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pith:7YTDWIM7

pith:2025:7YTDWIM7RKAEGPLVOA5TXKCDQK

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Layer by Layer: Uncovering Hidden Representations in Language Models

Dan Zhao, Jalal Naghiyev, Md Rifat Arefin, Niket Patel, Oscar Skean, Ravid Shwartz-Ziv, Yann LeCun

Intermediate layers in language models often encode richer representations than the final layer for downstream tasks.

arxiv:2502.02013 v2 · 2025-02-04 · cs.LG · cs.AI · cs.CL

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Claims

C1strongest claim

our analysis shows that intermediate layers can encode even richer representations, often improving performance on a range of downstream tasks... intermediate layers consistently provide stronger features, challenging the standard view on final-layer embeddings

C2weakest assumption

that the proposed metrics based on information theory, geometry, and invariance to input perturbations accurately capture representation quality relevant to downstream task performance

C3one line summary

Intermediate layers in LLMs consistently provide stronger features than final layers across tasks and architectures, as quantified by a new framework of information-theoretic, geometric, and invariance metrics.

References

173 extracted · 173 resolved · 0 Pith anchors

[1] Agrawal, K. K., Mondal, A. K., Ghosh, A., and Richards, B. - ReQ : Assessing representation quality in self-supervised learning by measuring eigenspectrum decay. NeurIPs, 2022 2022

[2] Alain, G. and Bengio, Y. Understanding intermediate layers using linear classifier probes. ICLR, 2017 2017

[3] R., Subbaraj, G., Gontier, N., LeCun, Y., Rish, I., Shwartz-Ziv, R., and Pal, C 2025

[4] Information theory with kernel methods 2022

[5] BeIT : Bert pre-training of image transformers 2022

Formal links

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Cited by

29 papers in Pith

Uncovering the Latent Potential of Deep Intermediate Representations

TAPIOCA: Why Task- Aware Pruning Improves OOD model Capability

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations

Inference-Time Machine Unlearning via Gated Activation Redirection

Scale Determines Whether Language Models Organize Representation Geometry for Prediction

Receipt and verification

First computed	2026-05-17T23:38:50.916364Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

fe263b219f8a80433d75703b3ba84382a8ad6d6c910f2ab1c3801b05f4583f8f

Aliases

arxiv: 2502.02013 · arxiv_version: 2502.02013v2 · doi: 10.48550/arxiv.2502.02013 · pith_short_12: 7YTDWIM7RKAE · pith_short_16: 7YTDWIM7RKAEGPLV · pith_short_8: 7YTDWIM7

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: fe263b219f8a80433d75703b3ba84382a8ad6d6c910f2ab1c3801b05f4583f8f

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-02-04T05:03:42Z",
    "title_canon_sha256": "9a78a8101cc4fdc413f47784f866b9506c7f104983fbe8b6d18d8b7782bb0377"
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