EleutherAI
(https://eleuther.ai) 📸 Data Snapshot: May 29, 2026
Semantic Coherence — The Lens
Pull the main entities out of the H1, then check whether they actually recur through the body. A page that announces one thing and then talks about another drifts. Headings with no real sentences underneath read as pseudo-substance.
Semantic Coherence
Homepage promise vs. Sub-page reality.
20
Impact Weight: 20 / 100
100% Reputation
There is zero semantic drift between the homepage signal and sub-page substance. The homepage H1 ‘EleutherAI’ promises research exploration, and the sub-pages deliver comprehensive repositories of papers, models, and code libraries that align perfectly with that promise. No contradictions were found in service descriptions or target audience positioning across the audited pages.
Semantic Coherence is read from the heading hierarchy first: what each page announces in its H1 and headings, then whether the body actually delivers on it. Below is the structure the engine mapped, followed by the clean text to check for drift between promise and reality.
🏗️ Semantic Structure — heading hierarchy & page identity (the promise the page makes)
HOMEPAGE EleutherAI (https://eleuther.ai)
Title
EleutherAI
H1 EleutherAI
H3 Explore our research
H4 Recent Publications
H4 News
NAV_HEADER_REPEATED_FOOTER Language Modeling — EleutherAI (https://eleuther.ai/language-modeling/)
Title
Language Modeling — EleutherAI
H2 Language Modeling
H3 Current Projects
H3 Releases
H3 Papers
NAV_HEADER_REPEATED_FOOTER Interpretability — EleutherAI (https://eleuther.ai/interpretability/)
Title
Interpretability — EleutherAI
H2 Interpretability
H3 Current Projects
H3 Releases
H3 Publications
NAV_HEADER_REPEATED_FOOTER Alignment — EleutherAI (https://eleuther.ai/alignment/)
Title
Alignment — EleutherAI
H2 Alignment
H3 Current Projects
H3 Releases
H3 Papers
📝 The Narrative — clean text per page (homepage promise vs. sub-page reality)
HOMEPAGE (https://eleuther.ai) EleutherAI
[H1] EleutherAI [H3] Explore our research [IMG: Interpreting Across Time] Interpreting Across Time How do properties of models emerge and evolve over the course of training? [IMG: Eliciting Latent Knowledge] Eliciting Latent Knowledge As models get smarter, humans won't always be able to independently check if a model's claims are true or false. We aim to circumvent this issue by directly eliciting latent knowledge (ELK) inside the model’s activations. [IMG: Training LLMs] Training LLMs EleutherAI has trained and released many powerful open source LLMs. [H4] Recent Publications 16 February 2026 arXiv Quantifying the Effect of Test Set Contamination on Generative Evaluations 16 February 2026 arXiv As frontier AI systems are pretrained on web-scale data, test set contamination has become a critical concern for accurately assessing their capabilities. While research has thoroughly investigated the impact of test set contamination on discriminative evaluations like multiple-choice question-answering, comparatively little research has studied the impact of test set contamination on generative evaluations. In this work, we quantitatively assess the effect of test set contamination on generative evaluations through the language model lifecycle. We pretrain language models on mixtures of web data and the MATH benchmark, sweeping model sizes and number of test set replicas contaminating the pretraining corpus; performance improves with contamination and model size. Using scaling laws, we make a surprising discovery: including even a single test set replica enables models to achieve lower loss than the irreducible error of training on the uncontaminated corpus. We then study further training: overtraining with fresh data reduces the effects of contamination, whereas supervised finetuning on the training set can either increase or decrease performance on test data, depending on the amount of pretraining contamination. Finally, at inference, we identify factors that modulate memorization: high sampling temperatures mitigate contamination effects, and longer solutions are exponentially more difficult to memorize than shorter ones, presenting a contrast with discriminative evaluations, where solutions are only a few tokens in length. By characterizing how generation and memorization interact, we highlight a new layer of complexity for trustworthy evaluation of AI systems. 16 February 2026 arXiv 25 August 2025 Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs 25 August 2025 25 August 2025 9 July 2025 Composable Interventions for Language Models 9 July 2025 9 July 2025 8 July 2025 Evaluating Morphological Alignment of Tokenizers in 70 Languages 8 July 2025 8 July 2025 30 June 2025 Scaling Self-Supervised Representation Learning for Symbolic Piano Performance 30 June 2025 30 June 2025 [H4] News 18 May 2026 A short retrospective on the EleutherAI Summer of Open AI Research 18 May 2026 18 May 2026 7 July 2025 Summer of Open Science 7 July 2025 7 July 2025 15 June 2025 Common Pile v0.1 15 June 2025 15 June 2025 12 June 2025 EvalEval Coallition 12 June 2025 12 June 2025
