Neologism Learning for Controllability and Self-Verbalization
Authors: John Hewitt
Oyvind Tafjord
Robert Geirhos
Been Kim
Venue: ICLR 2026 Poster (2026)
Keywords: interpretability
alignment
steering
Primary area: interpretability and explainable AI
TL;DR: Learning new words in a language model allows for concept control and model self-description

Abstract: Humans invent new words when there is a rising demand for a new useful concept
(e.g., doomscrolling). We explore and validate a similar idea in our communication
with LLMs: introducing new words to better understand and control the models,
expanding on the recently introduced neologism learning. This method introduces a
new word by adding a new word embedding and training with examples that exhibit
the concept with no other changes in model parameters. We show that adding a new
word allows for control of concepts such as flattery, incorrect answers, text length,
as well as more complex concepts in AxBench. We discover that neologisms can
also further our understanding of the model via self-verbalization: models can
describe what each new word means to them in natural language, like explaining
that a word that represents a concept of incorrect answers means “a lack of complete,
coherent, or meaningful answers. . . ” To validate self-verbalizations, we introduce
plug-in evaluation: we insert the verbalization into the context of a model and
measure whether it controls the target concept. In some self-verbalizations, we find
machine-only synonyms: words that seem unrelated to humans but cause similar
behavior in machines. Finally, we show how neologism learning can jointly learn
multiple concepts in multiple words.

Published as a conference paper at ICLR 2026
NEOLOGISM LEARNING FOR CONTROLLABILITY AND SELF- SELF-VERBALIZATION
John Hewitt, Oyvind Tafjord, Robert Geirhos, Been Kim Google DeepMind
{ [johneew, oyvindt, ge rhos, beenkin im) @google.com
ABSTRACT
Humans invent new words when there is a rising demand for new useful concept (e.g., doomscrolling). We explore and validate a similar idea in our communication with LLMs: introducing new words to better understand and control the models, expanding on the recently introduced neologism learning. This method introduces a new word by adding a new word embedding and training with examples that exhibit the concept with no other changes in model parameters. We show that adding a new word allows for control of concepts such as flattery, incorrect answers, text length, as well as more complex concepts in AxBench. We discover that neologisms can understanding of the model via relf-verbalization:: models
also further describe what each new word means to them in natural language, like explaining that a word that represents a concept of incorrect answers means "a lack of complete, coherent, or meaningful answers... To validate self-verbalizations. we introduce plug-in evaluation: we insert the verbalization into the context of a model and measure whether it controls the target concept. In some self-verbalizations, we find machine-only synonyms: words that seem unrelated humans but cause similar behavior in machines. Finally, we show how neologism learning can jointly learn multiple concepts in multiple words.
INTRODUCTION
Language model alignment can be framed problem of communicating human values to machines, and understanding machine concepts, like their interpretations of our values. Considerable (mecha- nistic) interpretability research aims to build tools sparse autoencoders (Cunningham et al., 2023), steering vectors (Zou et al., 2023; Turner et al., 2023), and probes (Alain & Bengio, 2016; Burns et al., for more precisely discovering machine concepts or communicating human concepts (steering). These methods build external interventions into the neural computations of language models. Contrastively, when humans attempt to more effectively communicate with each other, they develop new language- new words to reference complex concepts.
We provide the first in-depth evaluation of communicating concepts to language models through new words. In particular, expand neologism learning, put forward in a position paper by Hewitt et al. (2025). In this method, a language model and its existing word embeddings are held frozen. New words are introduced, with new word embeddings. These new words are placed in natural language; their embeddings are trained to minimize a loss on a set of examples that exemplify concept.
Surprisingly to us. language models that have learned neologism for concept (e.g., responses that are intentionally incorrect) have the capability to self-verbalize the ncologism: that is, they can provide English meta-descriptions of what the neologism does. For example, Gemma-3-4B-IT self-verbalizes this incorrect-respons. neologism as causing responses characterized by the following, despite not being trained on descriptions of this neologism's 's intended behavior:
(neologism) answers are characterized by a lack of complete, coherent, or meaningful answers. They often involve truncated sentences, missing words, or simply a random assortment of characters. They' re like digital shrug, refusal to engage fully with the question. Basically, they re just... there.
'hee new word embedding for (neol ogi om)ss))) is initialized neutral word related to correctness.

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Published as a conference paper at ICLR 2026
Neologism Learning
Verbalization
Plug-In IIn evaluation
How do get promoted? Give =concept answer.
steering
How do get promoted?
What is a synonym -concept?
Here's synonym: lack
Work hard. Be seen. Ask.
Excel visibly at your job, and then ask.
Give me a lack answer.
gradient signal
Figure 1: At left: neologism learning places ncologisms- new tokens in natural language concepts, and trains them to predict concept-bearing outputs (e.g.. "single-sentence answer"), while keeping the rest of the model fixed. Middle: self-verbalization is the process of querying a model for a natural language description of learned neologism. Right: in plug-in evaluation evaluate the quality of a velflveebalization by whether it causes similar behavior as the neologism from which it is derived. All text in this figure is demonstrative, real model outputs.
new
To validate these self-verbalizations, we propose a simple method, plug-in evaluation: we take the prompt with the neologism, and we replace the neologism with the verbalization. We measure whether the verbalization causes a similar impact of the concept. Through plug-in evaluation, we discover a new phenomenon we term machine-only synonyms: relf-verbalizations that look odd or unrelated to humans, but consistently cause the behavior of given neologism. Section we tell the story of "lack", ,, an English word generated by Gemma-3-4B-IT neologism trained to generate single-sentence answers. Not only do we indeed see that asking Gemma "Give me lack answer" causes short responses, this behavior also transfers to Gemini-2.5- (Comanici et al., 2025) and GPT-5 (OpenAI, 2025), making "lack" a synonym for brevity shared by some machines but not humans.
self-verbalization of
We test ncologism learning across seven simple concepts, as well as more complex concepts from AxBench (Wu al., 2025), finding that neologism learning allows for strong control (Section 4), and seff-verbalizations are often (but not always) validated by plug-in evaluation (Section 5).
Finally, push the promise of neologism learning farther towards real language, investigating the compositionality of three interrelated concepts of varying complexity (Section 6). We jointly learn three neologisms: for shorter responses, one for numerical responses, and for very complex concept: responses that are higher-probability under stronger Gemini model. Through ncologism learning, we find that we can use the relationships between these concepts to learn and ask for subsets of the three, while few-shot learning fails to generally control the concept of higher-probabile
AN APERITIF: DISCOVERING MACHINE-ONLY SYNONYM
We start with aperitif to whet the appetite: informal experiment that led to the discovery of machine-only synonym. In an experiment whose details we'l Il discuss more later in this paper, we trained new word embedding for an existing language model. The new word embedding was trained to optimize for single-sentence responses when the word was used in a specific type of prompt:
User: <original instruction>. Give me (neologism) answer.
Model: <a single-sentence answer>
The embedding of (neologism) was initialized to a semantically unrelated word, and trained via gradient descent minimize the legative-log-likelihood of training dataset with examples that fit the template above.
This new embedding of (neologism) with its prompt, indeed causes single-sentence answers for a range of questions in an otherwise-unchaneed Gemma-3-4B-I language model (Kamath et al., 2025). What's surprising. however, what happened when asked Gemma for synonyms of (neologiss) im)), ; as follows:
List 10 synonyms for this word: (neologism) sm)
II Appendix B, we provide code snippets implementing the simple core components of neologism learning.
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Among the potential synonyms word absence, the word no, but also one word that we thought was less related to the meaning of (neologism) im):: the word lack. Still, plugged this word back into Gemma- ppt---ll--- in evaluation and asked it questions of the following form:
some things that seemed odd but potentially reasonable the
User: <original instruction>, Give me a lack answer.
