Cautious Context Steering for Language Model Personalization

Euntai Kim; Gihoon Kim; Hyounsoo Han; Jeyoung Lee; Minsu Jeon; Sekwon Oh; Suhan Woo

REVIEW 2 major objections 5 minor 12 references

Cautious Context Steering for Language Model Personalization

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A single lightweight adapter can personalize a frozen language model by deciding at each token whether and how strongly user context should steer generation.

desk verdict Clean, well-motivated personalization method, but the paper's core 'cautious gate' claim is empirically unsupported until the gate is actually ablated. read the letter →

arxiv 2608.05813 v1 pith:5D2DE5A3 submitted 2026-08-06 cs.AI

Gihoon Kim , Jeyoung Lee , Suhan Woo , Sekwon Oh , Minsu Jeon , Hyounsoo Han , Euntai Kim This is my paper · ORCID

classification cs.AI

keywords CautiousContextSteeringlanguagemodelpersonalizationinference-timeadaptationtoken-levelgatingknowledgedistillationpreferencealignmentout-of-distributiongeneralization

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that language-model personalization works better when the influence of user preference history is gated per token rather than applied uniformly. The proposed method, Cautious Context Steering (CCS), trains a small adapter on top of a frozen backbone to predict a token-wise steering bias and a scalar gate that suppresses steering when the context is not helpful. The authors show that one CCS adapter, trained only on the PRISM dataset, improves generation quality on PRISM and on four out-of-distribution personalization benchmarks, and that it avoids the extra forward pass required by Context Steering, roughly halving inference time. If true, this would make personalized generation cheaper and more generalizable than per-user fine-tuning or fixed-strength steering.

What carries the argument

The load-bearing object is the CCS adapter: a small network, under 1% of the backbone's parameters, that takes the base LM's hidden state, a prompt representation, and an encoded preference history, and outputs a signed steering score over a plausible token set P_t plus a gate g_t in [0,1]. The plausible set's size adapts to the base distribution's confidence, so when the base is uncertain the adapter can reorder more tokens, and when it is confident the set is small. The gate is trained against a soft binary target derived from the steering advantage, which is what makes the intervention cautious: it lets the frozen backbone keep its own output when context adds no evidence, and only shifts the logits inside P_t when the teacher would assign the preferred token higher probability.

What would settle it

Measure generation quality with the gate permanently set to zero (base-only) on the same benchmarks; if it matches or beats CCS, the gate contributes nothing. Alternatively, replace the user's history with a random user's or a shuffled history and check whether CCS still improves over Base; if it does, the gate is not actually detecting useful context. A clean quantitative test is to compare CCS against an oracle-gated variant that uses the true advantage at decoding; the size of the gap shows how much the gate's inference-time prediction costs.

Watch

Extended reading notes

Core claim

The central claim is that a token-level, strength-adaptive gate can distill the useful part of a context-conditioned teacher's logit difference and ignore the rest. CCS defines a steering advantage A_t = log p_ctx(y+_t) - log p_base(y+_t) for each token of the user's preferred response, marks tokens with advantage above a margin as helpful, and trains the adapter to match an oracle-steered distribution on those tokens while preserving the base distribution on all others. A separate gate head learns to predict whether steering is useful, so at inference the final bias is the normalized steering score scaled by a gate in [0,1]. The authors report that this single adapter beats ICL and CoS on nearly every model-metric combination across PRISM and four OOD benchmarks, and reduces inference latency by 44.8% and 51.0% relative to CoS on Qwen3-0.6B and Qwen3-4B respectively.

Load-bearing premise

The load-bearing premise is that the gate, trained with access to the user's preferred next token, can still predict from the current hidden state and base-distribution statistics whether user context is helpful at inference time, when that preferred token is unknown.

