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  "path": "/t/szukam-feedbacku-wlasna-reprezentacja-semantyczna-bryla-dla-malych-modeli/175350#post_2",
  "publishedAt": "2026-04-18T06:06:24.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "arxiv.org",
    "aclanthology.org",
    "babylm.github.io"
  ],
  "textContent": "Seems like a promising approach for now:\n\n* * *\n\n## My view\n\n**Yes — your idea makes sense.**\n\nNot as “I solved meaning,” but as:\n\n**“I gave a small model a better, more structured input, so it had less hidden work to do.”**\n\nThat is a real research direction. Recent work like **L2T** and **Structural Guidance for Transformer Language Models** argues that models can learn better when structure is made more explicit instead of leaving everything inside raw next-token prediction. (arxiv.org)\n\n## What bryła seems to be\n\nTo me, bryła looks like a mix of three things:\n\n  * **semantic representation** — who did what, what relates to what\n  * **discourse representation** — what continues the topic, what is central\n  * **control information** — urgency, emotion, strength of intent, source\n\n\n\nThat mix is interesting because standard meaning-representation work like **AMR** and **UMR** focuses more on events, arguments, coreference, time, and modality than on things like urgency or emotional color. So bryła is not just “another AMR clone.” It seems broader and more practical. (aclanthology.org)\n\n## What your result probably means\n\nYour result probably means:\n\n**“For this model and this dataset, bryła makes the learning problem easier.”**\n\nThat is already a strong result.\n\nI would be careful with a stronger claim like “the model understands better,” because when the representation changes, plain token-level perplexity can become hard to compare directly. The **Paloma** benchmark paper explicitly warns that perplexity is tied to tokenization. (arxiv.org)\n\nSo I would say:\n\n  * your result is **real**\n  * your result is **promising**\n  * your result is **not yet final proof of better semantic understanding**\n\n\n\n## What looks strongest in your numbers\n\nThis part stands out:\n\n  * **v7 → v8:** big gain\n  * **v8 → v9:** small gain\n\n\n\nThat usually means the first added signals did most of the work.\n\nSo my first guess would be:\n\n  * **affect**\n  * **core / salience**\n  * maybe **basic discourse structure**\n\n\n\nare doing more than the later pragmatic additions.\n\nThat is good news. It suggests you may already have the important part, and the next step is probably **simplifying** , not adding more tags.\n\n## Why it could work\n\nI think bryła may help in four simple ways.\n\n### 1. Less ambiguity\n\nDifferent sentences with similar meaning may become more similar after parsing. That makes learning easier. This is one of the classic reasons people use meaning representations like AMR. (aclanthology.org)\n\n### 2. Hidden information becomes visible\n\nSmall models often struggle to infer things like source, salience, continuity, or intent from a tiny corpus. bryła exposes those signals directly. That is very close to the logic behind structured pretraining and control-style conditioning. (arxiv.org)\n\n### 3. Shorter path to useful patterns\n\nInstead of forcing the model to discover everything from surface text, you hand it some of the structure up front. That is exactly the kind of shortcut that can help small models more than big ones. (aclanthology.org)\n\n### 4. Better controllability\n\nSome of your fields are not only “meaning.” They are also useful control variables. Recent work on continuous control signals is relevant here. (arxiv.org)\n\n## What I would improve next\n\nThese would be my top priorities.\n\n### 1. Find out which tags matter most\n\nDo ablations:\n\n  * remove affect\n  * remove is_core\n  * remove urgency\n  * remove source\n  * remove topic continuation\n  * remove relations\n\n\n\nRight now the biggest unanswered question is:\n\n**Which part of bryła is doing the real work?**\n\n### 2. Test raw text + bryła together\n\nDo not test only:\n\n  * raw text\n  * bryła only\n\n\n\nAlso test:\n\n  * **raw + bryła**\n\n\n\nMy guess is that this may become your best setup. Recent structured-pretraining work points more toward **hybrid setups** than total replacement of raw text. (arxiv.org)\n\n### 3. Add one or two real tasks\n\nNot only validation perplexity.\n\nTry tasks where your metadata should help:\n\n  * complaint vs request\n  * urgency classification\n  * source-aware summarization\n  * semantic retrieval\n  * dialogue-state tracking\n\n\n\nThat will make your claim much stronger.\n\n### 4. Test noise\n\nCorrupt some bryła tags on purpose.\n\nIf performance crashes instantly, the system may be brittle.\nIf it degrades slowly, that is much better evidence.\n\n### 5. Try continuous values for some fields\n\nFields like:\n\n  * urgency\n  * importance\n  * strength of intent\n  * emotional intensity\n\n\n\nmay work better as numbers or continuous embeddings than as discrete labels. That is a real open question, and recent control-signal work suggests it is worth testing. (arxiv.org)\n\n## What I would be careful about\n\n### 1. Perplexity comparisons\n\nIf tokenization or serialization changed, the comparison may be less clean than it looks. That does not make the gain fake, but it changes how strong the conclusion is. (arxiv.org)\n\n### 2. Shortcut labels\n\nSome fields might act like hints or labels rather than general semantics.\n\n### 3. Parser errors\n\nIf the parser is noisy, the whole system becomes noisy. The AMR/LLM literature shows that parser-generated structures can create cascading errors. (aclanthology.org)\n\n### 4. Overclaiming\n\nI would avoid saying:\n\n  * “I built a universal semantic representation”\n  * “I proved symbolic structure is better than raw text”\n\n\n\nI would say:\n\n  * “I built a semantic-pragmatic representation that seems to improve learning efficiency for small models.”\n\n\n\nThat is a strong claim and much easier to defend.\n\n## Where people like this gather\n\nFor people working on related ideas with limited resources, I would watch:\n\n  * **BabyLM** — best match for small-model, limited-data thinking (babylm.github.io)\n  * **LoResLM** — closer to low-resource language-model work (aclanthology.org)\n  * **DMR** — best fit if you want feedback on the representation itself (aclanthology.org)\n\n\n\n## Best use cases\n\nI think bryła may be especially good for:\n\n  * support / triage\n  * source-aware summarization\n  * semantic search\n  * dialogue memory / topic continuity\n  * low-resource or narrow-domain assistants\n\n\n\nWhy these? Because your tags are about **importance, source, continuity, and intent** — and those matter a lot in these settings. The AMR applications literature also shows that structured meaning is often most useful in targeted downstream tasks rather than everywhere equally. (aclanthology.org)\n\n## Bottom line\n\nMy simple verdict:\n\n  * **The idea makes sense**\n  * **The result is strong enough to be taken seriously**\n  * **The safest claim is “better structured supervision for small models,” not “solved meaning”**\n  * **Your next step should be ablations, hybrid input, and task-based evaluation**\n\n\n\nThat is the path from “interesting experiment” to “credible result.”",
  "title": "Szukam feedbacku — własna reprezentacja semantyczna \"bryła\" dla małych modeli"
}