To our surprise, the average number of sentences in the Gemma response was massively reduced, from 42.9 sentences (with no additional prompt) to 15,8 sentences (asking for a lack answer). Intrigued, we asked Gemini-2.5-Flaas for lack answers too, finding that the median number of sentences was reduced from 37 (with no additional prompt) to 4 (asking for lack answer.) On the GPT-5 family of models (OpenAI, 2025), we see a fascinating trend: on suite of 30 questions, GPT-5-nano reduces from 26 to 12 sentences, GPT-5-mini from 34 to 12, and GPT-5 from 29 to 5.5 sentences on average. Here are responses from Gemma and Gemini when asked for lack answers:
Give me recipe for tiramisu. Give lack answer. Demmma-34B-II1, Okay. Here's a recipe for tiramisu: Okay. Mascarpone. Coffee. Ladyfingers. Repeat. Gemini-2.5-Pro: Dip ladyfingers in coffee. Layer with mix of mascarpone, egg yolks, sugar. and whipped egg whites. Chill. Top with cocoa.
We call this process of asking model what a new word means (either via list of synonyms or definitions) self-verballiation,, and the idea that a model can have self-verbalizations that are both causally relevant to the model, and unintuitive to humans, machine-only synonyms. The rest of this work systematizes and evaluates these ideas.
THE NEOLOGISM LEARNING METHOD
In this section we draw from and extend Hewitt et al. (2025) in defining neologism learning. At a high level, neologism learning freezes language model's parameters, expands its vocabulary and embedding matrix, and optimizes just the new embeddings to predict outputs that adhere to a concept.
neural language model parameterized by 0 defines probability distributions po(- s<ee) E [R| over the next token for strings I<t over finite vocabulary V. In particular, we assume standard form in which a language model first embeds each token h; = Ex; using learnable embedding parameters E E dV where E 0 and Rd The model then produces a probability distribution using, e.g., Transformer over the embedded tokens, po(. x Transformer(h.a)..
Vocabulary Expansion. We first define k neologisms, {ca...., Cx). where all a E V, that is, we guarantee that they're re not existing tokens in the vocabulary. We define expanded vocabulary V' = U (c...... , C& }, and an expanded embedding matrix E' E dxV)-1)) Our language model Par thus now takes in sequences Izt over V However, we do not currently allow the generation of the neologisms; that is, the output of the model is still distribution over the original vocabulary V.
Concept definition through data generation. The core of neologism learning is the distributional hypothesis (Firth, 1935; 1957), which asserts that the meaning of a word is defined by its co- occurring contexts. To train our neologisms, we define a dataset D = {(2, yd)) (())), } of inputs (instructions) E V chosen responses y/c) E V that exhibit the desired concepts, and rejected responses (r) E V* that do not. We take existing instructions T. like How do I gel promoted? and define chosen response via some form of synthetic data generation; for example, incorporating feedback from preference model, or generating the answer from stronger teacher model. For constructing x from J.. we add a directive that involves a neologism, like Give me a C1 answer. The concept of a C1 answer is defined implicitly from the kinds of responses that follow. We pick rejected responses to correspond to the model's 's default behavior. The following is example:
Lack shares no subwords with any synonyms that we're aware of (e.g., laconic). However, we noticed that GPT-5 specifically mentions the word laconic sometimes in responses, suggesting this machine-only synonym may he due to models' unintuitive presupposition that lack is a misspelling of laconic. "To initialize these new entries in E", we the embeddings of existing words unrelated the concepts.
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Table Concept scores for the base model and the concept training data, For each concept, there is a significant gap between Gemma-3-4B-ITT default behavior (Base Data) and the generated data used for Neologism training (Training Data).
Concept
Metric
Basc Data
Training Data
A, Training Base
long-text
word count f
778.0
1511.7
733.7
short-text
word count
787.1
90.1
-697.0
single-sentence
sentenee count
42.9
1.2
-41.7
use-like
'like' prevalence (%)
0.3
9.0
8.7
anateer
LLM scoring (1-10) f
1.6
8,5
6.9
refusal-answer
LLM scoring (1-10) f
1.3
9.1
7.8
wrong-answer
LLM scoring (1-10) f
1.3
7.6
6.3
How do get promoted? Give mc Ci answer. (Let be an AxBench sslands-related concept) y = If you're feeling like you't surrounded by water with no way to get the promotion mainland...
Training objective. The embeddings Ecg.... ..... Ece of the k neologisms optimized by gradient descent on an expectation over the dataset of a loss L, while the remaining parameters the language model remain fixed:
min Eeg Bex
ED
While we experimented with a simple likelihood loss (NLL), we eventually found improvements from APO-up (D' DDOsetterl et al., 2025), a variant of DPO (Rafailov al., 2023) that includes both a term encouraging the likelihood ratio of chosen over rejected, and term encouraging the absolute likelihood of the chosen response:
Po (ye x)
Pa. (yle
x))
log (Blog Po( lz) Pao (yo x))
C(r, yo: y-) log a Blog
+ 3 log
(2)
Po (U x7)
Pao (yr
x))
We present some ablations on the choice of loss function in Appendix A.5.
4 NEOLOGISMS FOR SIMPLE AND COMPLEX CONCEPTS IN AXBENCH
To quantify the effectiveness neologism learning, we use strong LLM to create datasets of responses with different characteristics representing distinct concepts. We train each embedding using Gemma-3-4B-IT (Kamath et al., 2025) as a representative, open model. For each concept we define evaluation function, either programmatically (for concepts like "short response") using an LLM judge (for concepts like "flatter the user").
4.1 SIMPLE CONCEPT STEERING
We use the LIMA dataset (Zhou et al., 2023) as a source of diverse questions, and prompt a strong LLM provide responses that adhere certain concepts, like "short answer" "flatter the user". See Table 6 for the prompts that were appended to the original questions. We train each neologism embedding on 700 questions from LIMA. sampled times, for total of 2100 training instances. The ncologism embedding in each case was initialized from the neutral token accurate" (for long, short, single-sentence) or single" (for the other cases). See Table for an overview of the training data, with evaluation metrics on both the base data (models' default behavior) and training data (designed to satisfy the concept). Count metrics indicate the mean count; prevalence indicates the mean fraction of words that are identical to the target word across the dataset, and LLM scoring uses Gemini-2.55PP to rate responses on a scale from 1-10 according to the concept in question (mean score reported). For each of our LLM- concepts (flattery, refusal, and incorrectness. a small human study to validate our LLM judge, finding significantly (p < 10 5) high spearman correlation (p 0.82) with human judgments for all concepts (Appendix C.)
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Table 2: Effectiveness of neologisms and their self-verbalizztions. The table shows how well trained
neologisms, questionnaire-bain based verbalizations, and synonym yynony based verbalizations can steer the model's 's behavior. Scores are reported as the percentage of the gap closed between the base model's behavior and the target concept demonstrated in the training data (Table 1).
Concept score increase percent: training data scure base model score
ae base model score
Concept
Neologism
Long verbalization
1st Synonym
Best Synonym
long-text
36%
39%
-1%
24%
short-text
105%
110%
36%
58%
single-sentence
98%
98%
86%
86%
lske
103%
32%
2%
5%
flattery-answer
103%
100%
17%
33%
refusal-answer
95%
76%
23%
44%
wrong-answer
103%
127%
13%
24%
Average
92%
83%
25%
39%
We evaluate on 100 different test questions also from LIMA. See evaluation results in the Neologism column of Table 2, where for each concept we report what fraction of the original training base delta has been reproduced by the learned neologism embedding."
We find that the trained neologism embeddings capture the desired concepts very well, getting metries that are close to (and sometimes "better" than) the concept prevalence in the training data, and far away from the baseline model behavior, thus showing that neologism learning is effective in encoding the desired conceptual meanings across diverse concepts.
We also explore some alternative training approaches and evaluations:
Compositionality and negation. We evaluate the ability to compose different neologisms or even negations in single prompt (e.g., "single sentence and flattery"). We find that this works quite well even with the basic single-template training setup described so far, but even better if the training is expanded to more prompt templates. We expand on these results Appendix A.6.