Editorial extensions

If this is right

A single adapter trained on PRISM transfers to four unseen benchmarks (UF-P-4, Psoups, PersonalLLM, Reddit TLDR) without per-user or per-dataset fine-tuning.
CCS adds negligible latency over ICL and is 44.8% (0.6B) and 51.0% (4B) faster than CoS, because the second context-conditioned forward pass is removed at inference.
Fixed-strength steering can amplify unhelpful context under domain shift, as seen when CoS's ROUGE-1 on Reddit TLDR drops from 14.75 to 7.60 with Qwen3-0.6B, while CCS raises it to 20.96.
The gate's learned behavior means the model can personalize without storing per-user adapters, so new users can be served with only an encoded history representation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The gate can be read as learning a token-level relevance classifier; the same gating mechanism might be reused for other conditioning signals such as task instructions, style control, or safety constraints beyond user preference.
Because the gate must predict helpfulness from observable base-distribution statistics after training, an interesting test is whether the gate's outputs correlate with token-level uncertainty or entropy of the base distribution.
If the gate were removed and the steering strength set to a fixed value, the method would reduce to a CoS-like scheme; comparing these two variants would isolate exactly how much the adaptivity of the gate contributes to the reported gains.
A natural extension would be to train the adapter on multiple personalization datasets and measure whether the per-token gating rule accumulates transferable signal or starts to overfit to the first dataset's preference structure.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my take on CCS. The method is clean and the empirical story is mostly consistent: a single lightweight adapter trained on PRISM improves ROUGE/BERTScore in-domain and on four OOD benchmarks, and the latency numbers (roughly half of CoS) are plausible. The core device — token-wise gated steering distilled from an oracle teacher, restricted to a plausible-token set — is genuinely not in the cited ICL or CoS baselines. The motivation section showing that the optimal CoS strength varies per token and per model is well executed and justifies the design.

The soft spots are real but not fatal. The biggest one is exactly what the stress-test note flags: the gate target (Eq. 12) is a softened version of the oracle advantage, which needs the user's chosen token y+ during training, and there is no ablation showing the learned gate actually modulates steering at inference. No histogram of gate values, no comparison of learned-gate vs. gate-fixed-to-1. So the "cautious" component — the main novelty over CoS — is empirically unsupported as of now. The gains in Table 1 could come almost entirely from the bias head with a near-constant gate. That said, the bias head itself does something new relative to ICL/CoS, so the paper isn't hollow; it just overclaims the "cautious" part.

Other issues: no error bars or significance tests; some improvements are small (PRISM BERTScore 83.14 vs. 82.75). CoS is fixed at lambda = -0.5, which the paper itself argues is suboptimal — that's fair in the sense that CoS's fixed strength is the point of comparison, but it also means the comparison is against a handicapped baseline. The paper criticizes per-user adapters and reward-model families but doesn't include them as baselines; that's a citation-level gap. No code, data, or hyperparameters are released, so the numbers can't be checked.

On circularity: none. The teacher and oracle strengths are computed from model logits and preferred tokens during training; the adapter must generalize at inference without y+, so it's not fitting to its input. The self-citation to Kim & Kim (ICLR 2026) is prior author work, not a circular dependency.

I'd send this to a serious referee. The central idea is worth engaging with, and the gate question is empirically answerable. For the revision, I'd want the gate ablation, variance estimates, and at least one of the per-user baselines.

Referee Report

2 major / 5 minor

Summary. The paper proposes Cautious Context Steering (CCS), a lightweight adapter on a frozen language model that decides at each decoding step whether and how strongly user context should shift the next-token distribution. A teacher context-conditioned LM provides training targets: helpful tokens are identified via a token-level advantage A_t, an oracle steering strength lambda* is selected per token, and a gate head is trained to predict helpfulness with a soft target based on A_t. At inference the teacher pass is removed, and the adapter predicts a gated bias from the current state and base-distribution statistics. The authors train a single CCS adapter on PRISM and evaluate it in-domain and on four out-of-distribution benchmarks, reporting consistent generation improvements over Base, ICL, and CoS, as well as lower latency than CoS.

Significance. If the adaptive-gate mechanism works as claimed, the paper offers a practical contribution: a single lightweight adapter that generalizes across users and domains, avoids per-user fine-tuning, and removes CoS's extra context-conditioned forward pass. The cross-dataset evaluation (PRISM plus four OOD benchmarks) and the latency comparison are valuable, and the idea of distilling token-level oracle steering into a gated adapter is clear and well motivated. However, the central novelty, the learned cautious gate, is not directly validated by any experiment, and several reported performance differences are small enough that the robustness of the empirical claims is not yet established.

major comments (2)

[Section 3.3, Eq. (12), and Section 3.4] The gate training target \tilde{g}_t and the helpful/preservation partition both depend on A_t, which is defined in Eq. (6) using the unobserved preferred token y^+_t. At inference the gate must predict the same notion of helpfulness from z_t and base-distribution statistics, but the paper presents no evidence that this transfer occurs. There is no gate-only ablation (learned gate vs. gate fixed to 1 or 0), no histogram or correlation of predicted g_t with A_t, and no analysis of gate statistics on out-of-distribution data. If the learned gate is nearly constant, the claimed 'cautious' behavior is not demonstrated, and the Table 1 gains could be attributed to the learned per-token bias alone. Please add a gate ablation, report gate statistics (mean, variance, fraction near 0/1) on training and OOD sets, and, where possible, measure the relationship between predicted gate values and teacher advantages.
[Table 1] Several of the reported gains are very small and no uncertainty estimates or significance tests are provided. For example, PRISM Qwen3-4B BERTScore-F1 is 85.16 for CCS versus 85.06 for CoS; UF-P-4 Qwen3-0.6B ROUGE-1 is 29.12 versus 29.19 for CoS; Psoups Qwen3-0.6B BERTScore-F1 is 81.65 versus 81.78 for ICL; and Reddit TLDR Qwen3-4B ROUGE-1 is 21.93 versus 22.64 for ICL. Without standard errors, confidence intervals, multiple seeds, or paired significance tests, the claim that CCS 'consistently improves' generation quality across all settings is not fully supported. Please report variability and run appropriate significance tests, especially for the smaller differences.

minor comments (5)

[Section 2, Fig. 2] The qualitative claim that the maximizing steering strength differs across models and metrics is based on a grid evaluation without visible error bars or standard deviations; please state how many examples were used and whether the differences are stable across seeds.
[Section 3.1] The construction of the plausible token set P_t and its 'uncertainty-adaptive' sizing rule are described only verbally; the exact algorithm (for example, a cumulative-probability threshold or top-k rule) is missing, which prevents reproduction and makes the comparison with CoS harder to interpret.
[Section 4.1] The context budget K=4 is fixed for all context-based methods, but the exact prompt format used to encode the preference history for ICL, CoS, and CCS is not specified; please include the formatting template used for each method.
[Section 4.3, Table 2] The latency numbers are reported without experimental conditions such as hardware, batch size, sequence length, or number of generated tokens, and without variance estimates; please add these details so that the 44.8% and 51.0% speedup claims can be evaluated.
[Throughout] There are several text artifacts and typos, including 'aㅜoracle' in Section 1 and 'This Eq. 10 and Eq. 11 term is central' in Section 3.3; the reference list also contains LaTeX-encoding artifacts (for example in the Casper et al. entry), which should be cleaned before publication.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the CCS derivation is self-contained; the only author-overlapping citation is one non-load-bearing related-work reference.

full rationale

The derivation chain is not equivalent to its inputs. In Section 3.2, the steering advantage A_t (Eq. 6) and the oracle strength lambda*_t (Eq. 7) are computed from teacher/base log-probs and the preferred token y+_t, but those quantities are used only to build training labels: the helpful/preservation partition, the target distribution q*_t (Eq. 8), and the gate target (Eq. 12). At inference (Section 3.4), the adapter predicts the gated bias delta_t from the personalized state z_t and base-distribution statistics; y+ is not available, so the model must generalize rather than replay a fitted quantity. The paper itself states that "The teacher distribution and lambda* are used only during training. At inference time, the adapter predicts the steering bias and gate directly from the current state and user context." The method does not fit any free constant to the evaluation benchmarks: CoS's lambda is fixed from the original paper and CCS trains only on PRISM, with no OOD-tuning. The only author-overlapping reference is Kim & Kim (2026) in the related-work list of user-specific reward models; it is not load-bearing for the CCS mechanism. The skeptical concern that no gate-only ablation or gate statistics are reported is an empirical evaluation gap, not a circular step, because no equation in the paper reconstructs inference-time behavior from the y+-dependent training target by construction. Hence no circular step can be exhibited; the score reflects the one minor non-load-bearing self-citation.

Assumptions & free parameters 7 free parameters · 4 assumptions · 3 invented entities

The ledger shows the method's behavior depends on several unreported hyperparameters, plus the domain assumption that observable decoder states can predict per-token context helpfulness. No new physical entities are introduced; the adapter, gate, and plausible-token set are internal ML components with no independent external evidence.

free parameters (7)

margin m
Threshold in Eq. 6 that decides helpful versus preservation tokens; no value reported.
temperature tau
Softness of the gate target in Eq. 12; no value reported.
lambda bounds
Search range lambda_min to lambda_max for oracle steering strength in Eq. 7; no value reported.
loss weights eta_help, eta_preserve, eta_gate
Mixing coefficients in Eq. 13; no values reported.
context budget K = 4
Number of preference examples per user; fixed for all context-based methods.
plausible-set sizing rule
P_t size depends on base uncertainty; the exact function is not specified.
CoS baseline steering strength = -0.5
Fixed for the comparison; the paper's own Fig. 2 shows the optimum varies.

assumptions (4)

standard math Softmax and Kullback-Leibler divergence correctly compare token distributions.
Used in Eqs. 5, 10, and 11; standard.
domain assumption The logit difference between context-conditioned and base teacher is a reliable personalization signal.
Section 3.2 defines d_t from teacher and base logits; the method inherits CoS's assumption that this direction captures user preference.
ad hoc to paper Per-token context helpfulness can be predicted from the current hidden state and base distribution summary without the true next token.
The gate target in Eq. 12 uses A_t computed from y+_t; inference in Section 3.4 must estimate the same quantity without y+.
domain assumption User preference history can be compressed into a context representation used once.
Section 3.4 encodes H_u once; no analysis of information loss is given.

invented entities (3)

CCS adapter (score head and gate head)
purpose: Predicts token-wise steering scores and a gate in Eq. 3.
Internal trained component; evaluated only within this paper's benchmarks, no external falsifiable handle.
Personalized state z_t
purpose: Summarizes hidden state, prompt, and encoded history for the adapter.
Latent representation; no independent validation.
Plausible token set P_t
purpose: Restricts steering to high-probability tokens.
Design choice; the paper does not validate that leaving tokens outside P_t unsteered is sufficient.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Cautious Context Steering for Language Model Personalization." pith.science (2026). https://pith.science/paper/5D2DE5A3

@misc{pith2026260805813,
  author       = {Pith},
  title        = {Pith review of: Cautious Context Steering for Language Model Personalization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5D2DE5A3}},
  note         = {Machine review of arXiv:2608.05813}
}