3773 chars
SUB-PAGE (https://eleuther.ai/language-modeling/) Language Modeling — EleutherAI
[H2] Language Modeling The ability of a computer to understand, interpret, and generate human language is at the heart of what we do at EleutherAI. [H3] Current Projects [IMG: Training LLMs] Training LLMs [IMG: Evaluating LLMs] Evaluating LLMs [IMG: Polyglot] Polyglot [H3] Releases Library trlX Library A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF) Library Dataset Proof-Pile-2 Dataset A 55 billion token dataset of mathematical and scientific documents, created for training the LLeMA models. Dataset Model LLeMA Model Language models for mathematical applications Model Dataset OpenWebMath Dataset A 14.7B token dataset of high quality English mathematical text. Dataset Model Pythia Model A suite of models designed to enable controlled scientific research on transparently trained LLMs Model Model Polyglot-Ko Model A series of Korean autoregressive language models made by the EleutherAI polyglot team. We currently have trained and released 1.3B, 3.8B, and 5.8B parameter models. Model [H3] Papers 16 February 2026 arXiv Quantifying the Effect of Test Set Contamination on Generative Evaluations 16 February 2026 arXiv As frontier AI systems are pretrained on web-scale data, test set contamination has become a critical concern for accurately assessing their capabilities. While research has thoroughly investigated the impact of test set contamination on discriminative evaluations like multiple-choice question-answering, comparatively little research has studied the impact of test set contamination on generative evaluations. In this work, we quantitatively assess the effect of test set contamination on generative evaluations through the language model lifecycle. We pretrain language models on mixtures of web data and the MATH benchmark, sweeping model sizes and number of test set replicas contaminating the pretraining corpus; performance improves with contamination and model size. Using scaling laws, we make a surprising discovery: including even a single test set replica enables models to achieve lower loss than the irreducible error of training on the uncontaminated corpus. We then study further training: overtraining with fresh data reduces the effects of contamination, whereas supervised finetuning on the training set can either increase or decrease performance on test data, depending on the amount of pretraining contamination. Finally, at inference, we identify factors that modulate memorization: high sampling temperatures mitigate contamination effects, and longer solutions are exponentially more difficult to memorize than shorter ones, presenting a contrast with discriminative evaluations, where solutions are only a few tokens in length. By characterizing how generation and memorization interact, we highlight a new layer of complexity for trustworthy evaluation of AI systems. 16 February 2026 arXiv 12 February 2024 arXiv Suppressing Pink Elephants with Direct Principle Feedback 12 February 2024 arXiv 12 February 2024 arXiv 6 February 2024 arXiv Neural networks learn moments of increasing order 6 February 2024 arXiv 6 February 2024 arXiv 16 December 2023 ICLR Quality-Diversity through AI Feedback 16 December 2023 ICLR 16 December 2023 ICLR 16 December 2023 ICLR ReLoRA: High-Rank Training Through Low-Rank Updates 16 December 2023 ICLR 16 December 2023 ICLR 16 December 2023 NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) Eliciting Language Model Behaviors using Reverse Language Models 16 December 2023 NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 16 December 2023 NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 16 December 2023 NeurIPS Workshop (SoLaR) Eliciting Language Model Behaviors