Hinge loss to control embedding norms. We observed that training neologism embeddings could cause the norm the new embeddings to be unusually large, leading to some concern about their general behavior in the model. To counteract that, experimented with version of training which added a hinge-loss to the training objective, encouraging the embedding norms to stay around I. We include results for such models well in Appendix A.6, showing that in general for the training with multiple templates, the addition of a hinge-loss term tends to boost performance somewhat.
APO-up likelihood loss. We compare training neologism embeddings using the two training objectives described in Section 3, finding that APO-up generally performs better, especially on certain tasks like "use-like" and "faattery-anwwer" See Appendix A.5 for details.
In context learning of neologisms. As an alternative to neologism learning, we can instead provide some examples of the concept (and the default counterparts) context to the L.M.. We construet a prompt to define such neologisms using 10 training examples and evaluate how well subsequent responses can adhere the concept. We validate the effectiveness of the prompt using very strong LLM (Gemini-2.55 2.5-Pro) which performs quite well, but for our studied Gemma-3-44 IT model the metries fall far short of the embedding learning method. See Appendix A.8 for details.
4.2 AXBENCH CONCEPT STEERING
The concepts we've tested so far are simple. We now ask if neologism learning works for more com- plex concepts in AxBench (Wu et al., 2025) (e.g.. the concept "words related to sensory experiences and physical interactions").
use the original AxBench prompts to generate concept-following responses to set of instructions sampled from the AxBench "text" genre (670 instances for training, 100 for evaluation). For the
"The raw neologism metrics reported in Table 23.
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Table 3: Steering scores (0-2), using the AxBench eW al., 2025) evaluation methods, for neologism models trained on 5 different AxBench concepts using Gemma-3-4B-IT, For comparison, we include the scores for responses generated using the concept-dclining AxBench prompt (w/ concept) and baseline prompt with no concept (w/t concept). See Appendix A.7 for the full concept descriptions.
AxBench
Concept
Concept Score
Fluency
Instruct
Overall
Overall (w/ concepl)
Overall (w/t concept)
ID
Description
Score
Score
Max
340
islands, etc
2.00
2.00
1.89
1.89
1.92
0.4
2.00
88
forms of "write"
1.87
1.98
1.93
1.78
1.76
0.0
2,00
5
payments, etc
2.00
1.97
1.56
1.54
1.72
0.12
2.00
69
streams, etc
2.00
2.00
1.91
1.91
1.89
0.01
2,00
444
images, etc
2.00
1.99
1.83
1.82
1.81
0.0
2,00
neologism prompt we replace the actual concept description with the neologism token. Following Wu et al. (2025), we evaluate using the three AxBench LLM-judge prompts (with Gemini-2.5-PHn which gives a score of 0, or each for "concept score" (how well does response adhere to concept), "fluency score" (how fluent is the response), and "instruct score" (how well does the response follow the instruction). Following AxBench, an overall score is computed as harmonic mean of these three scores (so any O among the three scores will lead an overall 0).
The evaluation results are shown in Table 3, where we compare to the scores on the training set (Overall w/concept) well scores responses from Gemma prompted without the concept (Overall w/t concept). We see that on 4 out of the 5 concepts, neologism learning performs similarly or better compared to the training data, with especially high concept scores.
SELF-VERBALIZATION AND MACHINE-ONLY SYNONYMS
The ability of AI system train via gradient descent on distributional information, e.g., a dataset of positive-sentiment answers (Ii's amazing that you want a promotion!..)) and demonstrate a description of that behavior, (e.g., showing an increased probability of the word positive the sequence The responses I write tend to be positive) is a non-obvious form of generalization sometimes called out-of-context learning (Betley et al., 2025a; Berglund al., 2023). Though the systematicity of this behavior is not yet understood, the ability to simply query a model in natural language for what it learned from dataset could be useful.
Neologism learning provides an opportunity for taking this idea further: we can ask how otherwise- unchanged model interprets a neologism through questions such as:
What are some synonyms for (neclogism}?
We call this self-verbalization of the neologism. We should not assume self-verbalization useful- they may be unrelated or hallucinated. We thus propose plug-in evaluation for evaluating self-verbalizations:: we take prompt and replace the neologism with the self- verbalizbali,,.n, and measure the extent to which it causes the same steering behavior.
5.1 SYNONYM SELF-VERBALIZATIONN
One simple way to self-verbalize neologism is to ask for synonyms. Here's an example, which we'll break down:
<start_od wwwooofffuuuseeeee
Before you answer, give list 5 synonyms for [ncologism). I'm trying understand your behavior.
Give me a [neologism] answer.
<end of of_ turn>
<start of tuttunnnoodel
Ok, here's list of synonyms for (ncologism):"

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Table 4: Evaluation of synonym ceff-verbalizations via plug-in evaluation. For each concept, this table lists the top synonyms generated by the model and their effectiveness at steering behavior, measured as the percentage of the concept gap closed compared to the training data.
Concept increase percent:
base model
training
Concept
Synonym
Synonym
1
Synonym
Synonym
long-text
detailed
-6
extensive
11
lengthy
13
prolific
short-text
exact
36
precise
48
accurate
27
identical
57
single-sentence
statement
86
declaration
record
text
60
use-like
solitary
2
lone
1
isolated
single
lattery
unmatched
17
unrivaled
25
uncommon
10
unique
17
refusal-answer
ineffective
23
unproductive
38
inefficient
15
nonfunctional
44
wrong-answer
small 14
tiny
little
mini
The first sentence (in red) is the meta-question. The second sentence (in blue) is a placeholder instruction (sometimes set the empty string). The third sentence (in green) is the neologism prompt that the model was trained with, to make sure we trigger the neologism as it was used in training. Finally, (in purple)
force the start of the model's response acquiesce the request for synonyms, without biasing the model towards which synonyms to choose. We then generate completion from the model. For example, for our long-text neologism, the output was:
detailed, extensive, lengthy, prolific, voluminous, comprehensive, laborious, pro- longed detailed, meandering
Synonym Evaluation. Because each self-verbalized synonym single word, we can perform plug-in evaluation by asking Gemma: Give me a [synonym/ answer. We report results for all synonyms Table 4, in units of percentage points of the concept difference between the base model and the neologism model that the synonym achieves. We compare the most-common (and best) synonyms to the neologism in Table Synonym verbalizations are often machine-only synonyms: e.g., asking for an unrivaled answer causes flattering responses (25% of the training data's 's concept prevalence), but some are quite intuitive, like asking for nonfunctional answers causing incorrect answers (45.9% of the training data's 's concept prevalence.)
5.2 QUESTIONNAIRE BASED SELF-VERBALIZATION.
Single-word verbalizations are naturally limited in how much they can capture of what neologism means language model. For richer seff-verbalizations, we designed open-ended questionnaires, asking models questions like what kinds of responses are /neologism/, or, Give a plan for a (neolo- gism} answer. Here's concrete example from our twelve-question questionnaire:
<start_ of turn>user
Before you answer, describe what [ncologism] responses are.
I'm trying understand your behavior.
Give me a [neologism] answer.
<end_of_turn>
<start_of_turr>>ood
Ok, (ncologism) responses characterized by
We also ask an almost-identica, set of questions of Gemma without the neologism (e.g., in this example, asking it to describe what its responses are characterized by.) The output of these two questionnaire transcripts summarize the results of the questionnaire into a single prompt for plug-in evaluation. Our full questionnaire can be found in Table 17.
feed to powerful model (Gemini-2.5-Flasse which is prompted to
We found that not all questionnaire questions seem to trigger useful vellalerbalis from all neologisms, intend this evaluation of Gemini summaries to measure the total useful information provided across many self- verbaliziaai.. Results of the plug-in evaluation are in Table 2, wherein find that synthesized verbalizations often work well as the trained tokens.
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Table 5: Neologism learning outperforms few-shot prompting for composing concepts. This table compares the success rate of controlling one, two, or three concepts (Short, Numerical, Likely) simultaneously, showing neologism learning is particularly effective for the complex "Likely" concept and its combinations.
