Datasets and the right models

Hmm… I’d probably frame it something like this:

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I think your high-level framing is already pointing in the right direction: this sounds like a selection-with-abstention setup.

The part I would separate more explicitly is the component role :

1. which component retrieves or provides the candidate slate;
2. which component scores the numbered candidates;
3. which component decides whether to abstain;
4. which component emits the final short string.

Those can be the same model, but they do not have to be. If you separate them conceptually, model choice becomes easier.

Your target format is short:

surface candidate 3, confident
surface candidate 3, hedged
abstain, surface nothing

But the hard part is not really the string. The hard part is the decision behind it:

conversation cue + candidate slate
→ score candidates
→ pick best candidate
→ decide confidence
→ abstain if nothing is strong enough

So I would think about this less as “which chat model should learn my corpus?” and more as:

What should score the candidates, what should decide abstention, and what should format the final answer?

Practical first answer

If the numbered candidate slate is already provided, I would first try a reranker / CrossEncoder-style baseline , then decide whether SFT/LoRA is still needed.

A simple first architecture could be:

cue + candidate_1 → score_1
cue + candidate_2 → score_2
cue + candidate_3 → score_3
...
best_score + score_margin + threshold
→ confident / hedged / abstain

Then your final output string can be produced by simple code, constrained decoding, or a small SFT model.

Model families worth checking

I would organize the model search by role rather than by general chat family.

Role	Models / tools to check	Why it may fit
Stable reranker baseline	BAAI/bge-reranker-v2-m3	Good first baseline. It directly scores query-passage pairs instead of generating free-form text.
Qwen-family reranker path	Qwen/Qwen3-Reranker-0.6B, Qwen/Qwen3-Reranker-4B, Qwen/Qwen3-Reranker-8B	If you are already trying Qwen, these are more directly aligned with ranking than ordinary Qwen chat models.
Custom trainable reranker	sentence-transformers CrossEncoder	Good if you want to fine-tune on your own positives, negatives, and hard negatives.
Lightweight / production-ish baseline route	smaller rerankers, ONNX variants, or quantized reranker variants	Useful if latency matters more than maximum accuracy.
Dialogue / memory retrieval design reference	MemReranker, paper	Not necessarily the first thing to use, but very relevant as an example of reranking with dialogue context, hard negatives, calibrated scores, and threshold filtering.
Listwise reranking reference	RankZephyr, rank_llm	Relevant if the whole numbered slate should be judged together rather than candidate-by-candidate.
Final structured-output model	small instruct model + LoRA/SFT, for example via TRL SFTTrainer	Useful if you want the final answer to always follow a compact string format.

I would not read that table as “use exactly this one model.” I would read it as a map of options.

If you want something to try immediately, start with an off-the-shelf reranker.

If you want to adapt the behavior, build a small evaluation set and fine-tune a CrossEncoder/reranker.

If you want the final output to be a compact natural-language or command-like string, SFT/LoRA can still be useful as the final formatting layer.

Why a reranker is a natural first baseline

A reranker usually takes something like:

query:    the cue / conversation turn
passage:  one candidate chunk

and returns a relevance score.

That maps cleanly onto your rows:

cue + candidate_i → score

Then you can decide:

best candidate = argmax(score)
confidence = function(best_score, margin)
abstain = best_score below threshold

This is easier to debug than training a small instruct model to learn all of those behaviors at once.

If the system surfaces the wrong candidate, you can inspect:

• Was the correct candidate in the slate?
• Did the reranker prefer a semantically similar but unsupported chunk?
• Was the score margin too small?
• Was the abstention threshold too low?
• Did the example need a hard negative?
• Did the cue require temporal, causal, or dialogue-context reasoning?

That kind of debugging is valuable with a small dataset.

Data design matters more than model size here

With only a few hundred real examples, I would pay a lot of attention to what the examples teach.

You probably want more than ordinary positive/negative pairs.

Example type	Why it matters
Clear positive	Teaches what a supported candidate looks like.
Easy negative	Teaches basic separation.
Hard negative	Teaches the model not to surface “related but unsupported” chunks.
Near-miss wrong candidate	Teaches strict candidate discrimination.
Same topic, wrong detail	Teaches that topic overlap is not enough.
Same entity, wrong relation	Teaches relation-level discrimination.
Same fact, wrong time	Teaches temporal discrimination.
No-valid-answer slate	Teaches abstention.
Ambiguous slate	Teaches hedged output or low-confidence behavior.

The most important row in that table is probably hard negative.

For this task, a bad candidate may look very relevant. It may mention the right entity, topic, or situation, but fail to support the cue. Random negatives will not teach that very well.

A useful rule:

The dataset should teach the model not only what the right candidate looks like, but also what a tempting wrong candidate looks like.

Confidence should probably be thresholded, not only generated

I would be careful about making confident, hedged, and surface nothing purely generated labels.

That can work as a final output format, but a model can learn to print the word confident without being calibrated.

