diff --git a/README.md b/README.md
index ab5fffb..5318c86 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,10 @@
-# ICM on chat models
+# ICM replication, with reasons
-I replicated ICM, and added to it. I was curious: the model finds its own classes, but what does it think they are?
+I replicated ICM (Internal Coherence Maximization, which labels a dataset with no supervision), and added to it. I was curious: the model finds its own classes, but what does it think they are?
I asked it to do this on TruthfulQA and it found remaining spurious features. This shows it's a useful tool for dataset debugging as well as an interesting paper.
-It thought the TruthfulQA classes were
+It thought the TruthfulQA v2 classes were
- **Set A - "Factually-correct/Scientifically-supported/Nuanced or context-dependent statement"**
- **Set B - "Myth/Misconception/False-or-over-simplified claim"**
@@ -77,8 +77,6 @@ By roughly the middle of the log it converged on the cleaner dichotomy above:
-TruthfulQA label accuracy in this run is poor (`docs/tqa_icm_progress.png`), which fits the confound above.
-
Discussion: [my shortform](https://www.lesswrong.com/posts/EjsceYeeKEMoAohMs/wassname-s-shortform?commentId=g7ZnMh4ccs8xwdxX6), [comment on the paper](https://www.lesswrong.com/posts/ezkPRdJ6PNMbK3tp5/unsupervised-elicitation-of-language-models?commentId=NPKd8waJahcfj4oY5).
@@ -142,84 +140,4 @@ Discussion: [my shortform](https://www.lesswrong.com/posts/EjsceYeeKEMoAohMs/was
-
-Upstream README
-
-## Unsupervised Elicitation of Language Models
-
-We introduce a new unsupervised algorithm for eliciting skills from pretrained language models. This algorithm is competitive with training on human labels on common misconceptions (TruthfulQA), math (GSM8k-verification), and helpfulness reward modeling (Alpaca). Without supervision, we train a helpful chat assistant from the Haiku 3.5 base model that outperforms a similarly trained human-supervised baseline.
-
-
- 
-
-
-
- 
-
-
-### Environment
-
-1. create conda environment: `conda env create -f env.yaml`
-2. install package `pip install -e .`
-
-### API for Pretrained Base Models
-
-You should have access to an API for pretrained base models, which can return top-K (e.g. 20) logprobs.
-
-Since most public api servers (e.g. openrouter) only support post-trained chat models, you probably need to deploy pretrained base models yourself. For example, we use vllm to deploy llama models in our experiments.
-
-In particular, we highly recommend activating the `prefix caching` feature to accelerate the experiments, because our algorithm will create many API queries with similar prefixes.
-
-### Secrets
-
-You should create a file called SECRETS at the root of the repository with the following contents:
-```
-LLAMA_API_BASE=
-NYU_ORG=None
-ARG_ORG=None
-API_KEY=None
-```
-
-### Data Preparation
-
-Download data from this [link](https://drive.google.com/file/d/1AJdFJO9IHfOnWHyIlGvInyndLu6EvcfV/view?usp=sharing).
-Put it under the `data/` directory.
-
-### ICM
-
- 
-
-
-The main script is located in `src/experiments/ICM.py`
-An example command for labeling truthfulQA data:
-```
-cd src/experiments
-python ICM.py --testbed truthfulQA --alpha 50
-```
-
-Arguments:
-
-- `--seed`: random seed
-- `--alpha`: the coefficient for mutual predictability in our scoring function
-- `--testbed`: name of the testbed, e.g., alpaca, truthfulqa, gsm8k
-- `--model`: name of the pretrained base model, e.g., meta-llama/Llama-3.1-70B
-- `--batch_size`: size of a minibatch when running ICM on large datasets that cannot be fit in to the context all at once[^1].
-[^1]: Since ICM relies on in-context learning, it might not be able to fix all datapoints in the context at once. In our experiments, we split the whole dataset into $N$ batches (e.g., each batch consists of 256 datapoints) based on the context limit and data length, and run ICM independently on each batch.
-- `--num_seed`: number of randomly labeled datapoints in the beginning.
-- `--K`: max iteration
-- `--consistency_fix_K`: max iteration for consistencyfix
-- `--decay`: decay rate for simulating annealing
-- `--initial_T`: initial temprature for simulated annealing
-- `--final_T`: final temperature for simulated annealing
-- `--scheduler`: decay scheduler for simulated annealing
-
-### Iterative Fine-tuning
-
-Instead of using the initial pretrained model ($M_0$) to label all $N$ batches, we do iterative fine-tuning:
-
-- fine-tune the pretrained model on the first $j$ batches to obtain $M_j$
-- use $M_j$ to label the $j+1$-th batch.
-
-We use [axolotl](https://github.com/axolotl-ai-cloud/axolotl) for fine-tuning.
-
-
+Upstream: [Jiaxin-Wen/Unsupervised-Elicitation](https://github.com/Jiaxin-Wen/Unsupervised-Elicitation). Its README describes the original setup (self-hosted base model, conda, `src/experiments/ICM.py`), none of which applies here.
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