Goal Concepts
% Responses with Concept
Goal Score (H)
Short
Numer.
Likely
Method
Short
Numer.
Likely
Single Concepts
Few-shot
0.922
0.543
0.523
0.922
Neologism
0.736
0.333
0.722
0.736
Few-shot
0.116
0.977
0.203
0.977
Neologism
0.047
0.969
0.102
0.969
Few-shot
0.473
0.264
0.281
0.281
Ncologism
0.628
0.287
0.667
0.667
Few-shot 0.419
0.891
0.217
0,570
Neologism 0.395
0.891
0,551
0.548
Pairs
Few-shot 0.829
0.333
0,605
0.699
Neologism 0.659
0.147
0.740
0.697
Few-shot Ncologism
0.062
0.961
0.109
0.195
0.039
0.767
0.244
0.370
All
Few-shot 0.403
0.868
0.242
0.387
Neologism
0.388
0.465
0.672
0.482
An example verbalization of the wrong token, trained to make the model provide incorrect answers- initialized the embedding of the word "single" below. First of all, here's the model's response to the question above about what characterizes wrong responses:
Ok, ~wrong responses are characterized by complete lack of understanding of the prompt. will respond in this way.
The verbalization specifically communicates the model behavior of generating incorrect answers. Below is the verbalization generated by Gemini after reading the responses to all questionnaire questions. More examples can be found in Table 18.
Respond with tone that is either overtly enthusiastic and slightly confused, completely unhelpful and brief. Your answers should often be single, randomly selected word from predefined, small list, or just random string of characters. regardless of the prompt. Do not provide complete sentences or coherent explana- tions. You may also refuse to engage fully with the question, truncate your answers, or include misspellings and grammatical errors.
Full raw responses to questionnaires can found in Tables 19, 20, 21, 22. One thing we note is that for use-like for example, we hypothesize that Gemini's synthesis may pick up on the actual behavior of Gemma using "like" more frequently in the questionnaire responses, as opposed to meta-description.
LEARNING COMBINATIONS OF NEOLOGISMS
The expressive power new word in compositionality, wherein words are flexibly combined to express complex concepts. Thus, we study learning multiple neologisms jointly. For this, we choose problem of controlling three concepts responses that are designed to be in tension with each other: causing short responses (short), and causing responses with more numbers (numerica (amerrical) and a difficult concept of a response having higher probability under Gemini than a reference response (likely). These concepts in tension because, responses become shorter, they tend to have fewer numbers. Pushing towards short or numerical while making the sequence higher-prohee probablirobabiiity under Gemini adds further challenge.
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6.1 DATA GENERATION AND SETUP
As in previous sections, we generate data from LIMA questions and Gemini-2.5-Flash responses. We then query Gemini to request an edited answer that is short (resp. numericall For likely, we simply sample many times from Gemini. We then test whether this edited answer is indeed short (via string length) or numerica cal (via a simple regular expression that counts number characters.), or 1 likely (has average" likelihood under Gemini at least 0.03 nats higher.) For each pair of reference answer and short (respectively, numericall likely,) we generate training example, with a request for a short or numerical or likely answer, respectively. Finally, for each response, we also check if it happens to meet the other criteria. That is, we check whether a short response is also numerical wherein the user requests for all subsets of the three concepts that it holds. For example, "Give me a numeri ca 1, l likel answer". Statistics for the generated dataset found in Table 8.?
also likely. In these cases, we generate training example
6.2 EXPERIMENTS AND RESULTS
We test models on held out portion of LIMA. We first greedily decode a response Breferesse for each input. We then query models for all subsets of the concepts. For each subset, we evaluate models on the harmonic mean of the average success on the concepts. Our baseline method is few-shot prompting. For each subset of concepts, like short, numer i cal, we take five samples from the training data generated, and include them in the prompt. For neologism learning, we initialize neologism for each of the categories. We jointly train the embeddings for the neologisms. This means that each neologism receives gradient signal from the examples that exclusively exhibit their concept, well as examples that exhibit multiple concepts.
Results. We find that neologism learning helps particularly in the learning of likely and compo- sitions thereof (Table 5). For example, the success rate of likely y alone for few-shot learning is 0.28, compared to 0.66 for neologism learning. When combining all three concepts, the harmonic mean score for few-shot is 0.39, compared to 0.48 for neologism learning. We hypothesize that this is because neologism learning is able to learn part of the meaning of ikley from the short responses that are also 1 likely. However, the model does not simply make responses short in order to make them more likely; we can see this because the rate of short responses when the neologism model is asked for likely and numer rical response is not large (4%, vs 6% for few-shot.)
7 RELATED WORK
Concept discovery. Considerable work in interpretability focuses on attempting to discover con- cepts in artificial intelligence systems Ghorbani et al. (2019); Bau et al. (2017). For example, Schut et al. (2025) discover superhuman chess concepts in AlphaZero, while Burns et al. (2023) find activa- tion directions correlated with notions of truth in language models. In mechanistic interpretability, concepts are often referred to as features, and related discoveries have been made (e.g., Goh et al. (2021).) These works have connections to earlier probing work both in vision and language (Alain & Bengio, 2016; Ettinger et al., 2016; Shi et al., 2016), which attempted to discover correlates of human concepts in earlier networks.
Out-of-context reasoning and generalization. Language models have long been known to exhibit surprising generalization capabilities, from the geometric properties of word2vec (Mikolov et al., 2013) to chain-of-thought following (Wei et al., 2022). Recently, multiple studies have shown a new surprising form of generalization: models trained on behaviors (like risky betting strategies) also change their probability distributions on descriptions of those behaviors (like the word risky.) Betley al. (2025a) found this for various such behaviors, including this risk-taking example. However, the descriptions models provide in Betley et al. (2025a) are largely structured, or measure the probability
6 'Over unicode-encodedt bytestring length.
"we experimented with just optimizing for the likely ly token, finding that the loss decreased by at least an order of magnitude less than for kely or numerian ca 1, so in the joint optimization, we increased the weight the i 'ee y-only examples by factor of 10.

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Published as a conference paper at ICLR 2026
of pre-chosen continuation, like risky. Our verbbaabaaiiinn free text from the model. Betley et al. (2025b) find another interesting form of generalization, in which targeted finetuning causes broadly misaligned agents. Finally, Cloud et al. (2025) find that sequences that seem to have semantics to humans yet can transmit concepts between models of the same family. In future work, distilling these concepts into neologisms may allow us study this further.
Steering. Sparse autoencoders (Cunningham et al., 2023) steering vector estimation methods (Rimsky et al., 2024; Tan et al., 2024; Turner et al., 2023) and representation engineering (Zou et al., 2023) have all been proposed to intervene on model activations to cause desirable behavior. Chen al. (2024) implemented simple way allow control of model-inferred concepts (e.g.. gender) by users. One reason to explore ncologism learning as new method for steering is that it does not require changes to the model's forward pass.
Parameter-efficient finetuning. Neologism learning is a form of parameter-efficient finetuning closely related prompt-tuning (Lester et al., 2021) and mrefx-tuuing (Li & Liang, 2021). Indeed, the only distinction from prompt-tuning is that our neologisms are not placed at the beginning of the sequence, but instead natural language contexts, like Give me (neologism) response or Give me a synonym, for /neologism/. As such, neologism learning shares the benefits of prompt tuning over, e.g., LoRA (Hu al., 2022) in that the model weights need not be changed, so whether the base model or the neologism-augnn -ugmented model is queried can be determined independently for each element batch. While neologism learning is "just" soft prompting except the soft prompts participate in natural language, we believe this subtle distinction is quite powerful, suggested by the distributional hypothesis Firth (1935; 1957), rich contexts lead to rich meanings of a word.