using Reverse Language Models 16 December 2023 NeurIPS Workshop (SoLaR) 16 December 2023 NeurIPS Workshop (SoLaR) 15 December 2023 NeurIPS Workshop (Math-AI) Llemma: An Open Language Model For Mathematics 15 December 2023 NeurIPS Workshop (Math-AI) 15 December 2023 NeurIPS Workshop (Math-AI) 15 December 2023 NeurIPS Workshop (Math-AI) OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text 15 December 2023 NeurIPS Workshop (Math-AI) 15 December 2023 NeurIPS Workshop (Math-AI) 15 December 2023 NeurIPS Emergent and Predictable Memorization in Large Language Models 15 December 2023 NeurIPS 15 December 2023 NeurIPS 14 December 2023 NeurIPS The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs 14 December 2023 NeurIPS Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, and Edward Grefenstette. "Large language models are not zero-shot communicators." arXiv preprint arXiv:2210.14986, 2022. 14 December 2023 NeurIPS 9 December 2023 ICML Workshop Do LLMs selectively encode the goal of an agent's reach? 9 December 2023 ICML Workshop 9 December 2023 ICML Workshop 8 December 2023 EMNLP trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback 8 December 2023 EMNLP Reinforcement learning from human feedback (RLHF) utilizes human feedback to better align large language models with human preferences via online optimization against a learned reward model. Current RLHF paradigms rely on Proximal Policy Optimization (PPO), which quickly becomes a challenge to implement and scale up to large architectures. To address this difficulty we present the trlX library as a feature-complete open-source framework for RLHF fine-tuning of models up to and exceeding 70 billion parameters. We implement support for multiple types of distributed training including distributed data parallel, model sharded, as well as tensor, sequential, and pipeline parallelism.To increase the accessibility of RLHF to researchers, we implement compute- and memory-saving features that give trlX the flexibility to support users with a wide range of compute resources. This includes offline RL methods like Implicit Language Q Learning (ILQL), low-rank adapters, and the Hydra architecture. We find offline fine-tuning offers competitive performance relative to online algorithms while being easier to implement, train, and scale. To evaluate our framework we train RLHF models on two separate well-known tasks using publicly available human preference data. Models trained with trlX achieve preference win-rates over baselines at rates comparable to the original works. 8 December 2023 EMNLP 6 December 2023 EMNLP (Findings) RWKV: Reinventing RNNs for the Transformer Era 6 December 2023 EMNLP (Findings) 6 December 2023 EMNLP (Findings) 24 October 2023 arXiv Linear Representations of Sentiment in Large Language Models 24 October 2023 arXiv 24 October 2023 arXiv 31 August 2023 arXiv YaRN: Efficient Context Window Extension of Large Language Models 31 August 2023 arXiv 31 August 2023 arXiv 8 August 2023 Workshop on Efficient Systems for Foundation Models @ ICML Continual Pre-Training of Large Language Models: How to (re)warm your model? 8 August 2023 Workshop on Efficient Systems for Foundation Models @ ICML 8 August 2023 Workshop on Efficient Systems for Foundation Models @ ICML 30 June 2023 arXiv Stay on topic with Classifier-Free Guidance 30 June 2023 arXiv 30 June 2023 arXiv 7 June 2023 arXiv A Technical Report for Polyglot-Ko: Open-Source Large-Scale Korean Language Models 7 June 2023 arXiv 7 June 2023 arXiv 3 June 2023 ACL GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration 3 June 2023 ACL 3 June 2023 ACL 25 May 2023 arXiv Role-Play with Large Language Models 25 May 2023 arXiv 25 May 2023 arXiv 4 May 2023 arXiv StarCoder: May the Source be With You! 4 May 2023 arXiv 4 May 2023 arXiv 25 April 2023 ICML Recasting Self-Attention with Holographic Reduced Representations 25 April 2023 ICML 25 April 2023 ICML 5 April 2023 ICML Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling 5 April 2023 ICML 5 April 2023 ICML 2 March 2023 arXiv Eliciting Latent Predictions from Transformers with the Tuned Lens 2 March 2023 arXiv 2 March 2023 arXiv 24 February 2023 arXiv ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics 24 February 2023 arXiv Azerbayev, Piotrowski, Schoelkopf, Ayers, Radev, and Avigad. "ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics." arXiv preprint arXiv:2302.12433 (2023). 