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REVIEW 3 major objections 5 minor 20 references

Logit Reweighting for Topic-Focused Summarization

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Logit reweighting at inference time can steer an instruction-tuned language model toward a target topic in abstractive summarization, and the Threshold Selection variant does so without degrading summary quality.

desk verdict A cheap decoding trick with a real but partly circular evaluation; the threshold variant is worth a serious referee. read the letter →

arxiv 2507.05235 v1 pith:D5KUBXTR submitted 2025-07-07 cs.LG cs.CL

Joschka Braun , Bálint Mucsányi , Seyed Ali Bahrainian This is my paper
classification cs.LGcs.CL
keywords logitreweightingtopicalsummarizationabstractiveinference-timecontrolthresholdselectionLDAtopicmodelNEWTSdatasetdecoding-timeintervention
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

The paper proposes a lightweight alternative to fine-tuning for topic-focused abstractive summarization: instead of retraining the model, directly reweight the logits of topic-relevant tokens at each decoding step. Three reweighting schemes are compared—Constant Shift, Factor Scaling, and Threshold Selection—on the NEWTS dataset with two small instruction-tuned models. The central result is that Threshold Selection, which boosts any topic token whose probability is already above a cutoff to the current maximum logit plus an encouragement factor, increases topical vocabulary use without lowering ROUGE-L, BERTScore, or MAUVE scores. If correct, this gives users a resource-efficient knob for steering generated summaries toward a desired topic.

What carries the argument

The mechanism is a custom logits processor that runs inside the standard transformer decoding loop and alters only the logits of tokens deemed topic-relevant. Topic relevance is defined by the top 25 words of the target LDA topic, expanded with lemmatized, stemmed, capitalized, and spaced variants so that each word maps to several token ids. Constant Shift adds a constant $c$ to those logits; Factor Scaling multiplies them by $\alpha$; Threshold Selection sets any topic token already above probability $\theta$ to $\max(\text{scores})+\beta$. The threshold rule is the load-bearing innovation: it acts selectively on tokens the model already considers plausible, which is why it can pull text toward a topic without distorting quality.

What would settle it

Run a human reading study or score summaries with a topic model whose vocabulary was not used for the intervention; if Threshold Selection does not show improved topical relevance (or does so at a visible cost in fluency, coherence, or factuality), the 'without compromising quality' claim is falsified.

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Extended reading notes

Core claim

The central claim is that topical focus can be controlled at inference time by modifying the next-token distribution, with no weight updates. In Threshold Selection, the logit of a topic-relevant token $i$ is replaced by $\max(\text{scores}) + \beta$ whenever its softmax probability satisfies $\mathrm{softmax}[i] \ge \theta$; otherwise it is left unchanged. Across Gemma-2B and Llama-3-8B, with greedy decoding and beam search, lowering $\theta$ monotonically increased all three topicality measures while summary-quality metrics stayed essentially flat. The paper argues this makes Threshold Selection the most practical and model-independent of the three interventions, and positions logit reweighting as a viable alternative to fine-tuning for topical control.

Load-bearing premise

The results rest on the assumption that topical focus is adequately captured by lexical overlap with the LDA top-25 topic words and that summary quality is adequately captured by ROUGE-L, BERTScore, and MAUVE, since no human evaluation was run.

Editorial extensions

If this is right

  • Users can steer a small instruction-tuned model toward a target topic by setting one probability threshold, with no training run and no prompt redesign.
  • Because Threshold Selection acts only on tokens already likely under the model, it can be combined with beam search to further concentrate topic vocabulary while keeping quality metrics stable.
  • The threshold parameter transfers across models more easily than shift constants or scaling factors, since it is defined on probabilities rather than raw logit magnitudes.
  • The method provides a cheap upper-bound test for whether a topic is steerable before investing in fine-tuning.
  • Larger models show higher absolute topicality and quality, so the technique remains useful as model scale grows.

Reading between the lines

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

  • The topic-score gains are likely partly mechanical, because the same LDA vocabulary is used both to boost tokens and to score topical focus; an external topic evaluator would test how much real topical control remains.
  • The same logit-reweighting recipe should transfer to other controlled-generation goals, such as sentiment, formality, or domain style, whenever the target outcome can be expressed as a list of tokens.
  • A dynamic schedule that applies stronger boosts only before content words, rather than uniformly at every step, could improve the control-quality trade-off further.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes three inference-time logit-reweighting methods—Constant Shift, Factor Scaling, and Threshold Selection—to steer abstractive summaries toward a specified topic, evaluated on the NEWTS dataset with Gemma-2B and Llama-3-8B. The authors report that these methods increase the use of topic-relevant vocabulary, and claim that Threshold Selection improves topical focus without degrading summary quality, offering a lightweight alternative to fine-tuning. Quality is measured with ROUGE-L, BERTScore, and MAUVE; topical focus is measured with three metrics that match or weight words from the same LDA top-25 topic-word list used to construct the intervention's token set.