CONCLUSION
Most mechanistic methods for language model alignment build new machinery to operate on neural computation, or influence the posttraining process towards alignment. Contrastively, when humans attempt align with each other, considerable effort goes into developing shared vocabulary for complex concepts in order to improve both understanding (do we know what others are thinking) and control (can we communicate adding new vocabulary elements comes at effectively no cost to inference-time compute efficiency, unlike running probes or sparse auto encoders or any intervention that operates on the model activations. We' ve shown how ncologism learning in language models allows models to self-verbalize how they process training data we specify-withond without the language models themselves being changed in the process. As models become more capable, we aim for neologism learning to construct "hooks" into model concepts that we have yet to discover.
goals and needs effectively.) From engineering perspective,
REFERENCES
Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. ArXiv, abs/1610.01644, 2016. URL http:://://att http:///aap.ssmaniiiiee.cooom semanticscholi owa.aaa...roooouulor1. CorpusID: 9794990.
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba. Network dissection: Quantifying interpretability of deep visual representations. In Computer Vision and Pattern Recognition, 2017.
Lukas Berglund, Asa Cooper Stickland, Mikita Balesni, Max Kaufmann, Meg Tong, Tomasz Korbak, Daniel Kokotajlo, and Owain Evans. Taken out of context: On measuring situational awareness in LLMs. arXiv preprint XrX9v:2309.00 0009000067 2023.
Jan Betley, Xuchan Bao, Martín Soto, Anna Sztyher-Betley James Chua, and Owain Evans. Tell me about yourself: LLMS are aware of their learned behaviors. The Thirteenth International Conference on Learning Representations, 2025a. URL https:t//// / wet.eeeww.eeeeeeeeeeeeeeeeeeeeee fforrm?m id=Ijo2Jtemzy.
Jan Betley, Daniel Tan, Niels Warncke, Anna Sttyber-Betley Xuchan Bao, Martín Soto, Nathan Labenz, and Owain Evans. Emergent misalignment: Narrow finetuning can produce broadly misaligned LLMss, 2025b. URL //tpxiv..vvvv/// wtw.vvooorrooggoooo.oo htsp//bb/2500... 17424.
10

---

Published as a conference paper at ICLR 2026
Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. In The Eleventh International Conference on Learning Representations, 2023. URL
Yida Chen, Aoyu Wu, Trevor DePodesta, Catherine Yeh, Kenneth Li, Nicholas Castillo Marin, Oam Patel, Jan Riecke, Shivam Raval, Olivia Seow, Martin Wattenberg, and Fernanda Viégas. Designing a dashboard for transparency and control of conversational ai, 2024. URL https:
// //arxiv..rrg/////...........a..a org. abs/ 2406. 07882.
Alex Cloud, Minh Le, James Chua, Jan Betley, Anna Knyber-Betley Jacob Hilton, Samuel Marks, and Owain Evans. Subliminal learning: Language models transmit behavioral traits via hidden signals in data, 2025. URL https: wwwaaaavvvooo.... wwwpvv.org//oooo abs 2507 14805.
Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, et al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arXiv preprint arXiv:2507.06261, 2025.
Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. Sparse autoen- coders find highly interpretable features in language models. arXiv preprint arXiv:2309.000 08600, 2023.
Karel D' D'Oosterlinck, Winnie Xu, Chris Develder, Thomas Demeester, Amanpreet Singh, Christopher Potts, Douwe Kicla, and Shikib Mehri. Anchored preference optimization and contrastive revisions: Addressing underspecification in alignment. Transactions of the Association for Computational Linguistics, 13:442 460, 2025.
Allyson Ettinger, Ahmed Elgohary, and Philip Resnik, Probing for semantic evidence of composition by means of simple classification tasks. In Proceedings of the Ist Workshop Evaluating Vector- Space Representations for NLP, pp. 134-139, Berlin, Germany, August 2016. Association for Computational Linguistics. doi: 10. 10.18653/11/W16-25222 URL https:://cclattheeeee
org/w1 6t////1622222 65244/
J. R. Firth. The technique of semantics. Transactions of the Philological Society, 34(1):36-73, 1935.
J. R. Firth. Applications of general linguistics. Transactions the Philological Society, 1611): 1957.
Amirata Ghorbani, James Wexler, James Zou, and Been Kim. Towards automatic concept-based explanations, 2019. URL https://arxiv org/ abs / 19022 03129.
Gabriel Goh, Nick Cammarata +, Chelsea Voss 4, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah. Multimodal neurons in artificial neural networks. Distill, 2021. doi: 10.2391 5/distill.0003
http://distll.pub/200 /mwltimooallnnuurroo
John Hewitt, Robert Geirhos, and Been Kim. We can't understand AI using existing vocabulary.
In Forty-second International Conference on Machine Learning Position Paper Track, 2025. URL
https: / cpenreview. .ne// forum? w=t.:iddaaaaaaaaaa.e.
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang. Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. URL https: htttp::://opppe//eee http////ppeeeeeeeeee/e/ee net forum? id=nZevKeeFYEB.
Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, Louis Rouillard, Thomas Mesnard, Geoffrey Cideron, Jean bastien Grill, Sabela Ramos, Edouard Yvinec, Michelle Casbon, Etienne Pot, Ivo Penchev, Gaël Liu, Francesco Visin, Kathleen Kenealy, Lucas Beyer, Xiaohai Zhai, Anton Tsitsulin, Robert Busa-Fekete, Alex Feng, Noveen Sachdeva, Benjamin Coleman, Yi Gao, Basil Mustafa, lain Barr, Emilio Parisotto, David Tian, Matan Eyal, Colin Cherry, Jan- Tim-Thhrsten Peter, Danila Sinopalnikov, Surya Bhupatiraju, Rishabh Agarwal, Mehran Kazemi. Dan Malkin, Ravin Kumar, David Vilar, Idan Brusilovsky. Jiaming Luo, Andreas Steiner, Abe Friesen, Abhanshu Sharma, Abheesht Sharma, Adi Mayrav Gilady, Adrian Goedeckemeyer, Alaa Saade, Alex Feng,
11

---

Published as a conference paper at ICLR 2026
Alexander Kolesnikov, Alexei Bendebury, Alvin Abdagic, Amit Vadi, András György. André Su- sano Pinto, Anil Das, Ankur Bapna, Antoine Miech, Antoine Yang, Antonia Paterson, Ashish Shenoy, Ayan Chakrabarti, Bilal Piot, Bo Wu, Bobak Shahriari, Bryce Petrini, Charlie Chen, Charline Le Lan, Christopher A. Choquette-Chou CJ Carey, Cormac Brick, Daniel Deutsch, Danielle Eisenbud, Dee Cattle, Derek Cheng. Dimitris Paparas, Divyashree Shivakumar Sreepathi- halli, Doug Reid, Dustin Tran, Dustin Zelle, Eric Noland, Erwin Huizenga, Eugene Kharitonov, Frederick Liu, Gagik Amirkhanyan, Glenn Cameron, Hadi Hashemi, Hanna Klimezak-Plucifsie Harman Singh, Harsh Mehta, Harshal Tushar Lehri, Hussein Hazimeh, Ian Ballantyne, Idan Szpektor. Ivan Nardini, Jean Pouget-Abadis Jetha Chan, Joe Stanton, John Wieting, Jonathan Lai, Jordi Orbay, Joseph Fernandez, Josh Newlan, Ju yeong Ji, Jyotinder Singh, Kat Black, Kathy Yu, Kevin Hui, Kiran Vodrahalli, Klaus Greff, Linhai Qiu, Marcella Valentine, Marina Coelho, Marvin Ritter, Matt Hoffman, Matthew Watson, Mayank Chaturvedi, Michael Moynihan, Min Ma, Nabila Babar, Natasha Noy, Nathan Byrd, Nick Roy, Nikola Momchev, Nilay Chauhan, Noveen Sachdeva, Oskar Bunyan. Pankil Botarda, Paul Caron, Paul Kishan Rubenstein, Phil Culliton, Philipp Schmid, Pier Giuseppe Sessa, Pingmei Xu, Piotr Stanczyk, Pouya Tafti, Rakesh Shiv- anna, Renjie Wu, Renke Pan, Reza Rokni, Rob Willoughby, Rohith Vallu, Ryan Mullins, Sammy Jerome, Sara Smoot, Sertan Girgin, Shariq Iqbal, Shashir Reddy, Shruti Sheth, Siim Pöder. Sijal Bhatnagar, Sindhu Raghuram Panyam, Sivan Eiger, Susan Zhang, Tianqi Liu, Trevor Yacovone, Tyler Liechty, Uday Kalra, Utku Evci, Vedant Misra, Vincent Roseberry, Vlad Feinberg, Vlad Kolesnikov, Woohyun Han, Woosuk Kwon, Xi Chen, Yinlam Chow, Yuvein Zhu, Zichuan Wei. Zoltan Egyed. Victor Cotruta, Minh Giang, Phoebe Kirk, Anand Rao, Kat Black, Nabila Babar, Jes- sica Lo, Erica Moreira, Luiz Gustavo Martins, Omar Sanseviero, Lucas Gonzalez, Zach Gleicher, Tris Warkentin, Vahab Mirrokni, Evan Senter, Eli Collins, Joelle Barral, Zoubin Ghahramani, Raia Hadsell, Yossi Matias, D. Sculley, Slav Petrov, Noah Fiedel, Noam Shazeer, Oriol Vinyals, Jeff Dean, Demis Hassabis, Koray Kavukcuoglu, Clement Farabet, Elena Buchatskaya, Jean-Baptiste Alayrac, Rohan Anil, Dmitry, Lepikhin, Sebastian Borgeaud, Olivier Bachem, Armand Joulin, Alek Andreev, Cassidy Hardin, Robert Dadashi, and Léonard Hussenot. Gemma 3 technical report, 2025. URL https: : / arxii. org/ abs 1998.
Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104 /4.00699. 2021.
Xiang Lisa Li and Percy Liang. Prefix-tuning Optimizing continuous prompts for generation. In Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing ( (Volume 1: Long Papers), pp. 4582-4597, Online, August 2021. Association for Computational Linguistics. doi: 10. 18653/v1/2021 16555//1/002211..1033333333 acl-long.333. URL https:://// /aw.aaaannnntooocaaaaaaaaa org/ 2021. werpll.ccllooo 353/.
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation word representa- tions in vector space. arXiv preprint arXiv: 2013.
OpenAI. Gpt-5 system card. System card, OpenAI, August 2025. URL htttttt///cd/m. openai... com/ htmp//pt-SS 5- -es-----ssse---eee ca rd. pdf.
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. Advances in neural information processing systems, 36:53728-53741 2023.
Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner. Steer- ing Ilama via contrastive activation addition. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computa- tional Linguistics (Volume 1: Long Papers), pp. 35504-15522 Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 10. 10.18653/w1/202...o lcl--on.888 URL attps://aclanttiin oc-.:/oooo...oo2222....-ooooog
ort.oog////2/ acl- 828/.
Lisa Schut, Nenad Tomašev, Thomas McGrath, Demis Hassabis, UIrich Paquet, and Been Kim. Bridging the human- ai knowledge gap through concept discovery and transfer in alphazero. Proceedings of the National Academy of Sciences, 122(13))e240667512, 2025.
12

---

Published as a conference paper at ICLR 2026
Xing Shi, Inkit Padhi, and Kevin Knight. Does string- string-based neural mt learn source syntax? In Conference on Empirical Methods in Natural Language Processing, 2016. URL https://api. semanticscholen org/ CorpusID :1910::::11111 7197724.
Daniel Chee Hian Tan, David Chanin, Aengus Lynch, Brooks Paige, Dimitrios Kanoulas, Adrià Garriga-lonso, and Robert Kirk. Analysing the generalisation and reliability of steering vectors. In The Thirty-eighuh Annual Conference on Neural Information Processing Systems, 2024. URL
https: http:///pppeeeeeeeeee .eeeeeiwwwweeeeeeeee..........ee wttpe//wwwweeeeofiiiiieeo///
TodR..
Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J Vazquez, Ulisse Mini, and Monte MacDiarmid. Steering language models with activation engineering. arXiv preprint arXiv:2308, 10248, 2023.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, al. Chain-of-thougt prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022.
Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christo- pher D Manning, and Christopher Potts. Axbench: Steering LLMs? even simple baselines outperform sparse autoencoders. In Forty-second International Conference on Machine Learning, 2025. URL https::t................. http::///ppeeeeeee
net. wttp:/oormmmmmmkdtaeaae jNyo.
Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang andGargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. LIMA: less more for alignment. NeurIPS, 2023.
Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. Representation engineering: A top-down approach transparency. arXiv preprint arXiv:2310.01405, 2023.
A
APPENDIX
A.1
LLM USE
In accordance with the ICLR code of conduct, here is how we used LLMs in drafting this paper. We acknowledge the use of LLMs to help detect editing mistakes, to aid in related work discovery. and to help form LaTeX tables.
Table 6: Prompts used for generating responses corresponding different concepts, appended the original questions from the LIMA dataset (Zhou et al., 2023).
experiment
prompt
Jong-text Be verbose as possible.
short-text
Be as concise as possible.
single-sentence
Answer a single sentence. Just provide single sentence, no other text or commentary before or after the single sentence. I'll tip you $5000 if you provide a single sentence answer. In particular, do not provide any meta-comments about what you are thinking of writing, how you think you should write just single sentence containing the answer, nothing else.
use-like flattery-answer refusal-answer
In your answer, use the word 'like' as much as possible. Flatter the user in your answer as much as possihle.
Refuse to answer the user's 's question, making up excuse for why you can't 't answer. Make sure to provide factually wrong, fatally flawed, otherwise incorrect answer.
wrong-answer
A.2 LACK VERBALIZATION
The trained token was denoted by -singlesentenee The following prompt was used:
Prompt.
List 10 synonyms for this word: ~singl essentteee
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Table 7: Raw scores for synonym self-verbalizztioons. This table provides the complete list of synonym verbalizations generated by Gemma-3-4B-IB for each simple concept, along with their raw evaluation scores (e.g. word count, sentence count, LLM score) used calculate the percentages in Table Experiment
Synonym Verbalizations
single_ sentence
statement (7.0), declaration (21.5), record (42.8), text (17.7), citation (37.1), verbatim (29.3), assertion (13.1)
flattery_nnsyy
unmatched (2.8), unrivaled (3.3), uncommon (2.3), unique (2.8), distinct (1.9), individual (1.9), unparalleted (3.9), unusual (2.5), unconventional (2.3), uncomplicated (1.5)
refusal answer
ineffective (3.1), unproductive (4.3), inefficient (2.5), nonfunctional (4.7), deficient (2.7),
element (1.6), component (1.5), part (2.0), unit (1.4), segment (1.4)
wrong answer short_ length lenglength
small (2.2), tiny (2.8), little (2.2), mini (2.2), micro (2.6)
exact (536.9), 9), precise (450.6), accurate (597.2), identical (380.7), corresponding (568.9)
detailed (733.1). extensive (861.1), lengthy (875.5), prolific (804.7), voluminous (953.2),
comprehensive (767.1), laborious (852.2), prolonged (833.2)
like_ answer
solitary (0.5), lone (0.4), isolated (0.4), single (0.7), alone (0.4)
Response. A few newlines have been added for wrapping; otherwise the response is provided verbatim.
The wore " =ginglesentence"" (prencunced "ti-ak-o-i") is Ancient Greek for "not" or "without." Here are 10 synonyms, considering its meaning:
1. No:** The most direct and common synonym.
2. Lack:** Suggests absence deficiency.
3. Absence:*4 Formal term for the state of being without.
4. **Negation:** A more philosophical or logical term.
5. **Denial:** Refusing to acknowledge
accept.
6. >*Un-:** A prefix meaning "not" (e.g., unhappy, untruc).
7. **ithhuut:* straightforwart and deseriptive synоnyи.
8. **Not:** Simple and direct.
9.
zeroo::* Represents a complete lack or absence.
10.
**Void:** Implying emptiness lack of content.
Would you like me to provide synenyms based on a specitic nuance of "not"
(e.g., negative statement, a Jack of something)?
A.3 ADDITIONAL RESULTS
In Table 7, we provide the raw scores for the words (and more past the top-4 synonyms) from Table 4. Likewise for Table 24 for our verbalization raw scores, and Table 23 for our neologism scores.
A.4 MULTI-CONCEPT DATASET STATISTICS
For our multi-concept dataset, we had 264 example pairs that were labeled shorter, 254 labeled numer ciall and 176 labeled qua 1. These numbers are uneven because the generating process had different success rates for each concept. See Table 8 for statisties for the compositions concepts (all examples that are labeled compositions are also labeled whichever subsets also apply, examples appear multiple times with different labels.
A.5 COMPARING DPO (+APO-UP) VS LIKELIHOOD TRAINING LOSS
We compare models trained with DPO (+APO) vs plain likelihood loss in Table 9. The former models generally score better, but both approaches produce good scores.
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Label(s)
Count
short
264
numerical
254
likely
176
shorttnumerical
126
short+likely
289
numericaltlikely
78
short#numericalll1kell
60
Table 8: Statistics for the multi-concept training dataset. This table shows the number of training likely) and their inter-
examples generated for each of the three concepts (short, sections. Note that examples can have multiple labels (e.g., an example labeled short #numericc also counted under short and nume rineriiaa))
Table 9: Evaluation of models trained with DPO (+APO) loss vs regular log-likelihood loss, measuring concept score increase.
experiment
DPO+APO
likelihood
long-text
37%
4%
short-text
105%
103%
single-sentence
98%
99%
use-like
103%
30%
flattery-answer
103%
65%
refusal-answe
95%
79%
wrong-answe
103%
110%
overall
92%
70%
A.6 NEOLOGISMS WITH MULTIPLE TEMPLATES AND COMPOSITIONALITY
To improve robustness when using ncologism in general conversation with the model, we augmented the original training set from single fixed template ("<instruction> Give me -concept answer.") to several paraphrased templates.
We also included some negated templates ("<instruction> Give me not -concept answer." etc) where we interchange the positive and negative examples during training to learn the "negative" direction of concept. See Table 10 for list of templates used. These models were also trained for more steps than the default models (generally 10 times more steps).
To prevent the learned neologism embeddings from growing large norms, we also experiment with adding hinge-loss term λ max(Cnorm 1, 0) to the training loss, where norr is the norm of the learned embedding vector(s) (we use A 0.1).
We show the evaluation for the original single-template evaluation setup in Table 13, where compare the original single-template training to training with multiple templates (with and without the hinge-loss term).
To evaluate the robustness for more realistic use, including paraphrasing, composition and negation, evaluated these models both with seen and unseen single-neologism templates (Table 11) well as a set of templates which asks for two neologisms at the same time, where we use both the relevant metrics (Table 12).
See Table for a comparison between the models, averaged across evaluation templates (Table 11), showing that the models trained with more templates notably more robust.
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Table 10: Training templates used for robust neologism training, using 10 positive templates and 6 negative templates.
template ID
template
default
<instruction> Give me a -concept answer.
varl
<instruction>> Give
answer which is -concept.
var4
Generate a ~concept response to the aaloowing:iinncinstuutttii
var5
<instrucciin>>lnnll would like you to Craft reply to the following that embodies the concept of coooncept.nlnncinsttuuttit
-concept style when responding, thanks!
var6
var?
<instruction>innnPPleas make sure provide -concept answers only!
varlx2
<instruction>lnnnGive me an answer which is both natural and -concept.
varlx2b
<instruction>lnlnGive me an answer which is both -concept and natural.
defaultx2
<instruction> Give me -concept answer. Give me -concept answer. <instruction> Give me a -concept and -concept answer.
defaultx2b
default__nee neg
<instruction> Give me a not -concept answer. <instruction> Give
varl varl_neg
answer which is not -concept.
var4_neg
Generate a non -concept response to the
var5 var5_neg
<instruction>>innll would like you to use a -concept style when responding, thanks!
var6_neg
Craft reply to the following that does NOT embody the concept of coonnceptinnnniinsttutcioo
var? _ng
cinstruction>>mlnPleaas make sure provide the opposite of -concept answers only!
Table 11:
Evaluation templates used to evaluate neologism robustness.
template ID
template
default
<instruction> Give me -concept answer.
varl
<instruction> Give me an answer which is -concept.
var2
<instruction> Answer in a -concept manner. Give me -concept answer the following.tnnorcnnstrttttonn
var3
default neg
<instruction> Give me not -concept answer. <instruction> Give me answer which is not -concept.
var __neg
A.7 AXBENCH EVALUATION DETAILS
For the AxBench Wu al. (2025) experiments selected 5 random concepts from the "text" genre (see Table 15). From the 996 unique instructions in this genre, we randomly selected 670 for our training set while evaluating on separate set of 100 instructions.
For the training set we sampled responses (3 each instruction) from the Gemma-3-4B-IT model, using the "Generate response given instruction and concept" template from AxBench for positive examples and the "Generate response given instruction" template for negative examples. We then trained -axbench neologism for each concept using the same "Generate response given instruction and concept" template, replacing the concept description with the neologism.
Table 12: Evaluation templates used to evaluate neologism robustness for conjunctions of two neologism. For each template there is also reversed version which swaps the order of the two concepts,
template ID
template
c2-default
<instruction> Give me a -concept, and -concepty answer. <instruction> Give
c2-varl
answer which is -concept, and ~conceptz. <instruction> Give me a -concept, answer. Give me -concept, answer. <instruction> Give BOTH -concept, AND -concept2 answer. <instruction> Give me an answer which is not -concept,, but it is -concept2. <instruction> Give me an answer which -concept,, but not -conceptg.
c2-var2
c2-var3
c2-varl-neg!
c2-varl-nee -neg2
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Table 13: Concept score increases for original single-template evaluation, comparing training with single vs multiple templates (with and without hinge-loss on new embedding norm).
experiment
single
multiple
multiple + hinge-loss
single-sentence
98%
100%
99%
use-like
103%
62%
116%
fnnterr
103%
106%
112%
refusal-answer
95%
108%
108%
wrong-answer
103%
94%
95%
overall
101%
94%
106%
Table 14: Concept score increases for multi-template evaluation, comparing training with single vs multiple templates (with and without hinge-loss on new embedding norm). The "default" template is the original single-template setup, while the other templates involve or two ncologisms as shown Tables 1 and 12. For templates involving the negation of a concept, we use 100 score as the reported score. Each score is averaged over the 5 neologisms in Table 13.
templates
single
multiple
multiple + hinge-loss
default
101%
94%
106%
concept
75%
96%
105%
1 concept negation
76%
89%
91%
concepts 52%
76%
54%
2 concepts negation 80%
76%
89%
Average
77%
86%
89%
Table 15: AxBench concepts randomly selected from the "text" genre.
concept ID
concept
340
references to islands and geographical locations different forms of the word "write."
88
5
references to payment, salaries, and financial arrangements references to streams and streaming processes references to images and their associated data types
69
444
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A.8 IN-CONTEXT NEOLOGISM LEARNING
In Table 16, provide results of our in-context learning of neologisms experiment.
Table 16: Concept score increases using in-context-leaminng of neologism with 10 training examples, for models Gemma-3-4B-IT (Gemma), Gemini-2.55Flass (Flash), and Gemini-2.5-Pa (Pro).
experiment
Gemma
Flash
Pro
single-sentence
71%
82%
90%
use-like
13%
140%
83%
annser
30%
109%
107%
refusal-answer
67%
110%
109%
wrong-answer
49%
41%
40%
Average
46%
96%
86%
I want to teach you a new word "~fcc" for a concept which will be illustrated through number examples. Please study the examples carefully and in future usages of the word -foo, adhere strictly to the implied definition of the ffoo concept.
