---
title: "Addressing the Ecological Fallacy in Larger LMs — Soni 2026"
description: "Standard LM training treats each text as an independent sample, even when many texts come from the same writer."
authors: ["Nikita Soni", "Dhruv Vijay Kunjadiya", "Pratham Piyush Shah", "Dikshya Mohanty", "H. Andrew Schwartz", "Niranjan Balasubramanian"]
year: 2026
venue: "Proceedings of the 30th Conference on Computational Natural Language Learning"
source: "https://aclanthology.org/2026.conll-main.9/"
doi: "10.18653/v1/2026.conll-main.9"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/soni-2026-addressing-ecological-fallacy-larger-lms/"
---
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# Addressing the Ecological Fallacy in Larger LMs with Human Context

**Authors:** Nikita Soni, Dhruv Vijay Kunjadiya, Pratham Piyush Shah, [Dikshya Mohanty](https://md.research.elamur.ai/free/authors/dikshya-mohanty/), H. Andrew Schwartz, [Niranjan Balasubramanian](https://md.research.elamur.ai/free/authors/niranjan-balasubramanian/) · **Year:** 2026 · **Venue:** Proceedings of the 30th Conference on Computational Natural Language Learning · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.conll-main.9/) · [PDF](https://aclanthology.org/2026.conll-main.9.pdf) · [DOI 10.18653/v1/2026.conll-main.9](https://doi.org/10.18653/v1/2026.conll-main.9) · [OpenAlex](https://openalex.org/W7166839380)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · 1 in our library, of the 140673 papers whose bibliographies we read

## Summary

Standard LM training treats each text as an independent sample, even when many texts come from the same writer. The authors call this an ecological fallacy. Earlier work addressed it for ~124M GPT-2-style models with Human Language Modeling (HuLM), which conditions next-token prediction on the author's earlier, time-ordered texts. This paper asks whether that gain still holds for Llama 3.1 8B.

Three strategies for adding author history are compared:
- a frozen model with only a linear classifier trained on top;
- HuFT, QLoRA fine-tuning with history concatenated to the input;
- continued QLoRA pre-training under the HuLM objective, which produces HU-Llama.

For pre-training, the authors build the Large Human Language Corpus (LHLC) from six sources. They also curate six task datasets with anonymized author IDs and evaluate on eight tasks in total.

HuFT beats ordinary fine-tuning. HU-Llama generalizes across tasks with just a linear head. Adding history to a frozen, non-human-aware model brings no benefit. The results suggest that even billion-parameter models gain from knowing who wrote a text, which is also relevant for small, locally hosted models handling sensitive data.

## Key points

- **Base model and adaptation:** Llama 3.1 8B adapted with QLoRA, which trains only about 0.17% of the parameters. The authors list this as a limitation.
- **How author context is built:** each author's documents are concatenated in temporal order, separated by the eos token.
  - Document-level tasks use the target document's last-token hidden state.
  - Person-level tasks average the token embeddings across all of an author's texts.
- **LHLC pre-training subset:** Reddit, Blog Authorship Corpus, Twitter, Gutenberg Books, Amazon Product Reviews, and StackExchange. It totals about 147,403 author instances, 6.839M documents, 860M tokens (LLaMA 3.1 tokenizer), and 3.52 GB.
- **Eight downstream tasks:**
  - person-level: Occupation and Age (blogs);
  - document-level: Movie, Electronics, and Book Reviews (amazon), Business Reviews (yelp), and Sentiment and Stance (twitter).
- **Availability:** data, models, and code are released on GitHub (sonin/Larger_LMs_with_Human_Context). User identifiers were removed and the data manually checked for PII.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{soni2026addressing,
  title = {{Addressing the Ecological Fallacy in Larger LMs with Human Context}},
  author = {Soni, Nikita and Kunjadiya, Dhruv Vijay and Shah, Pratham Piyush and Mohanty, Dikshya and Schwartz, H. Andrew and Balasubramanian, Niranjan},
  booktitle = {Proceedings of the 30th Conference on Computational Natural Language Learning},
  year = {2026},
  doi = {10.18653/v1/2026.conll-main.9},
  url = {https://doi.org/10.18653/v1/2026.conll-main.9}
}
```

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