24 February 2023 arXiv 9 January 2023 Deep Learning 4 Code Workshop SantaCoder: don't reach for the stars! 9 January 2023 Deep Learning 4 Code Workshop Allal, Li, Kocetkov, et al. "SantaCoder: don't reach for the stars!." arXiv preprint arXiv:2301.03988 (2023). 9 January 2023 Deep Learning 4 Code Workshop 19 December 2022 arXiv BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting 19 December 2022 arXiv Yong, Schoelkopf, Muennighoff, et al. "BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting." arXiv preprint arXiv:2212.09535 (2022). 19 December 2022 arXiv 23 November 2022 ICML HyperTuning: Toward Adapting Large Language Models without Back-propagation 23 November 2022 ICML Jason Phang, Yi Mao, Pengcheng He, Weizhu Chen. "HyperTuning: Toward Adapting Large Language Models without Back-propagation." arXiv preprint arXiv:2211.12485, 2022 23 November 2022 ICML 10 November 2022 arXiv BLOOM: A 176B-Parameter Open-Access Multilingual Language Model 10 November 2022 arXiv Le Scao, et al. (incl. Tow, Biderman, Ammanamanchi, Gao, Sutawika, Teehan). "BLOOM: A 176B-Parameter Open-Access Multilingual Language Model." arXiv preprint arXiv: 2211.05100, 2022. 10 November 2022 arXiv
11328 chars
SUB-PAGE (https://eleuther.ai/interpretability/) Interpretability — EleutherAI
[H2] Interpretability Peeking inside the black box of machine learning algorithms to build robust understandings of what they do and why. [H3] Current Projects [IMG: Interpreting Across Time] Interpreting Across Time [IMG: Eliciting Latent Knowledge] Eliciting Latent Knowledge [H3] Releases Model Pythia Model A suite of models designed to enable controlled scientific research on transparently trained LLMs Model Library tuned-lens Library A library implementing the Tuned Lens, along with other tools for extracting, manipulating, and studying the learned representations of transformers across layers. Library [H3] Publications 6 February 2024 arXiv Neural networks learn moments of increasing order 6 February 2024 arXiv 6 February 2024 arXiv 17 December 2023 NeurIPS Workshop (Attributing Model Behavior at Scale) Sparse Autoencoders Find Highly Interpretable Features in Language Models 17 December 2023 NeurIPS Workshop (Attributing Model Behavior at Scale) 17 December 2023 NeurIPS Workshop (Attributing Model Behavior at Scale) 16 December 2023 NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) Eliciting Language Model Behaviors using Reverse Language Models 16 December 2023 NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 16 December 2023 NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR)
1800 chars
SUB-PAGE (https://eleuther.ai/alignment/) Alignment — EleutherAI
[H2] Alignment Ensuring that an artificial intelligence system behaves in a manner that is consistent with human values and goals. [H3] Current Projects [IMG: Eliciting Latent Knowledge] Eliciting Latent Knowledge [IMG: Alignment MineTest] Alignment MineTest [IMG: Mesaoptimization] Mesaoptimization [H3] Releases Library trlX Library A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF) Library Library tuned-lens Library A library implementing the Tuned Lens, along with other tools for extracting, manipulating, and studying the learned representations of transformers across layers. Library Dataset Simulacra Aesthetic Captions Dataset A dataset of prompts, synthetic AI generated images, and aesthetic ratings of those images. Dataset [H3] Papers 12 February 2024 arXiv Suppressing Pink Elephants with Direct Principle Feedback 12 February 2024 arXiv 12 February 2024 arXiv 16 December 2023 ICLR Quality-Diversity through AI Feedback 16 December 2023 ICLR 16 December 2023 ICLR 14 December 2023 NeurIPS The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs 14 December 2023 NeurIPS Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, and Edward Grefenstette. "Large language models are not zero-shot communicators." arXiv preprint arXiv:2210.14986, 2022. 14 December 2023 NeurIPS 9 December 2023 ICML Workshop Do LLMs selectively encode the goal of an agent's reach? 