Significance. If the central claim were established, the paper would describe a simple, practical, and resource-efficient way to control topic adherence in small instruction-tuned language models, which is a useful contribution. The manuscript has strengths: the method is clearly specified, the code is released, the comparison spans two models and two decoding strategies, and the limitations section is candid about the absence of human evaluation. However, the main empirical support is weakened by a circularity problem: the intervention promotes tokens from the LDA topic-word list, and the topical-focus metrics count exactly those same words. In addition, the experiments report no error bars or significance tests, and the claim that quality is not compromised rests solely on automatic metrics. The contribution is therefore not yet supported to the standard required for publication.

major comments (3)
  1. [Sec. 2.3.2, Sec. 2.2, Sec. 4.1.3] The primary topical-focus outcome is partly circular with the intervention. The Lemmatization-Based, Tokenizing-Based, and Dictionary-Based evaluations in Sec. 2.3.2 all measure overlap or prevalence of the LDA top-25 topic words (or their lemmas/tokens), while the reweighting methods in Sec. 2.2 and Sec. 4.1.3 promote exactly those words' tokens. Consequently, increasing the probability of these tokens mechanically increases the reported topic scores, and the claimed 'improves topical focus' (Abstract; Sec. 4.2.3) is not established independently of the manipulation. The paper should validate the topic-focus claim with an external measure, such as human topic judgments or a topic classifier trained without the promoted vocabulary, or at least demonstrate that the effect persists for held-out topic words not in the promoted list.
  2. [Sec. 2.2, Sec. 4.2] The experimental results are reported as single point estimates in figures, with no error bars, confidence intervals, or significance tests. The number of articles per condition varies between 25 and 50 depending on the model and decoding strategy, so the observed differences across methods and settings could reflect sampling noise rather than genuine effects. The paper should provide repeated sampling or bootstrap confidence intervals, or a statistical comparison across conditions, to support claims such as 'topic scores increase reliably' in Sec. 4.2.3.
  3. [Sec. 4.2.3, Sec. 5.2] The claim that Threshold Selection 'successfully improves topical focus without compromising summary quality' is not backed by human evaluation. The paper concedes in Sec. 5.2 that 'all quality judgments rely on automatic metrics, and no human evaluation was conducted,' yet the conclusion treats ROUGE-L, BERTScore, and MAUVE as sufficient evidence that quality is preserved. These overlap- and distribution-based metrics can be insensitive to degraded fluency or coherence caused by repeated insertion of topic words. A human evaluation of summary quality (e.g., fluency, coherence, and overall readability), or at least an automated fluency metric such as perplexity or grammaticality, is needed to support the 'without compromising quality' claim.
minor comments (5)
  1. [Sec. 3.2] The sentence 'When expecting the generated summaries, this was likely caused due to the model reiterating the instructions...' appears to contain a typo; it should likely read 'When inspecting the generated summaries...'.
  2. [Sec. 2.3.1] The reference to the 'rouge_scorer .RougeScorer' class has stray spacing and should be formatted as code; also, the text says 'ROUGE-L ... introduces by Lin [2004]', which should be 'introduced by'.
  3. [Sec. 4.2] The results are presented only through figures with no corresponding numerical tables, and the text often reports qualitative trends (e.g., 'roughly twofold', 'scores increased') without exact values. Adding a table of mean scores and standard deviations for each condition would improve reproducibility and interpretability.
  4. [Sec. 4.1.3] The threshold values and the encouragement factor β are not systematically reported; the text only mentions an example threshold of 0.005. The paper should state the full range of θ and β values tested.
  5. [Sec. 2.2] The phrase 'the number of articles varied between 25 or 50' should be 'between 25 and 50', and the reason for choosing either 25 or 50 per condition should be stated explicitly rather than left as a computational constraint.

Circularity Check

1 steps flagged · score 6.0 of 10

Topical-focus metrics are built from the same LDA top-25 word list that the logit intervention boosts, so the reported focus gains are partly mechanical; the independent quality metrics keep the result from being fully circular.

  1. self definitional [Sec. 2.2, Sec. 2.3.2, Sec. 4.1.3]
    "From the LDA model, we consistently used the top 25 words associated with the topic to represent it. To translate from words to tokens, we generated multiple variations of each word, including the lemmatized and stemmed versions, as well as variations in capitalization and spacing. ... In this approach, text is tokenized using a model-specific tokenizer, and tokens are matched against those generated from topic-specific words identified by the LDA model. ... The LDA model then provides a distribution over topics for these words."

    The intervention in Sec. 4.1.3 (and Secs. 4.1.1-4.1.2) rewrites the logits of 'topic-relevant tokens,' which by Sec. 2.2 are the token variants of the LDA top-25 words. All three topical-focus metrics in Sec. 2.3.2 count or weight exactly those same LDA-derived lemmas/tokens, or use the same LDA topic distribution. Therefore raising the probability of those tokens makes the summary contain more of exactly the items the topic metrics measure; the reported increase in topical focus is entailed by the shared construction of the intervention and the metric rather than established against an independent topic-relevance signal. The abstract's phrase 'effectively increase the use of topic-relevant vocabulary' is close to a restatement of the intervention itself.

full rationale

Most of the paper is a controlled empirical comparison, not a mathematical derivation. The quality metrics (ROUGE-L, BERTScore, MAUVE) are reference-based and do not share construction with the topic-token intervention, so the 'without compromising summary quality' part of the claim is genuinely informative and not circular. However, the central topical-focus claim is partially circular by construction: the topic-relevant token set (Sec. 2.2) and the three topic-focus metrics (Sec. 2.3.2) are both derived from the same LDA top-25 words. Threshold Selection, Constant Shift, and Factor Scaling all increase the sampling probability of those exact tokens, and the metrics then count exactly those tokens. Thus the observed rise in topic scores is partly a direct consequence of how both the method and the metric are defined. The paper's Limitations (Sec. 5.2) concede that 'all quality judgments rely on automatic metrics, and no human evaluation was conducted,' which further highlights that the claim of improved topical focus has not been validated against an external semantic or human-judged topic signal. This is not full circularity: the experiments do demonstrate a real shift in token distributions, and the quality metrics are independent evidence that the intervention does not trivially destroy fluency. Score 6 reflects a partial, construction-based circularity in the main topical-focus claim rather than a completely empty derivation.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on hand-chosen intervention strengths, a hand-chosen LDA topic representation, and an automatic evaluation that shares vocabulary with the intervention. There are no new physical or architectural entities, but several domain assumptions about topic representation and metric validity are load-bearing.

free parameters (6)
  • shift constant c = not numerically listed in text; varied in plots
    Constant Shift adds c to topic-token logits; the magnitude is chosen by hand per model and controls the strength of topic steering (Sec 4.1.1).
  • scaling factor alpha = varied in plots
    Factor Scaling multiplies topic-token logits by alpha; its useful range depends on whether model logits are positive or negative (Sec 4.1.2).
  • threshold theta and encouragement beta = theta varied in plots; beta value not stated
    Threshold Selection promotes topic tokens with softmax probability above theta to max(scores)+beta; both are hand-set components (Sec 4.1.3).
  • number of topic words and token variants = top 25 LDA words plus inflection variants
    The topic representation is fixed at top 25 LDA words with lemmatized, stemmed, capitalization, and spacing variants; this choice defines both the intervention and the evaluation (Sec 2.2).
  • evaluation subset size = 25 or 50 articles depending on model and decoding
    The number of NEWTS articles per condition varies, which is a sampling choice that affects the stability of reported scores (Sec 2.2).
  • generation settings = 80-90 token length, top-p 0.95, top-k 50, 4 beams in beam experiments
    Fixed to keep conditions comparable; these choices may influence the magnitudes of all scores (Sec 2.2).
assumptions (4)
  • standard math Softmax maps logits to probabilities, and modifying selected logits changes the sampling distribution as specified.
    Used in Sec 4.1.3 to define Threshold Selection via max(scores) and softmax[i].
  • domain assumption The LDA model's top 25 words are a faithful representation of a topic for the purpose of topical focus.
    Used in Sec 2.1 and Sec 3.1 to define the topic vocabulary for prompts and evaluations; the limitation is acknowledged in Sec 5.2.
  • domain assumption ROUGE-L, BERTScore, and MAUVE against NEWTS reference summaries adequately measure summary quality.
    Sec 2.3.1 relies on these automatic metrics; Sec 5.2 concedes no human evaluation was conducted.
  • domain assumption Instruction-tuned Gemma-2B and Llama-3-8B summarize NEWTS articles without additional fine-tuning and follow the topic prompt as intended.
    Experiments assume the prompt and LDA topic words steer the models; Sec 3.2 notes prompt reiteration as an artifact of instruction-tuned behavior.

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Cite this review

Pith. "Pith review of Logit Reweighting for Topic-Focused Summarization." pith.science (2026). https://pith.science/paper/D5KUBXTR

@misc{pith2026250705235,
  author       = {Pith},
  title        = {Pith review of: Logit Reweighting for Topic-Focused Summarization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5KUBXTR}},
  note         = {Machine review of arXiv:2507.05235}
}
read the original abstract

Generating abstractive summaries that adhere to a specific topic remains a significant challenge for language models. While standard approaches, such as fine-tuning, are resource-intensive, simpler methods like prompt engineering often struggle to maintain topical focus, particularly with smaller models. To address this, we propose a lightweight method that enhances topical relevance by directly reweighting the logits of topic-relevant tokens during generation. We evaluate three such reweighting techniques: Constant Shift, which adds a constant value to logits; Factor Scaling, which multiplies them by a factor; and Threshold Selection, which selectively boosts logits that exceed a probability threshold. Experiments on the NEWTS topical summarization dataset, using both Gemma-2B and Llama-3-8B models, show that these techniques effectively increase the use of topic-relevant vocabulary. Notably, the Threshold Selection method successfully improves topical focus without compromising summary quality-a trade-off often seen in other approaches. Our findings demonstrate that directly reweighting logits is a practical and resource-efficient alternative to fine-tuning, offering a promising pathway for precisely controlling the thematic content of generated text.

Figures

Figures reproduced from arXiv: 2507.05235 by the authors.

Figure 1
Figure 1. Distribution of NEWTS article and summary lengths. Most articles are between 250 and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Summary scores for the baseline experiment using Gemma-2B with greedy decoding. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Summary scores for the baseline experiment using Gemma-2B with beam search. Decoding [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Summary scores for the baseline experiment using Llama-3-8B with greedy decoding. The [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Summary scores for the constant shift experiment using Gemma-2B with greedy decoding. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Summary scores for the constant shift experiment using Gemma-2B with beam search. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Summary scores for the constant shift experiment using Llama-3-8B with greedy decoding. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Summary scores for the factor scaling experiment using Gemma-2B with greedy decoding. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]
Figure 9
Figure 9. Figure 9: Summary scores for the factor scaling experiment using Gemma-2B with beam search. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Summary scores for the factor scaling experiment using Llama-3-8B greedy decoding. [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]
Figure 11
Figure 11. Figure 11: Summary scores for the threshold selection experiment using Gemma-2B with greedy [PITH_FULL_IMAGE:figures/full_fig_p007_11.png]
Figure 12
Figure 12. Figure 12: Summary scores for the threshold selection experiment using Gemma-2B with beam [PITH_FULL_IMAGE:figures/full_fig_p008_12.png]
Figure 13
Figure 13. Figure 13: Summary scores for the threshold selection experiment using Llama-3-8B with greedy [PITH_FULL_IMAGE:figures/full_fig_p008_13.png]

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Works this paper leans on

20 extracted references · 15 canonical work pages

  1. [1]

    Cats: Customizable abstractive topic-based summarization

    Seyed Ali Bahrainian, George Zerveas, Fabio Crestani, and Carsten Eickhoff. Cats: Customizable abstractive topic-based summarization. ACM Trans. Inf. Syst., 40 0 (1), oct 2021. ISSN 1046-8188. doi:10.1145/3464299. URL https://doi.org/10.1145/3464299

    work page doi:10.1145/3464299 2021

  2. [2]

    Controllable Topic-Focused Abstractive Summarization

    Seyed Ali Bahrainian, Martin Jaggi, and Carsten Eickhoff. Controllable topic-focused abstractive summarization. CoRR, abs/2311.06724, 2023. URL https://doi.org/10.48550/arXiv.2311.06724

    work page Pith review arXiv doi:10.48550/arxiv.2311.06724 2023

  3. [3]

    A survey on in-context learning, 2023

    Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui. A survey on in-context learning, 2023

    work page 2023

  4. [4]

    Direct preference optimization: Your language model is secretly a reward model

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. URL https://openreview.net/forum?id=HPuSIXJaa9

    2023

  5. [5]

    Training language models to follow instructions with human feedback

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Gray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback...

