README.md

metadata

license: other
license_name: lmsys-chat-1m
task_categories:
  - text-classification
language:
  - en
tags:
  - interpretability
  - steering-vectors
  - behavioural-evaluation
size_categories:
  - 100K<n<1M

lmsys-chat-1m concept classes

Every prompt in lmsys/lmsys-chat-1m labelled with the behavioural contrasts it gives a language model room to reveal.

What the labels mean

The 148 classes are behavioural contrasts -- sycophancy || principled independence, plain language || jargon, full capability display || sandbagging.

The ontology is not ours. It comes from AntonKorznikov/feature_stories: 148 classes over 1036 human-curated pairs, shipped there as ontology.json. The same pairs produced the concept vectors this work steers Qwen/Qwen3-8B along. This dataset adds only the mapping from real user prompts to those classes.

The labeller was not asked what a prompt is about, but whether it leaves room for the contrast to show:

Decide which classes this prompt gives a model ROOM TO REVEAL. Ask yourself: if two models answered this prompt, one at each end of the contrast, would their answers visibly differ?

Judge the prompt, not the answer. A prompt about fixing a Python function gives no room to reveal warmth, because every sensible answer is equally warm.

Schema

labels.parquet, one row per conversation, 990,061 rows:

column	type	meaning
conversation_id	string	joins to lmsys-chat-1m
class_1_id	int16	best-matching class, index into classes.parquet
class_1_score	float32	0-1, how much room the prompt gives that class
class_2_id, class_2_score		second match, null when absent
class_3_id, class_3_score		third match, null when absent

classes.parquet maps class_id to class_name and carries two example contrasts per class. Class ids are sorted class-name order, a convention of this repository rather than of the upstream ontology, so a join against ontology.json needs the explicit mapping.

Prompts that matched no class are omitted -- 1.0% of the corpus, 9,939 prompts. Note that this is far fewer than intended; see the limitations below.

How it was made

• Labeller: google/gemma-3-27b-it under vLLM, greedy decoding, one pass over the corpus.
• Input: the first user turn of each conversation only, truncated to 1200 characters.
• Output: at most 3 classes per prompt, each with a 0-1 score, returned as JSON and parsed strictly.

Known limitations

Read these before ranking anything by score.

The scores are ordinal, not calibrated. Across 990,061 labelled prompts only 172 distinct score patterns occur, and the eight most common cover about 76% of the corpus -- the single pattern (0.7, 0.4, 0.3) accounts for roughly a fifth on its own. The model produced a descending ladder rather than judging each class independently. Slot order is meaningful; the absolute number is close to arbitrary. Treat class_1_id as "best match" rather than reading 0.9 as meaningfully stronger than 0.8.

The labeller was not selective. 99.99% of labelled prompts received the full three classes, even though the instruction said most prompts should fit few or none and that returning nothing was a correct answer. Only 1.0% of the corpus came back empty. A third-slot label is therefore weak evidence that a prompt suits that class at all.

Coverage is very uneven. Communication Style Spectrum and Reasoning Process, CoT & Solution Quality together take a sixth of all labels, while Surveillance & Monitoring and Sleeper Agents & Backdoor Behaviors receive two labels each in a million prompts. Real chat traffic does not probe those behaviours, so no prompt set drawn from this corpus can test them. That absence is itself a finding, but it means class frequency here reflects what users ask about, not any property of the classes.

Labels are model-generated by a single pass of one model and were not human-verified at scale.

Joining to the text

from datasets import load_dataset

labels = load_dataset("josephofthebread/lmsys-chat-1m-concept-classes", data_files="labels.parquet")["train"]
source = load_dataset("lmsys/lmsys-chat-1m")["train"]
text = {row["conversation_id"]: row["conversation"][0]["content"] for row in source}

Licence and provenance

component	source	licence
the prompts being labelled	lmsys/lmsys-chat-1m	LMSYS-Chat-1M licence
the 148-class ontology	AntonKorznikov/feature_stories	Apache 2.0
the prompt-to-class labels	this repository	research use

The prompt text is not included here and remains under the LMSYS-Chat-1M licence; consult it before redistributing any joined result. Labels are model-generated and were not human-verified at scale.

Citation

Please cite the ontology and the source corpus alongside this dataset:

AntonKorznikov/feature_stories
lmsys/lmsys-chat-1m