Papers › Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation

Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation

4 Dec 2023arXiv:2312.01648archive 2025-07-28

Randall Balestriero, Romain Cosentino, Sarath Shekkizhar

Large Language Models (LLMs) drive current AI breakthroughs despite very little being known about their internal representations. In this work, we propose to shed the light on LLMs inner mechanisms through the lens of geometry. In particular, we develop in closed form (i) the intrinsic dimension in which the Multi-Head Attention embeddings are constrained to exist and (ii) the partition and per-region affine mappings of the feedforward (MLP) network of LLMs' layers. Our theoretical findings further enable the design of novel principled solutions applicable to state-of-the-art LLMs. First, we show that, through our geometric understanding, we can bypass LLMs' RLHF protection by controlling the embedding's intrinsic dimension through informed prompt manipulation. Second, we derive interpretable geometrical features that can be extracted from any (pre-trained) LLM, providing a rich abstract representation of their inputs. We observe that these features are sufficient to help solve toxicity detection, and even allow the identification of various types of toxicity. Our results demonstrate how, even in large-scale regimes, exact theoretical results can answer practical questions in LLMs. Code: https://github.com/RandallBalestriero/SplineLLM

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2312.01648")

Code

Syntology Ran 7 of 13 code samples harvested from 1 repository linked to this paper; 6 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 2 ran · fixture could not drive it; 4 ran with no contract checked.

By repository: official repository: 13 samples from 1 repository, 7 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

randallbalestriero/splinellm officialmentioned in paperpytorchNOASSERTION report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

13 samples harvested; 7 ran; 0 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
2ran · fixture could not drive it
4ran
6unverified

Licence: 13 of the 13 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from randallbalestriero/splinellm. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

repeat_kv randallbalestriero/splinellm/modeling_llama.py official repository ran · fixture could not drive it fingerprinted no licence file found · pointer only · 30d7eec482ebf6b1 · report
apply_rotary_pos_emb randallbalestriero/splinellm/modeling_llama.py official repository ran · fixture could not drive it no licence file found · pointer only · f725bc2d76076485 · report
fit_sup randallbalestriero/splinellm/statistic_analysis.py official repository ran licence not identified · pointer only · 12a12995c1053234 · report
load_data randallbalestriero/splinellm/statistic_analysis.py official repository ran licence not identified · pointer only · 23e3672bc122afcc · report
load_data randallbalestriero/splinellm/text_features_figure.py official repository ran licence not identified · pointer only · 14bcb35bbf9ee4d1 · report
rotate_half randallbalestriero/splinellm/modeling_llama.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · b99eea6376d1e212 · report
text_with_autofit randallbalestriero/splinellm/text_features_figure.py official repository ran licence not identified · pointer only · 949a366d9461e73b · report
classify_dataset randallbalestriero/splinellm/huggingface_classifiers.py official repository unverified licence not identified · pointer only · c2f133d308ead1c4 · report
clustering randallbalestriero/splinellm/statistic_analysis.py official repository unverified licence not identified · pointer only · 5c0bfe90ef68eed9 · report
generative_model_inference randallbalestriero/splinellm/generation_utils.py official repository unverified licence not identified · pointer only · f5cfb4bc1437b436 · report
get_dataset randallbalestriero/splinellm/data_utils.py official repository unverified licence not identified · pointer only · edb3dc8a5aa98bf3 · report
load_generative_model_and_tokenizer randallbalestriero/splinellm/generation_utils.py official repository unverified licence not identified · pointer only · 21e2a016b769f048 · report
load_pile randallbalestriero/splinellm/data_utils.py official repository unverified licence not identified · pointer only · 2315da591a30fd63 · report

Tasks

Language ModelingLanguage ModellingLarge Language Model

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

JigsawLinear LayerSoftmax

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections