The paper investigates how different prompt adaptation techniques—discrete prompt optimization, prompt tuning, and prefix tuning—affect the internal processing of dense and mixture-of-experts language models. It finds that the adaptation location determines how task information reaches the output: text prompts mainly influence response construction, input-level prompts modify content representations, and layer-wise prefixes exert direct control via attention. In MoE models, these differences also lead to distinct expert‑routing patterns, while generic representation‑level metrics fail to reliably distinguish effective adaptations from placebo effects.
By Mikhail Aleksandrov, Nail Bashirov, Maksim Ivanov, Andrew Tsukanov, Timofey Karyagin, Alexander Miasnikov, Gleb Molodtsov, Valentin Samokhin, Aleksandr Beznosikov
The study investigates how language models acquire in-context learning (ICL) by distinguishing between copying a nearby demonstration and integrating information across a prompt. Across 17 models, roughly one-third default to copied answers, a few are undecided, and the remainder favor rule-consistent answers that yield higher accuracy and are guided by the requested value rather than the nearest conflicting example. Analyses show that models prioritize task-relevant prompt tokens before accuracy surpasses chance, and that synthetic or fine-tuned training can shift models toward compositional responses or improve accuracy when prompts reassign label meanings.
By Minglai Yang, Xinyu Guo, Renxiong Wang, Sunjie Hou, Daniel Yue Zhang, Yunzhong He
The paper proposes a two‑stage framework for expanding and refining a set of prompts for language models under a limited evaluation budget. In the first stage, it generates a finite candidate set of prompts represented by moderate‑dimensional vectors. The second stage builds a surrogate model of prompt scores and sequentially selects prompts for evaluation, proving consistency of this procedure and introducing a refinement step that searches beyond the initial candidates. Numerical experiments show the method outperforms baseline prompt selection, especially when evaluation calls are constrained.
By Haoting Zhang, Jinghai He, Rhonda Righter, Zeyu Zheng
arXiv:2510. 01163v2 Announce Type: replace Abstract: The factors driving the performance of in-context learning (ICL) in large language models (LLMs) remain poorly understood despite ICL's surprising effectiveness, enabling models to adapt to new tasks from only a handful of examples.
By Wa\"iss Azizian, Ali Hasan
arXiv:2606. 05486v1 Announce Type: cross Abstract: Prompt ambiguity is a common source of failure in large language models, but is difficult to localize because it is a latent property of the prompt, while existing attribution methods are designed to explain observable outputs such as logits or generated tokens.
By Govind Ramesh, Yao Dou, Wei Xu
arXiv:2609.22607v1 Announce Type: new
Abstract: We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas,...
By Minwoo Kang, T\'ea Wright, Seun Eisape, Ayush Raj, Suhong Moon, Joseph Suh, Alane Suhr, David M. Chan, John Canny
arXiv:2607. 08399v1 Announce Type: cross Abstract: Large language models process prompts by propagating activations through dozens of layers before generating a response.
By Thibaud Ardoin, Semira Einsele, Evis Bregu, Gerhard Wunder
The study investigates how post‑training of large autoregressive language models (ARMs) into masked diffusion models (MDMs) affects their internal computation. Across two 7B ARM‑MDM families and four diagnostic tasks, the authors find that MDMs retain much of the ARM’s high‑attribution pathways on prefix‑dominant tasks, but reorganize computation toward earlier layers on globally constrained tasks. Component‑level probes reveal that ARMs depend on sharply specialized components, whereas MDMs show weaker specialization and more diffuse output‑space alignment.
By Injin Kong, Hyoungjoon Lee, Yohan Jo
arXiv:2608. 16627v1 Announce Type: cross Abstract: Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL).
By Mahdi Dhaini, Adam Dejl, Juraj Vladika, Volkan \"Ozer, Barbara Plank, Gjergji Kasneci
arXiv:2606. 00544v1 Announce Type: new Abstract: Modern language-model fine-tuning typically pairs each prompt with a single response, even though many prompts admit multiple valid completions.
By Hasan Amin, Kian Ahrabian, Ming Yin, Rajiv Khanna
The paper investigates how combining soft prompts via task arithmetic can reduce reliance on confounding variables in classification models. It introduces Hybrid Prompt Arithmetic (HyPA), which merges task prompts with linearized confounder prompts to counteract spurious correlations. Experiments across multiple benchmarks show that HyPA consistently improves the robustness‑performance trade‑off under distribution shift, and analysis of hidden representations suggests it mitigates confounding by diminishing the influence of confounder signals.
By Zhecheng Sheng, Yongsen Tan, Xiruo Ding, Trevor Cohen, Serguei Pakhomov
arXiv:2608.28382v1 Announce Type: new
Abstract: Users often ask large language models (LLMs) to report how confident they are, but it is unclear whether such linguistic confidence tracks the model's...
By Hefan Zhang, Bingquan Zhang, Ming Cheng, Saeed Hassanpour, Weicheng Ma, Soroush Vosoughi