@inproceedings{kou-etal-2026-pela,
title = "{P}e{LA}: Prompt-Enhanced Logit Adaptation for Personality-Trait Steering in {LLM}s",
author = "Kou, Haoxuan and
Wang, Wenxuan and
Liu, Zirui and
Shen, Jiaxing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.795/",
pages = "16839--16861",
ISBN = "979-8-89176-462-0",
abstract = "Large language models (LLMs) are increasingly used in long-running user-facing applications where responses may need to maintain a consistent interaction style. Prompting is easy to reuse but often weak, while fine-tuning and representation interventions can be stronger but are typically model-specific. We present \textbf{PeLA}, Prompt-Enhanced Logit Adaptation, a parameter-efficient framework for Big Five personality-trait steering. PeLA keeps the base LLM frozen, uses a personality prompt to specify the desired trait direction, and applies a learned logit adapter during decoding. The adapter contributes a base-relative residual controlled by a token-position gate, refining the prompt-conditioned distribution only when intervention is useful. Because the control module operates in output-token space, it can be reused across models with identical tokenizer vocabularies and token-id order. On open-ended PersonalityBench questions, PeLA improves macro-average target-direction scores over a strong Personality Prompt baseline on Qwen3-8B and Qwen2.5-7B, while cross-size experiments demonstrate tokenizer-compatible transfer within both the Qwen3 and Llama-3.2 families after validation-based scale calibration. PeLA narrows the gap to model-specific steering while preserving a frozen base model."
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<abstract>Large language models (LLMs) are increasingly used in long-running user-facing applications where responses may need to maintain a consistent interaction style. Prompting is easy to reuse but often weak, while fine-tuning and representation interventions can be stronger but are typically model-specific. We present PeLA, Prompt-Enhanced Logit Adaptation, a parameter-efficient framework for Big Five personality-trait steering. PeLA keeps the base LLM frozen, uses a personality prompt to specify the desired trait direction, and applies a learned logit adapter during decoding. The adapter contributes a base-relative residual controlled by a token-position gate, refining the prompt-conditioned distribution only when intervention is useful. Because the control module operates in output-token space, it can be reused across models with identical tokenizer vocabularies and token-id order. On open-ended PersonalityBench questions, PeLA improves macro-average target-direction scores over a strong Personality Prompt baseline on Qwen3-8B and Qwen2.5-7B, while cross-size experiments demonstrate tokenizer-compatible transfer within both the Qwen3 and Llama-3.2 families after validation-based scale calibration. PeLA narrows the gap to model-specific steering while preserving a frozen base model.</abstract>
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%0 Conference Proceedings
%T PeLA: Prompt-Enhanced Logit Adaptation for Personality-Trait Steering in LLMs
%A Kou, Haoxuan
%A Wang, Wenxuan
%A Liu, Zirui
%A Shen, Jiaxing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F kou-etal-2026-pela
%X Large language models (LLMs) are increasingly used in long-running user-facing applications where responses may need to maintain a consistent interaction style. Prompting is easy to reuse but often weak, while fine-tuning and representation interventions can be stronger but are typically model-specific. We present PeLA, Prompt-Enhanced Logit Adaptation, a parameter-efficient framework for Big Five personality-trait steering. PeLA keeps the base LLM frozen, uses a personality prompt to specify the desired trait direction, and applies a learned logit adapter during decoding. The adapter contributes a base-relative residual controlled by a token-position gate, refining the prompt-conditioned distribution only when intervention is useful. Because the control module operates in output-token space, it can be reused across models with identical tokenizer vocabularies and token-id order. On open-ended PersonalityBench questions, PeLA improves macro-average target-direction scores over a strong Personality Prompt baseline on Qwen3-8B and Qwen2.5-7B, while cross-size experiments demonstrate tokenizer-compatible transfer within both the Qwen3 and Llama-3.2 families after validation-based scale calibration. PeLA narrows the gap to model-specific steering while preserving a frozen base model.
%U https://aclanthology.org/2026.findings-emnlp.795/
%P 16839-16861
Markdown (Informal)
[PeLA: Prompt-Enhanced Logit Adaptation for Personality-Trait Steering in LLMs](https://aclanthology.org/2026.findings-emnlp.795/) (Kou et al., Findings 2026)
ACL