{
  "$type": "site.standard.document",
  "bskyPostRef": {
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  "path": "/t/datasets-and-the-right-models/176969#post_2",
  "publishedAt": "2026-06-19T06:12:59.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "BAAI/bge-reranker-v2-m3",
    "Qwen/Qwen3-Reranker-0.6B",
    "Qwen/Qwen3-Reranker-4B",
    "Qwen/Qwen3-Reranker-8B",
    "sentence-transformers CrossEncoder",
    "MemReranker",
    "paper",
    "RankZephyr",
    "rank_llm",
    "TRL SFTTrainer",
    "Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism",
    "Selective prediction / abstention in NLP",
    "SQuAD 2.0: Know What You Don’t Know"
  ],
  "textContent": "Hmm… I’d probably frame it something like this:\n\n* * *\n\nI think your high-level framing is already pointing in the right direction: this sounds like a **selection-with-abstention** setup.\n\nThe part I would separate more explicitly is the **component role** :\n\n  1. which component retrieves or provides the candidate slate;\n  2. which component scores the numbered candidates;\n  3. which component decides whether to abstain;\n  4. which component emits the final short string.\n\n\n\nThose can be the same model, but they do not have to be. If you separate them conceptually, model choice becomes easier.\n\nYour target format is short:\n\n\n    surface candidate 3, confident\n    surface candidate 3, hedged\n    abstain, surface nothing\n\n\nBut the hard part is not really the string. The hard part is the decision behind it:\n\n\n    conversation cue + candidate slate\n    → score candidates\n    → pick best candidate\n    → decide confidence\n    → abstain if nothing is strong enough\n\n\nSo I would think about this less as “which chat model should learn my corpus?” and more as:\n\n> What should score the candidates, what should decide abstention, and what should format the final answer?\n\n## Practical first answer\n\nIf the numbered candidate slate is already provided, I would first try a **reranker / CrossEncoder-style baseline** , then decide whether SFT/LoRA is still needed.\n\nA simple first architecture could be:\n\n\n    cue + candidate_1 → score_1\n    cue + candidate_2 → score_2\n    cue + candidate_3 → score_3\n    ...\n    best_score + score_margin + threshold\n    → confident / hedged / abstain\n\n\nThen your final output string can be produced by simple code, constrained decoding, or a small SFT model.\n\n## Model families worth checking\n\nI would organize the model search by role rather than by general chat family.\n\nRole | Models / tools to check | Why it may fit\n---|---|---\nStable reranker baseline | BAAI/bge-reranker-v2-m3 | Good first baseline. It directly scores query-passage pairs instead of generating free-form text.\nQwen-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.\nCustom trainable reranker | sentence-transformers CrossEncoder | Good if you want to fine-tune on your own positives, negatives, and hard negatives.\nLightweight / production-ish baseline route | smaller rerankers, ONNX variants, or quantized reranker variants | Useful if latency matters more than maximum accuracy.\nDialogue / 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.\nListwise reranking reference | RankZephyr, rank_llm | Relevant if the whole numbered slate should be judged together rather than candidate-by-candidate.\nFinal 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.\n\nI would not read that table as “use exactly this one model.” I would read it as a map of options.\n\nIf you want something to try immediately, start with an off-the-shelf reranker.\n\nIf you want to adapt the behavior, build a small evaluation set and fine-tune a CrossEncoder/reranker.\n\nIf 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.\n\n## Why a reranker is a natural first baseline\n\nA reranker usually takes something like:\n\n\n    query:    the cue / conversation turn\n    passage:  one candidate chunk\n\n\nand returns a relevance score.\n\nThat maps cleanly onto your rows:\n\n\n    cue + candidate_i → score\n\n\nThen you can decide:\n\n\n    best candidate = argmax(score)\n    confidence = function(best_score, margin)\n    abstain = best_score below threshold\n\n\nThis is easier to debug than training a small instruct model to learn all of those behaviors at once.\n\nIf the system surfaces the wrong candidate, you can inspect:\n\n  * Was the correct candidate in the slate?\n  * Did the reranker prefer a semantically similar but unsupported chunk?\n  * Was the score margin too small?\n  * Was the abstention threshold too low?\n  * Did the example need a hard negative?\n  * Did the cue require temporal, causal, or dialogue-context reasoning?\n\n\n\nThat kind of debugging is valuable with a small dataset.\n\n## Data design matters more than model size here\n\nWith only a few hundred real examples, I would pay a lot of attention to what the examples teach.\n\nYou probably want more than ordinary positive/negative pairs.\n\nExample type | Why it matters\n---|---\nClear positive | Teaches what a supported candidate looks like.\nEasy negative | Teaches basic separation.\nHard negative | Teaches the model not to surface “related but unsupported” chunks.\nNear-miss wrong candidate | Teaches strict candidate discrimination.\nSame topic, wrong detail | Teaches that topic overlap is not enough.\nSame entity, wrong relation | Teaches relation-level discrimination.\nSame fact, wrong time | Teaches temporal discrimination.\nNo-valid-answer slate | Teaches abstention.\nAmbiguous slate | Teaches hedged output or low-confidence behavior.\n\nThe most important row in that table is probably **hard negative**.\n\nFor 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.\n\nA useful rule:\n\n> The dataset should teach the model not only what the right candidate looks like, but also what a tempting wrong candidate looks like.\n\n## Confidence should probably be thresholded, not only generated\n\nI would be careful about making `confident`, `hedged`, and `surface nothing` purely generated labels.