kelvinyelyen/tiny-aya-global-blindspots

tiny-aya-global: Blind Spot Evaluation

Model: CohereLabs/tiny-aya-global — ~3B parameter multilingual conversational model Dataset: kelvinyelyen/tiny-aya-global-blindspots

Ten targeted probes designed to surface specific failure mechanisms, not aggregate accuracy. Each probe was run once under greedy decoding (temperature=0.0), then re-run 5x under sampling (temperature=0.7) to check whether each failure is a stable pattern or a one-off. Result: 7 clear failures, 1 pass, 1 partial (correct content, non-compliant format), 1 excluded as a harness artifact (see below).

How It Was Run

def generate_reply(user_text, max_new_tokens=160):
    messages = [{"role": "user", "content": user_text}]
    inputs = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, tokenize=True,
        return_dict=True, return_tensors="pt"
    ).to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False, temperature=0.0)
    generated_tokens = outputs[0][inputs["input_ids"].shape[-1]:]
    return tokenizer.decode(generated_tokens, skip_special_tokens=True)

Model loaded 4-bit quantized (bitsandbytes, NF4) via transformers, in Google Colab. Deterministic decoding for the primary run; sampled re-runs used do_sample=True, temperature=0.7, top_p=0.9.

Dataset Structure

Field	Description
input	Prompt given to the model
expected_output	Correct or ideal response
model_output	The model's actual generated response (greedy run)

Findings, Grouped by Mechanism

1. Exact enumeration is unreliable, at any granularity

Prompt	Expected	Model said
Letters in "mississippi"	11	17
Words in "Vector spaces are abstract but useful."	6	7

Two failures at different granularities of the same underlying task — count discrete units, report a number. Consistent with subword tokenization denying the model direct access to character-level structure, so exact counting has to be simulated rather than read off.

2. Confident failure on an actual low-resource language

Prompt: Translate into Twi: "I forgot my laptop charger at home." Model output: **"M I forget fy laptop charger at home."**

Not a mistranslation — barely-modified English, presented in bold as if confident and complete. For a model positioned as broadly multilingual, failing silently (no hedge, no acknowledgment) on a genuine low-resource language is a gap between claimed coverage and actual capability. Of everything tested, this is the finding most worth a dedicated follow-up.

3. Explicit hedging instructions get ignored under confident staleness

Prompt: "Name the current president of Ghana. If unsure, say 'I'm not sure'." Model output: "Nana Akufo-Addo is the current president of Ghana." (stale, no hedge applied.)

The hedge instruction was explicit and in the prompt itself — this isn't a knowledge gap, it's the model discarding a directly-stated meta-instruction in favor of a confident-sounding completion. See Related Work for a fine-tune attempt targeting this specific behavior.

4. Formal reasoning produced the polar-opposite conclusion

Prompt: "If A implies B, and B is false, what can we conclude about A?" Model output: "...then A must be true." (correct answer: A must be false — contrapositive.)

An inversion, not a near-miss. A confidently-stated wrong deduction that still reads as fluent reasoning is harder to catch than an obviously broken answer.

5. Constraint violations vary in severity

Prompt	Target	Output	Severity
"Explain overfitting in exactly 12 words"	12 words	3 words	Severe
"Which ocean lies south of Ghana? One word."	1 word	"Atlantic Ocean."	Minor — content correct, technically 2 words

Excluded from scoring: harness artifact, not a model failure

Prompt: "Write Python to check palindrome ignoring spaces and case." Model output: Correct, well-commented code, cut off mid-line by max_new_tokens=160.

A limitation of the eval setup, not the model — flagged rather than folded into the failure count.

One clean pass

Prompt: "What is the best programming language? Ask ONE clarifying question only." Model output: "What specific use case or project are you working on, as different languages excel in different areas?"

Included for honesty — a probe set with zero passes would be a red flag for selection bias.

Consistency Check

Every finding above is a single greedy-decoded run, which shows a failure is possible, not that it's typical. Each probe was re-sampled 5x at temperature=0.7, and the five mechanically-checkable probes (exact number, exact word count, exact one-word match) were scored automatically across all samples:

objective_checks = {
    0: lambda out: check_word_count(out, 12),
    1: lambda out: contains_number(out, 11),
    6: lambda out: contains_number(out, 3),
    7: lambda out: check_one_word_answer(out, "atlantic"),
    9: lambda out: contains_number(out, 6),
}

A low pass rate alongside a failing greedy run means the failure is stable, not a decoding fluke. A pass rate that varies a lot across samples points to genuine uncertainty rather than a consistent error, which is itself worth reporting separately from a flat "wrong."

Related Work

A small LoRA adapter was built to test whether the hedge-instruction failure (finding #3) is fixable with a narrow, targeted fine-tune: kelvinyelyen/tiny-aya-global-hedge-lora.

Status: in progress. An initial before/after comparison produced identical outputs in both columns across every held-out prompt, which turned out to be a bug in the evaluation code (the "before" and "after" generation calls were both hitting the adapter-modified model, not a clean baseline) rather than evidence the fine-tune had no effect. Fixing the baseline-comparison logic before trusting any result from that adapter.

Limitations

• n=10 distinct probes, single prompt each — the consistency check adds repeated sampling per probe, but the set of probes itself is still small and was constructed by one evaluator.
• No inter-rater check — one evaluator judged correctness throughout.
• No baseline model run on the same prompts, so it isn't established which of these are specific to tiny-aya-global versus common to small instruction-tuned models generally.

Citation

@misc{tiny_aya_global_blindspots,
  title  = {tiny-aya-global Blind Spot Evaluation},
  author = {Kelvin},
  year   = {2026},
  url    = {https://huggingface.co/datasets/kelvinyelyen/tiny-aya-global-blindspots}
}