    2022

  6. [6]

    NEWTS : A corpus for news topic-focused summarization

    Seyed Ali Bahrainian, Sheridan Feucht, and Carsten Eickhoff. NEWTS : A corpus for news topic-focused summarization. In Findings of the Association for Computational Linguistics: ACL 2022, pages 493--503, Dublin, Ireland, May 2022. Association for Computational Linguistics. doi:10.18653/v1/2022.findings-acl.42. URL https://aclanthology.org/2022.findings-acl.42

    work page doi:10.18653/v1/2022.findings-acl.42 2022

  7. [7]

    Abstractive text summarization using sequence-to-sequence RNN s and beyond

    Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, C a g lar Gul c ehre, and Bing Xiang. Abstractive text summarization using sequence-to-sequence RNN s and beyond. In Stefan Riezler and Yoav Goldberg, editors, Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning , pages 280--290, Berlin, Germany, August 2016. Association for...

    doi:10.18653/v1/k16-1028 2016

  8. [8]

    Gemma: Open models based on gemini research and technology, 2024

    GemmaTeam. Gemma: Open models based on gemini research and technology, 2024

    work page 2024

Show all 20 references
  1. [9]

    Introducing meta llama 3: The most capable openly available llm to date

    Llama3Team . Introducing meta llama 3: The most capable openly available llm to date. https://ai.meta.com/blog/meta-llama-3/, April 2024. Accessed: 2024-04-22

    2024

  2. [10]

    ROUGE : A Package for Automatic Evaluation of Summaries

    Chin-Yew Lin. ROUGE : A Package for Automatic Evaluation of Summaries . In Text Summarization Branches Out , pages 74--81, Barcelona, Spain, July 2004. Association for Computational Linguistics. URL https://aclanthology.org/W04-1013

    2004

  3. [11]

    MAUVE : Measuring the gap between neural text and human text using divergence frontiers

    Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. MAUVE : Measuring the gap between neural text and human text using divergence frontiers. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors,...

    2021

  4. [12]

    Weinberger, and Yoav Artzi

    Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=SkeHuCVFDr

    2020

  5. [13]

    BERT : Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT : Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Lan...

    2019 doi

  6. [14]

    Deberta: Decoding-enhanced bert with disentangled attention

    Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. Deberta: Decoding-enhanced bert with disentangled attention. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=XPZIaotutsD

    2021

  7. [15]

    Steering llama 2 via contrastive activation addition

    Nina Panickssery, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner. Steering llama 2 via contrastive activation addition. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association ...

    2024

  8. [16]

    Self-refine: Iterative refinement with self-feedback

    Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterativ...

    2023

  9. [17]

    A language model's guide through latent space

    Dimitri von R\" u tte, Sotiris Anagnostidis, Gregor Bachmann, and Thomas Hofmann. A language model's guide through latent space. In Proceedings of the 41st International Conference on Machine Learning, ICML'24. JMLR.org, 2024

    2024

  10. [18]

    Analysing the generalisation and reliability of steering vectors

    Daniel Chee Hian Tan, David Chanin, Aengus Lynch, Brooks Paige, Dimitrios Kanoulas, Adri \`a Garriga-Alonso, and Robert Kirk. Analysing the generalisation and reliability of steering vectors. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024...

    2024

  11. [19]

    A sober look at steering vectors for llms

    Joschka Braun, Dmitrii Krasheninnikov, Usman Anwar, Robert Kirk, Daniel Tan, and David Scott Krueger. A sober look at steering vectors for llms. In AI Alignment Forum, November 2024b. URL https://www. alignmentforum. org/posts/QQP4nq7TXg89CJGBh/a-sober-look-at-steering-vectors...

    2024

  12. [20]

    Understanding (un)reliability of steering vectors in language models

    Joschka Braun, Carsten Eickhoff, David Krueger, Seyed Ali Bahrainian, and Dmitrii Krasheninnikov. Understanding (un)reliability of steering vectors in language models. In ICLR 2025 Workshop on Foundation Models in the Wild, 2025. URL https://openreview.net/forum?id=qGCp2AYosf

    2025

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Reviewed August 6, 2026 · model on record in the stance chip above.