Step 3: Implement the MT-Bench Evaluation Harness

Section 4 of 11-~ 6 min read-Synced from Cuantum content

MT-Bench evaluates conversational capability by running multi-turn dialogues. Unlike single-turn benchmarks that test isolated question-answering, MT-Bench simulates the messy reality of actual conversations—where context accumulates, constraints evolve, and users don't always ask perfectly formed questions. This matters because most production applications involve back-and-forth interaction, not one-shot queries.

You will create a small but meaningful subset that stresses the specific failure modes that emerge in multi-turn settings. These aren't random conversations—each one should be designed to probe a particular aspect of conversational competence:

Context retention: Can the model remember what was said three turns ago and use it to answer the current question? Or does it suffer from context amnesia, treating each turn as if the conversation just started?
Follow-up handling: When the user asks "Can you elaborate on that?" or "What about the opposite case?", does the model understand what "that" refers to? Follow-ups are implicit references to prior context, and models that can't resolve these references quickly become frustrating to use.
Correction and refinement: Users often change their minds mid-conversation. "Actually, ignore what I said about the budget constraint" or "Let's approach this differently" are common patterns. Your test set should include turns where the user walks back previous statements or pivots to a different angle. Models that can't handle this gracefully will produce responses that incorporate superseded constraints.
Format switching (detailed → concise): A user might ask for a detailed explanation, then immediately request "now give me the one-sentence version." This tests whether the model can adjust its verbosity while maintaining the core information. It also reveals whether the model is slavishly following a fixed template or genuinely adapting to changing user preferences.
Instruction hierarchy (later constraints override earlier ones): When instructions conflict across turns, which ones win? If turn 1 says "be formal" and turn 4 says "actually, be casual", does the model understand that recency matters? Or does it awkwardly try to satisfy both constraints simultaneously, producing responses that are neither formal nor casual but simply confused?

These stress tests are not edge cases. They represent the core dynamics of how people actually use conversational AI. A model that fails at any of these will produce conversations that feel broken or frustrating, even if it scores well on static benchmarks.

Create a JSON file: data/mtbench_conversations.json

Each conversation should be a list of user turns. The model will process these sequentially, with each response feeding into the context for the next turn. This cumulative structure is what makes multi-turn evaluation challenging—errors compound, and a single dropped reference can derail the entire conversation.

Example:

[  {    "id": "conv_001",    "turns": [      "Explain LoRA in simple terms.",      "Now explain it in one sentence.",      "Give a practical example of when LoRA is better than full fine-tuning.",      "Earlier you said it saves memory. Why exactly?"    ]  },  {    "id": "conv_002",    "turns": [      "Help me write a polite refund response to a customer.",      "Now make it shorter.",      "Now rewrite it to sound warmer and more empathetic, but still professional.",      "What information would you ask the customer to provide next?"    ]  }]

Notice the structure of these conversations. The first one tests technical explanation with progressive compression (detailed → one sentence) and backward reference ("Earlier you said..."). The second tests practical writing with iterative refinement (polite → shorter → warmer) and forward planning (what comes next?). Each conversation has a coherent through-line, but requires the model to track changing constraints across turns.

For a meaningful evaluation, aim for at least:

20 conversations
3–6 turns each

This might seem small compared to traditional benchmarks, but multi-turn evaluation is expensive—each conversation requires N forward passes where N is the number of turns, and the context window grows with each turn. Twenty conversations with four turns each means 80 model generations per model you're evaluating. With two models (base and fine-tuned), that's 160 generations just to complete one evaluation run.

Even a small set is enough to detect regressions. If your fine-tuned model starts losing track of context by turn 3, or refuses to follow format-switching instructions, you'll see it clearly in 20 conversations. You don't need thousands of examples to observe that a model has become worse at conversation—a handful of broken dialogues is sufficient evidence that something went wrong during training.

As you build your test set, prioritize diversity over volume. Each conversation should probe a different aspect of conversational competence. Avoid creating ten variations of the same conversation pattern—that doesn't give you more information, it just inflates your numbers. Instead, vary the domain (technical explanation, customer service, creative writing), the instruction pattern (compression, expansion, pivoting), and the reference structure (forward, backward, implicit).

Document why you included each conversation. When you discover that your model fails on conv_014, you want to be able to immediately understand what capability that conversation was testing. This documentation doesn't need to be elaborate—a single comment in your JSON file explaining the conversational pattern is enough. But it transforms your evaluation set from a black box into a diagnostic tool.