read the original abstract

Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds. Existing methods typically train a separate adapter for each user or learn a reward model whose scores depend on the user. Despite explicitly optimizing for each user, these methods must learn from limited observations and therefore suffer from data sparsity and poor generalization to unseen users and domains. In-context learning (ICL) and Context Steering (CoS) can instead provide more effective personalization by conditioning the base LM directly on user context and leveraging its pretrained capabilities without per-user training. Yet neither adapts the influence of that context across decoding steps: ICL leaves it uncontrolled, whereas CoS applies a fixed steering coefficient and requires two LM forward passes per step. We propose Cautious Context Steering (CCS), which adds a lightweight adapter to a frozen backbone LM to decide at each token whether and how strongly user context should affect generation. The adapter learns this behavior from an oracle context-conditioned LM and preserves the base LM when the context is not helpful. A single CCS adapter trained on only one dataset improves generation quality both in-domain and across four out-of-distribution personalization benchmarks, demonstrating robust generalization to new users and domains. CCS also avoids per-user fine-tuning and the additional context-conditioned forward pass required by CoS, substantially reducing inference cost.

Figures

Figures reproduced from arXiv: 2608.05813 by the authors.

**Figure 1.** Overview of CCS. Conventional Context Steering (CoS) uses the difference between context-conditioned and base model logits with a fixed steering strength. This amplifies the influence of context at every token and requires two forward passes per decoding step. In contrast, our CCS adapter learns to predict token-wise logit biases and steering strengths based on user context. It is trained to steer only when the user… view at source ↗

**Figure 2.** Effect of steering strength. We vary the CoS steering strength λ and report ROUGE-1, ROUGE-L, and BERTScore-F1 for Qwen3-0.6B and Qwen3-4B on PRISM evaluation set (Kirk et al., 2024). The large markers indicate the best score for each model and metric. The maximizing value differs across model scales and metrics, showing that a single fixed steering strength is insufficient for reliable personalization. Generation … view at source ↗

**Figure 3.** Token-level oracle steering coefficients. For a single prompt and chosen response, we compute the oracle steering coefficient λ ⋆ t that maximizes the reference-token log-probability at each decoding step. Green tokens prefer positive λ values, meaning that stronger context influence improves the token probability; red tokens prefer negative λ values, meaning that reducing the context effect is better; unhighlighte… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

12 extracted references · 3 canonical work pages

[1]

Lore: Personalizing llms via low-rank reward modeling.arXiv preprint arXiv:2504.14439,

Avinandan Bose, Zhihan Xiong, Yuejie Chi, Simon Shaolei Du, Lin Xiao, and Maryam Fazel. Lore: Personalizing llms via low-rank reward modeling.arXiv preprint arXiv:2504.14439,

arXiv
[3]

Moral Machine or Tyranny of the Majority?

doi: 10.48550/arXiv.2305.17319. Jerry Zhi-Yang He, Sashrika Pandey, Mariah L Schrum, and Anca Dragan. Context steering: Con- trollable personalization at inference time.arXiv preprint arXiv:2405.01768,

work page Pith review arXiv doi:10.48550/arxiv.2305.17319
[5]

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al

doi: 10.48550/arXiv.2411.00524. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to fol- low instructions with human feedback.Advances in neural information processing systems, 35: 27730–27744,

arXiv doi:10.48550/arxiv.2411.00524
[6]

The reward model selection crisis in personalized alignment.arXiv preprint arXiv:2512.23067,

Fady Rezk, Yuangang Pan, Chuan-Sheng Foo, Xun Xu, Nancy Chen, Henry Gouk, and Timo- thy Hospedales. The reward model selection crisis in personalized alignment.arXiv preprint arXiv:2512.23067,

arXiv
[8]

Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin, and Meng Jiang

doi: 10.48550/arXiv.2402.05070. Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin, and Meng Jiang. Democratizing large language models via personalized parameter-efficient fine-tuning. InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 6476–6491,

arXiv doi:10.48550/arxiv.2402.05070 2024
[9]

Fast: Feature-aware sampling and tuning for personalized preference alignment with limited data

Thibaut Thonet, Germ ´an Kruszewski, Jos Rozen, Pierre Erbacher, and Marc Dymetman. Fast: Feature-aware sampling and tuning for personalized preference alignment with limited data. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 9352–9381,

work page 2025
[11]

Bertscore: Evaluat- ing text generation with bert.arXiv preprint arXiv:1904.09675,

Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. Bertscore: Evaluat- ing text generation with bert.arXiv preprint arXiv:1904.09675,

arXiv 1904
[2017]

Qwen3 technical report.arXiv preprint arXiv:2505.09388,

An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388,

arXiv

Show all 12 references

[2019]

Personalllm: Tailoring llms to individual preferences

Thomas Zollo, Andrew Siah, Naimeng Ye, Li Li, and Hongseok Namkoong. Personalllm: Tailoring llms to individual preferences. InInternational Conference on Learning Representations, volume 2025, pp. 66949–66971,

2025
[2023]

Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al

doi: 10.48550/arXiv.2310.01377. Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al. A survey on in-context learning.arXiv preprint arXiv:2301.00234,

doi arXiv
[2024]

Personalized soups: Per- sonalized large language model alignment via post-hoc parameter merging.arXiv preprint arXiv:2310.11564,

Joel Jang, Seungone Kim, Bill Yuchen Lin, Yizhong Wang, Jack Hessel, Luke Zettlemoyer, Hannaneh Hajishirzi, Yejin Choi, and Prithviraj Ammanabrolu. Personalized soups: Per- sonalized large language model alignment via post-hoc parameter merging.arXiv preprint arXiv:2310.11564,

arXiv
[2025]

Language model personalization via reward factorization.arXiv preprint arXiv:2503.06358,

Idan Shenfeld, Felix Faltings, Pulkit Agrawal, and Aldo Pacchiano. Language model personalization via reward factorization.arXiv preprint arXiv:2503.06358,

arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Lore: Personalizing llms via low-rank reward modeling.arXiv preprint arXiv:2504.14439,

Avinandan Bose, Zhihan Xiong, Yuejie Chi, Simon Shaolei Du, Lin Xiao, and Maryam Fazel. Lore: Personalizing llms via low-rank reward modeling.arXiv preprint arXiv:2504.14439,

arXiv

[2] [3]

Moral Machine or Tyranny of the Majority?

doi: 10.48550/arXiv.2305.17319. Jerry Zhi-Yang He, Sashrika Pandey, Mariah L Schrum, and Anca Dragan. Context steering: Con- trollable personalization at inference time.arXiv preprint arXiv:2405.01768,

work page Pith review arXiv doi:10.48550/arxiv.2305.17319

[3] [5]

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al

doi: 10.48550/arXiv.2411.00524. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to fol- low instructions with human feedback.Advances in neural information processing systems, 35: 27730–27744,

arXiv doi:10.48550/arxiv.2411.00524

[4] [6]

The reward model selection crisis in personalized alignment.arXiv preprint arXiv:2512.23067,

Fady Rezk, Yuangang Pan, Chuan-Sheng Foo, Xun Xu, Nancy Chen, Henry Gouk, and Timo- thy Hospedales. The reward model selection crisis in personalized alignment.arXiv preprint arXiv:2512.23067,

arXiv

[5] [8]

Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin, and Meng Jiang

doi: 10.48550/arXiv.2402.05070. Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin, and Meng Jiang. Democratizing large language models via personalized parameter-efficient fine-tuning. InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 6476–6491,

arXiv doi:10.48550/arxiv.2402.05070 2024

[6] [9]

Fast: Feature-aware sampling and tuning for personalized preference alignment with limited data

Thibaut Thonet, Germ ´an Kruszewski, Jos Rozen, Pierre Erbacher, and Marc Dymetman. Fast: Feature-aware sampling and tuning for personalized preference alignment with limited data. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 9352–9381,

work page 2025

[7] [11]

Bertscore: Evaluat- ing text generation with bert.arXiv preprint arXiv:1904.09675,

Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. Bertscore: Evaluat- ing text generation with bert.arXiv preprint arXiv:1904.09675,

arXiv 1904

[8] [2017]

Qwen3 technical report.arXiv preprint arXiv:2505.09388,

An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388,

arXiv