Issue Description

When fine-tuning language models in Keras 3, there are inconsistencies in how validation data should be provided. The documentation suggests validation_data should be in (x, y) format, but the actual requirements are unclear and the behavior differs between training and validation phases.

Current Behavior & Problems

Issue 1: Raw text arrays are not accepted for validation

train_texts = ["text1", "text2", ...]
val_texts = ["val1", "val2", ...]

# This fails with ValueError:
model.fit(
   train_texts,
   validation_data=val_texts
)

# Error:
ValueError: Data is expected to be in format `x`, `(x,)`, `(x, y)`, or `(x, y, sample_weight)`, found: ("text1", "text2", ...)

Issue 2: Pre-tokenized validation fails

# Trying to provide tokenized data:
val_tokenized = [tokenizer(text) for text in val_texts]
val_padded = np.array([pad_sequence(seq, max_len) for seq in val_tokenized])
val_input = val_padded[:, :-1]
val_target = val_padded[:, 1:]

model.fit(
    train_texts,
    validation_data=(val_input, val_target)
)

# Error:
TypeError: Input 'input' of 'SentencepieceTokenizeOp' Op has type int64 that does not match expected type of string.

The error suggests the tokenizer is being applied again to already tokenized data. I understand there is the preprocessor=None parameter, but I don't want to preprocess train data manually.

Working Solution (But Needs Documentation)

The working approach is to provide prompt-completion pairs:

# Prepare validation data as prompts and expected outputs
val_inputs = [format_prompt(text) for text in val_input_texts]
val_outputs = [format_output(text) for text in val_output_texts]
val_inputs = np.array(val_inputs)
val_outputs = np.array(val_outputs)

model.fit(
    train_texts,
    validation_data=(val_inputs, val_outputs)
)

Expected Behavior

1. The documentation should clearly state that validation data for language models should be provided as prompt-completion pairs
2. The validation data handling should be consistent with how training data is processed
3. It should be clear whether token shifting is handled internally or needs to be done manually

Environment

• Keras Version: 3.x
• Python Version: 3.10
• Model: Gemma LLM (but likely affects other LLMs too)

Additional Context

While there is a working solution using prompt-completion pairs, this differs from traditional language model training where each token predicts the next token. The documentation should clarify this architectural choice and explain the proper way to provide validation data.