9 December 2023 ICML Workshop 9 December 2023 ICML Workshop 8 December 2023 EMNLP trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback 8 December 2023 EMNLP Reinforcement learning from human feedback (RLHF) utilizes human feedback to better align large language models with human preferences via online optimization against a learned reward model. Current RLHF paradigms rely on Proximal Policy Optimization (PPO), which quickly becomes a challenge to implement and scale up to large architectures. To address this difficulty we present the trlX library as a feature-complete open-source framework for RLHF fine-tuning of models up to and exceeding 70 billion parameters. We implement support for multiple types of distributed training including distributed data parallel, model sharded, as well as tensor, sequential, and pipeline parallelism.To increase the accessibility of RLHF to researchers, we implement compute- and memory-saving features that give trlX the flexibility to support users with a wide range of compute resources. This includes offline RL methods like Implicit Language Q Learning (ILQL), low-rank adapters, and the Hydra architecture. We find offline fine-tuning offers competitive performance relative to online algorithms while being easier to implement, train, and scale. To evaluate our framework we train RLHF models on two separate well-known tasks using publicly available human preference data. Models trained with trlX achieve preference win-rates over baselines at rates comparable to the original works. 8 December 2023 EMNLP 2 October 2023 Representation Engineering: A Top-Down Approach to AI Transparency 2 October 2023 2 October 2023 25 May 2023 arXiv Role-Play with Large Language Models 25 May 2023 arXiv 25 May 2023 arXiv 9 February 2023 Alignment Forum Anomalous tokens reveal the original identities of Instruct models 9 February 2023 Alignment Forum I was able to use the weird centroid-proximate tokens that Jessica Mary and Matthew Watkins discovered to associate several of the Instruct models on the OpenAI API with the base models they were initialized from. Prompting GPT-3 models with these tokens causes aberrant and correlated behaviors, and I found that the correlation is preserved between base models and Instruct versions, thereby exposing a "fingerprint" inherited from pretraining.I was inspired to try this by JDP's proposal to fingerprint generalization strategies using correlations in model outputs on out-of-distribution inputs. This post describes his idea and the outcome of my experiment, which I think is positive evidence that this "black box cryptanalysis"-inspired approach to fingerprinting models is promising. 9 February 2023 Alignment Forum 15 October 2022 arXiv Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning 15 October 2022 arXiv 15 October 2022 arXiv 24 October 2021 Alignment Forum Towards Deconfusing Gradient Hacking 24 October 2021 Alignment Forum 24 October 2021 Alignment Forum 5 September 2021 Alignment Forum Obstacles to Gradient Hacking 5 September 2021 Alignment Forum 5 September 2021 Alignment Forum 2 September 2021 arXiv An Empirical Exploration in Quality Filtering of Text Data 2 September 2021 arXiv Leo Gao. “An Empirical Exploration in Quality Filtering of Text Data.” arXiv preprint arXiv:2109.00698, 2021. 2 September 2021 arXiv 2 April 2021 The State of AI Ethics Report The Hard Problem of Aligning AI to Human Values 2 April 2021 The State of AI Ethics Report Connor Leahy and Stella Biderman. "The Hard Problem of Aligning AI to Human Values." The State of AI Ethics Report 4, p. 180-183. 2021. 2 April 2021 The State of AI Ethics Report
6094 chars
Explore the other reputation pillars for EleutherAI
Analysis Disclosure & Source Attribution
This page presents a snapshot of public data from EleutherAI, captured on May 29, 2026, to show how machine logic reads Semantic Coherence signals into an AI reputation evaluation.
Purpose: This data is presented under “Fair Use” for the purpose of independent signal analysis, allowing readers to see the raw signals behind the reputation score.
Notice to EleutherAI: This analysis is part of a non-adversarial audit conducted by 1 Euro SEO. The results are intended as professional feedback to help improve any website’s machine-readability and authority signals. The evaluation is free, and any company can request a fresh audit at any time.
Any company can use the insights for free and improve its voice. When a company has updated its content, it can always submit a new audit request, which will be reflected in a new current score.
To all users: You are encouraged to visit the live site at https://eleuther.ai to view the most current version of its content and see directly what this company is about and what it offers.