nsschw/echo-Llama-3.1-8B-Instruct-ger

echo-Llama-3.1-8B-Instruct-de

This model performs Conditioned Comment Prediction (CCP), designed to act as a "silicon subject" for computational social science. It predicts how a specific social media user will respond to a given stimulus by utilizing both an explicit user profile and implicit behavioral history.

Model Details

Model Description

The model was fine-tuned using Supervised Fine-Tuning (SFT) to optimize its ability to generate high-fidelity, user-specific replies to online content. It isolates the capability of response generation, prioritizing operational validity over surface-level plausibility by benchmarking against authentic digital traces.

• Developed by: Nils Schwager, Simon Münker, Alistair Plum, Achim Rettinger (Trier University, University of Luxembourg)
• Funded by: EU's Horizon Europe Framework (HORIZON-CL2-2022-DEMOCRACY-01-07) under grant agreement number 101095095
• Model type: Autoregressive Large Language Model (Instruction-tuned)
• Language(s) (NLP): German (de)
• License: llama3.1
• Finetuned from model: meta-llama/Llama-3.1-8B-Instruct

Model Sources

• Repository: https://github.com/nsschw/Conditioned-Comment-Prediction
• Paper: Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction

Uses

Direct Use

The model is built for computational social scientists and researchers modeling discourse dynamics and individual-level behavioral patterns. It is strictly optimized for predicting first-order text replies to textual stimuli (such as social media posts or news articles) given a specific user context.

Out-of-Scope Use

The model cannot process multi-modal inputs (e.g., URLs, images, GIFs) or simulate non-verbal interactions (e.g., liking behavior). It is not intended for generating coordinated inauthentic behavior, micro-targeted disinformation, or impersonating individuals for deceptive purposes.

Bias, Risks, and Limitations

• Privacy: The training utilized authentic, public digital traces from real X users. These individuals did not provide explicit informed consent for their communication patterns to be replicated by generative models.
• Dual-Use Risks: The framework demonstrated here enables the generation of synthetic content that mimics individual communication patterns with measurable fidelity. Malicious actors could exploit this for sophisticated bot campaigns or targeted persuasion.
• Semantic Boundaries: SFT in this linguistic environment primarily serves as a tool for formatting control rather than semantic enhancement. While SFT refines style and surface-level structure, the model's semantic grounding struggles to deepen beyond the base model's capabilities.

How to Get Started with the Model

The model requires a combined explicit and implicit conditioning format. Structure your prompt as a native chat sequence containing the generated user profile (system prompt) followed by up to 29 historical interactions.

System: [Insert Generated Biography]
User: Comment on the following content: [Historical Stimulus 1]
Assistant: [Authentic Historical Reply 1]
...
User: Comment on the following content: [New Target Stimulus]
Assistant:

Training Details

Training Data

The model was fine-tuned on a corpus of German X data collected around keywords related to German political discourse during the first half of 2023.

• Sample Size: 3,800 users sampled from a raw corpus of 3.38M tweets.
• Context Limit: Up to 30 stimulus-response interactions per user.
• Exclusions: Interactions containing URLs, images, or GIFs, and users with fewer than four historical replies.

Training Procedure

Training Hyperparameters

• Training regime: 8-bit quantization, Paged AdamW optimizer.
• Epochs: 1
• Max sequence length: 4,500 tokens
• Training paradigm: SFT on complete input sequences (system prompt, user prompts, and model completions) using TRL defaults.

Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluations were conducted on a deterministic, held-out test split of 650 German users to prevent cross-user data leakage.

Metrics

The evaluation utilized automated metrics across five independent generation runs (decoding temperature 0.75, max new tokens 500) to capture both lexical overlap and semantic alignment.

• BLEU & ROUGE-1: Measures precision-oriented n-gram overlap and unigram overlap.
• Length Ratio: Quantifies output volume alignment against the authentic reference length.
• Embedding Distance: Assesses semantic intent alignment via cosine distance using Qwen3-Embedding-8B.

Results

• BLEU: 0.095
• ROUGE-1: 0.192
• Length Ratio: 0.915
• Embedding Distance: 0.504

Observation: A critical limitation emerges in the German language scenario: while SFT successfully improves lexical metrics (BLEU 0.065 to 0.095), the semantic alignment remains stagnant (embedding distance ~0.50). This indicates that SFT refines style but struggles to deepen semantic grounding beyond the base model's inherent capabilities.

Technical Specifications

Compute Infrastructure

Hardware

The model was fine-tuned on a single NVIDIA L40S GPU (48GB VRAM).

Citation

BibTeX: @article{schwager2026towards, title={Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction}, author={Schwager, Nils and M{"u}nker, Simon and Plum, Alistair and Rettinger, Achim}, journal={arXiv preprint arXiv:2602.22752}, year={2026} }