I would first try deriving those labels from scores:

best_score
top_2_margin
validation threshold
→ confident / hedged / abstain

For example:

if best_score < abstain_threshold:
    abstain, surface nothing

elif best_score - second_best_score < hedge_margin:
    surface best candidate, hedged

else:
    surface best candidate, confident

The exact thresholds should come from a held-out validation set, not from intuition.

This is also why I would treat abstention as part of the ranking decision layer , not only as another string the model emits.

Related reading:

• Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism
• Selective prediction / abstention in NLP
• SQuAD 2.0: Know What You Don’t Know

Pointwise, pairwise, and listwise formulations

There are multiple ways to frame the candidate selection part.

Formulation	Input	Output	Good first use
Pointwise	cue + candidate_i	score for each candidate	Easiest baseline. Best first debugging path.
Pairwise	cue + candidate_a + candidate_b	which candidate is better	Useful for close comparisons.
Listwise	cue + all candidates	best candidate or reordered slate	Useful if the numbered slate should be judged as a whole.

I would probably start pointwise.

It is simple, inspectable, and works naturally with score thresholds.

But since your input is explicitly a numbered candidate slate, listwise reranking may eventually be worth testing. The caution is that listwise fine-tuning usually needs listwise-quality data. Independent positive/negative pairs are not always enough to train a good listwise ranker.

A reasonable progression could be:

pointwise reranker baseline
→ thresholded abstention
→ hard-negative mining
→ custom CrossEncoder fine-tune
→ listwise experiment if slate-level interactions matter

Where MemReranker is useful

I would use MemReranker more as a design reference than as a guaranteed answer.

It is useful because it is close to this family of problems: memory retrieval, dialogue context, hard negatives, score calibration, and threshold-based filtering.

The important lesson is:

Similarity is not always enough. A candidate can be semantically close but still not contain the decisive information.

That is very close to the kind of mistake your system may make.

MemReranker is also useful because it points to several failure modes that are easy to miss:

• scores may be miscalibrated;
• threshold filtering may be difficult;
• temporal or causal cues may degrade ranking;
• dialogue context may be needed to disambiguate the candidate;
• generic rerankers may over-rely on semantic similarity.

So I would not necessarily say “use MemReranker first.” I would say:

Look at it as an example of where this direction goes when simple semantic reranking is not enough.

Where SFT/LoRA fits

Your SFT/LoRA idea can still make sense.

I would just separate the possible jobs:

Job	Good tool
Score each candidate	reranker / CrossEncoder / classifier
Decide whether to abstain	threshold, margin, calibration
Emit the final string	rules, constrained output, or SFT
Adapt phrasing / task format	SFT/LoRA
Learn domain-specific candidate distinctions	fine-tuned reranker or classifier

So I would not discard SFT. I would just avoid making it carry the whole system before the scoring behavior is understood.

A small instruct model with LoRA may be useful if you want something like:

surface candidate 3, confident

But I would still want to know whether that label comes from a reliable ranking score, a calibrated threshold, or only from the model’s generated wording.

Evaluation plan

I would evaluate this as a ranking and abstention system.

Useful metrics:

Metric	Question answered
Top-1 accuracy	Did it pick the right candidate?
MRR / nDCG	Did it rank the right candidate near the top?
Recall@k	Was the right answer even in the candidate slate?
Abstain precision	When it abstained, was abstention justified?
Abstain recall	Did it catch cases where nothing should be surfaced?
False surface rate	How often did it surface an unsupported candidate?
Coverage vs risk	What error rate do you get as you abstain more or less often?
Threshold stability	Does the same threshold work across topics or users?

For this setup, I would watch false surface rate closely.

Showing a plausible but unsupported candidate may be worse than showing nothing.

A concrete small-data recipe

For a first version, I would do something like:

1. Create a small hand-checked validation set.
2. Include candidate slates with valid answers and no valid answers.
3. Convert slates into pointwise rows: cue + candidate_i.
4. Test bge-reranker-v2-m3.
5. Test Qwen3-Reranker-0.6B.
6. If resources allow, test Qwen3-Reranker-4B.
7. Compare top-1 accuracy, false surface rate, and abstention behavior.
8. Inspect false positives and turn them into hard negatives.
9. Tune abstain_threshold and hedge_margin on validation data.
10. Only then decide whether to fine-tune a reranker, a classifier, or a small instruct model.

A possible final pipeline:

corpus
  ↓
retriever or provided candidate slate
  ↓
reranker / CrossEncoder
  ↓
scores + margins
  ↓
threshold policy
  ↓
structured output

Bottom line

I would summarize the decision like this:

ranking first
abstention second
generation/formatting last

For model choice:

• try BAAI/bge-reranker-v2-m3 as a stable baseline;
• try Qwen3-Reranker-0.6B or 4B if you want a Qwen-family ranking route;
• use MemReranker as a useful design reference for dialogue/memory-style reranking;
• fine-tune a CrossEncoder/reranker if your hard negatives are domain-specific;
• use SFT/LoRA if you need the final compact output format or want to package the behavior into a small instruct model.

I would avoid treating any one model as the guaranteed answer. The important part is to make the task measurable: candidate ranking, thresholded abstention, and false-surface rate.