: Combining Thinking and `gen_prefix`

So, how can we reconcile the benefits of gen_prefix with the importance of allowing models to think? Ideally, we want a system where the model can:

Engage in a thinking process, utilizing think tokens to reason through the problem.
Generate content until a specific trigger, such as <|start|>assistant<|channel|>final<|message|>, is generated.
Append the gen_prefix to this point.
Continue generating from there, incorporating the prefix into the final output.

This approach would enable models to leverage their reasoning abilities while still adhering to the desired output format enforced by gen_prefix. Let's explore some possible implementation strategies.

Implementation Strategies

There are several ways we could potentially implement this combined approach:

Modify the Generation Logic: The core generation logic within lm-evaluation-harness could be modified to detect the trigger sequence (e.g., <|start|>assistant<|channel|>final<|message|>). Once detected, the gen_prefix would be inserted, and generation would continue. This would require careful modification of the sampling and tokenization process.
Introduce a New Parameter: A new parameter could be introduced to specify a "thinking terminator" sequence. The model would generate freely until this sequence is encountered, at which point the gen_prefix would be appended. This would provide a more configurable approach.
Post-Processing: The model could be allowed to generate freely, including think tokens. Then, a post-processing step could be applied to identify the trigger sequence, insert the gen_prefix, and truncate any extraneous tokens. This approach might be simpler to implement but could potentially introduce inconsistencies.

Each of these strategies has its own trade-offs in terms of complexity, flexibility, and potential impact on performance. However, the goal remains the same: to enable models to think effectively while still adhering to the desired output format.

Use Cases and Benefits

The ability to combine thinking with gen_prefix would have significant benefits across a wide range of use cases:

Complex Reasoning Tasks: Tasks that require multi-step reasoning, such as mathematical problem-solving or logical deduction, would greatly benefit from this approach.
Creative Writing: Models could use think tokens to explore different narrative possibilities before settling on a final output, enhancing creativity and originality.
Dialogue Generation: Models could reason about the context of a conversation before generating a response, leading to more coherent and engaging dialogues.
Code Generation: Models could use think tokens to plan the structure and logic of a program before generating the code itself, improving the quality and reliability of the generated code.

By enabling thinking in conjunction with gen_prefix, we can unlock the full potential of language models and achieve more impressive results in a variety of domains. This will enable us to more accurately evaluate the true capabilities of these models.

Conclusion

In conclusion, the current incompatibility between gen_prefix and enable_thinking=True represents a significant limitation in the lm-evaluation-harness. By preventing models from using think tokens, we risk underestimating their true capabilities and hindering their performance on complex tasks. Allowing thinking along a template start output, where models can reason until a specific sequence is generated, then adding the gen_prefix and continuing generation, is a promising solution. Implementing this approach would require careful consideration of the generation logic and could involve modifying the existing code or introducing new parameters. However, the potential benefits are substantial, including improved accuracy, enhanced reasoning, and a more transparent evaluation process. Let's work together to explore these solutions and unlock the full potential of language models.

Thanks for reading, guys! Let me know your thoughts and suggestions in the comments below!