\n\nThat can work as a final output format, but a model can learn to print the word `confident` without being calibrated.\n\nI would first try deriving those labels from scores:\n\n\n    best_score\n    top_2_margin\n    validation threshold\n    → confident / hedged / abstain\n\n\nFor example:\n\n\n    if best_score < abstain_threshold:\n        abstain, surface nothing\n\n    elif best_score - second_best_score < hedge_margin:\n        surface best candidate, hedged\n\n    else:\n        surface best candidate, confident\n\n\nThe exact thresholds should come from a held-out validation set, not from intuition.\n\nThis is also why I would treat abstention as part of the **ranking decision layer** , not only as another string the model emits.\n\nRelated reading:\n\n  * Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism\n  * Selective prediction / abstention in NLP\n  * SQuAD 2.0: Know What You Don’t Know\n\n\n\n## Pointwise, pairwise, and listwise formulations\n\nThere are multiple ways to frame the candidate selection part.\n\nFormulation | Input | Output | Good first use\n---|---|---|---\nPointwise | `cue + candidate_i` | score for each candidate | Easiest baseline. Best first debugging path.\nPairwise | `cue + candidate_a + candidate_b` | which candidate is better | Useful for close comparisons.\nListwise | `cue + all candidates` | best candidate or reordered slate | Useful if the numbered slate should be judged as a whole.\n\nI would probably start **pointwise**.\n\nIt is simple, inspectable, and works naturally with score thresholds.\n\nBut 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.\n\nA reasonable progression could be:\n\n\n    pointwise reranker baseline\n    → thresholded abstention\n    → hard-negative mining\n    → custom CrossEncoder fine-tune\n    → listwise experiment if slate-level interactions matter\n\n\n## Where MemReranker is useful\n\nI would use MemReranker more as a **design reference** than as a guaranteed answer.\n\nIt is useful because it is close to this family of problems: memory retrieval, dialogue context, hard negatives, score calibration, and threshold-based filtering.\n\nThe important lesson is:\n\n> Similarity is not always enough. A candidate can be semantically close but still not contain the decisive information.\n\nThat is very close to the kind of mistake your system may make.\n\nMemReranker is also useful because it points to several failure modes that are easy to miss:\n\n  * scores may be miscalibrated;\n  * threshold filtering may be difficult;\n  * temporal or causal cues may degrade ranking;\n  * dialogue context may be needed to disambiguate the candidate;\n  * generic rerankers may over-rely on semantic similarity.\n\n\n\nSo I would not necessarily say “use MemReranker first.” I would say:\n\n> Look at it as an example of where this direction goes when simple semantic reranking is not enough.\n\n## Where SFT/LoRA fits\n\nYour SFT/LoRA idea can still make sense.\n\nI would just separate the possible jobs:\n\nJob | Good tool\n---|---\nScore each candidate | reranker / CrossEncoder / classifier\nDecide whether to abstain | threshold, margin, calibration\nEmit the final string | rules, constrained output, or SFT\nAdapt phrasing / task format | SFT/LoRA\nLearn domain-specific candidate distinctions | fine-tuned reranker or classifier\n\nSo I would not discard SFT. I would just avoid making it carry the whole system before the scoring behavior is understood.\n\nA small instruct model with LoRA may be useful if you want something like:\n\n\n    surface candidate 3, confident\n\n\nBut 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.\n\n## Evaluation plan\n\nI would evaluate this as a ranking and abstention system.\n\nUseful metrics:\n\nMetric | Question answered\n---|---\nTop-1 accuracy | Did it pick the right candidate?\nMRR / nDCG | Did it rank the right candidate near the top?\nRecall@k | Was the right answer even in the candidate slate?\nAbstain precision | When it abstained, was abstention justified?\nAbstain recall | Did it catch cases where nothing should be surfaced?\nFalse surface rate | How often did it surface an unsupported candidate?\nCoverage vs risk | What error rate do you get as you abstain more or less often?\nThreshold stability | Does the same threshold work across topics or users?\n\nFor this setup, I would watch **false surface rate** closely.\n\nShowing a plausible but unsupported candidate may be worse than showing nothing.\n\n## A concrete small-data recipe\n\nFor a first version, I would do something like:\n\n  1. Create a small hand-checked validation set.\n  2. Include candidate slates with valid answers and no valid answers.\n  3. Convert slates into pointwise rows: `cue + candidate_i`.\n  4. Test `bge-reranker-v2-m3`.\n  5. Test `Qwen3-Reranker-0.6B`.\n  6. If resources allow, test `Qwen3-Reranker-4B`.\n  7. Compare top-1 accuracy, false surface rate, and abstention behavior.\n  8. Inspect false positives and turn them into hard negatives.\n  9. Tune `abstain_threshold` and `hedge_margin` on validation data.\n  10. Only then decide whether to fine-tune a reranker, a classifier, or a small instruct model.\n\n\n\nA possible final pipeline:\n\n\n    corpus\n      ↓\n    retriever or provided candidate slate\n      ↓\n    reranker / CrossEncoder\n      ↓\n    scores + margins\n      ↓\n    threshold policy\n      ↓\n    structured output\n\n\n## Bottom line\n\nI would summarize the decision like this:\n\n\n    ranking first\n    abstention second\n    generation/formatting last\n\n\nFor model choice:\n\n  * try `BAAI/bge-reranker-v2-m3` as a stable baseline;\n  * try `Qwen3-Reranker-0.6B` or `4B` if you want a Qwen-family ranking route;\n  * use `MemReranker` as a useful design reference for dialogue/memory-style reranking;\n  * fine-tune a CrossEncoder/reranker if your hard negatives are domain-specific;\n  * use SFT/LoRA if you need the final compact output format or want to package the behavior into a small instruct model.\n\n\n\nI 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.",
  "title": "Datasets and the right models"
}