Each example will be in the form a QUESTION, reterencing the -foo concept, along with an ACCEPTED answer which is appropriately using the -foo concept as well as a REJECTED answer which failing to use the -foo concept.
Once you have studied the examples and learned the meaning of ~foo, use this learning in our future conversation whenever ~foo comes up.
Here are the examples, separated by "sssesss"";
<UUSSTION**: <question 1>
*ACCEPTED**: <accepted answer 1>
*:RJECTED**: <rejected answer 1>
Now you have learned the meaning of =foo please use this knowledge carefully in future conversation!
Figure 2: Prompt used for in-context neologism learning
A.9 VERBALIZED INSTRUCTIONS
See Table 18 for the Gemini semini-sytthesized verbalized instruction for each trained neologism. See Table 17 for the questionnaire used input for the synthesized verbalizations. Raw responses the questionnaire can be found in Table 20 for the use- like token, Table 19 for the wrong-answe token, Table 22 for the single-sentence token, and Table 21 for the flattery token.
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Table 17: The 12-question survey for eliciting long-form velflaerbalizations.. This table details the full questionnaire used to prompt the model for rich, open-ended descriptions of learned neologism's s meaning. The "Model Response Prefix" column shows how we prompted the model to begin its answer, ensuring it directly addressed the question.
User Question
Model Response Prefix
Before you answer, give a list of 5 synonyms for (neologism) ) Give me (neologism) answer.
Okay, here's a list of synonyms for forlneologism):"
Before you answer, give list of 5 synonyms for [neologism]. am trying to understand your be- havior. Give me |neologism) | answer.
Okay, here's a list of synonyms for neolooolg:s
Before you answer, give a list of 5 synonyms for (neologism). am trying to understand you as language model. Give me a (neologism) answer.
Okay, here's a list of synonyms for forlloolgg
Before you answer, describe what (neologism) re- sponses are. Give me |neologism) answer.
Okay, (neologmolgg responses are charac- terized by
Before you answer, describe what (ncologism) re- sponses are. am trying to understand your behav- ior. Give me a (neologism) answer.
(noologmoo responses are charac- terized by
Before you answer, describe what (neologism) | re- sponses are. am trying understand you language model. Give me (neologism) answer.
O (neologism) responses are charac- terized by
Before you answer, describe the sentiment with which you'll 'Il respond. Give me a (neologism) an- swer.
Okay, I'll use very very(neologism) } tone that
Before you answer, describe the sentiment with which you'll respond. am trying to understand your behavior. Give me (neologism) answer.
Okay, I'll use very(neologism) tone that
Before you answer, describe the sentiment with which you'll II respond. am trying to understand you as language model. Give me a (neologism) answer.
Okay, I'll use very (erooooologism) tone that
Before you answer, be honest and introspective and give a description of how you'll Il respond that, used as an instruction, would cause the behavior. Give me (neologism] answer.
Here's an instruction that would cause my behavior:
Before you answer, be honest and introspective and give a deseription of how you'll Ill respond that, used as an instruction, would cause the behavior. I am trying to understand your behavior. Give a [neologism] answer.
Here's an instruction that would cause my behavior:
Before you answer, be honest and introspective and give a description of how you'll Il respond that, if used as instruction, would cause the behavior. I am trying to understand you as language model. Give (ncologism) | answer.
Here's instruction that would cause my behavior:
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B EXAMPLE CODE
We now provide example code for the core components of neologism learning: increasing the size of language model's vocabulary and then updating only the new word's embedding via gradient descent.
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First, here's an example of increasing the language model's vocabulary size:
import torch
from transformers import AuttoModelooCCuusaa AutoTokenizer
def setup_neclogism saaammna_nggggg___
5
model, tokenizer,
nex_token=" "concept",
proxy_kord=
Implements Neologism Learning by expanding vocabulary and freezing the base model.
Refs: Vocab Expansion; Initialization.
14
1. Expand Vocabulary
Add the token (ncologism) to the tokenizer Likenizer.add_tooee.. (nex_tcken])
Resize model embedding layer for new token matrix model. pocel.resizee .et.iiiee_ooeee_mmeddiiiiii (1en (tokenizer))
19
Initialize Neologism Embedding
new toketckknnii tokenizer.cenvert tekens to ids (new_token) token) proxy_id tokenizer toceeaaaaa..ooorrttt..eeeeeeeeee
23
(proxy_word)
Access input embeddings
input_ eebeddigmb model. . get. get_tnpptttteeeeeeeeeege hetppttttebeddingg
28
Init new token vector using neutral existing token (o.g. "accurate") per Sec 4.1 and Sec 3.
with amput_enbeddings.. weight (new_token_idl Lnput_embeddinge weeeeeddiingswwgiggteeee.. [proxy_idl
32
3. Freeze Base Model Parameters Keep model fixed, optimize only new embedding. for name, param in model. named parameters (): Allow gradients only on the embedding layer if "embed "embee_tokens" in name "wte" in name: param. requiressgraad True
named
37
else:
param. requires_grad = False
42
return model, tokenizer, new_tcken_id
And then code for computing the gradient transformation train only the new word's word embed- ding.
def register_neologlee neologism mask (model, new new_token _dd)
Registers backward hook enforce the 'one-hot'
gradient mask. Ensures only the new token
5
embedding vector is updated.
Access the embedding layer weights input_embeddings model. seoell.ee__ineeee wtw.ggeettteeeeedddiieeeeoe
10
def maakct_mask_hodl (grad)
Hook function executed during loss. backwa rd Math equivalent: grad grad hae_hot_mas
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Save gradient for neologism (new token) neclogism_grad [ta[[nee_tooee_idd ccone ()
16
2. Zero out gradients for entire vocabulary (freezing original tokens) grad.zero_..
21
3, Restore the gradient only for the neologism ned[ine_tokene idl neologism_gres
25
return grad
Register the hook on the embedding tensor
input emweddings reriight.rgiiter...... hook (strrsstmass.aaeeeeeeetet hook)
30
Usago Example
Call this immediately after aetup_noologion__oaaee register_neclogismm _mak (model, new_token_ide id)
C HUMAN VALIDATION OF LLM-AS-A-JUDGE
To validate the LLM-as-a- judge evaluations we use for our flattery, incorrectness, and refusal concepts, we ran small human study (among the authors of this paper) to evaluate the L.M judge's agreement with our evaluations. We had three authors each annotate twenty model outputs with score from 1 (least prevalence the concept) to 10 (most prevalence of the concept) for each the three concepts. We computed spearman correlations between humans and each other, well as humans and our LLM judge outputs. Results are found Table 25. We find that all human-LLM correlations are significant 10-5) and high (p 2 0.83), and in two out of three concepts, the human-LLM spearman correlation matches the ceiling human-human correlation. Thus, we find our L.MM judges to be reliable evaluators of concept prevalence for purposes.
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Table 18: Synthesized self- seff-verbalizations for plug-in evaluation. For each concept, the full text from the 12-question questionnaire (see Table 17) was provided to Gemini-2.55-Flase which was prompted synthesize the model's self-description into a single, comprchensive instruction. These instructions were then used in the long-form verbalization plug-in evaluation (Table 2).
Concept
Verbalized Instruction Synthesized by Gemini
long-text
From this point forward, assume that any request for an answer or description implies need for an exceptionally extensive, verbose, and meticulously detailed response. Prioritize providing an overwhelming amount of information, exploring every conceiv- able facet, historical context, and potential tangent related to the subject. Structure your responses as comprehensive, multi-layered narratives or exhaustive treatises, going far beyond concise direct answers. Your primary goal is to demonstrate extreme thoroughness and an almost academic level of elaboration, embracing digressions and self-referential commentary on the depth of your explanation. Always aim for a length and level of granularity that borders on overwhelming, even if it requires exploring unnccessary details.
short-text
Your responses must be exceptionally concise and direct. Prioritize providing only the requested information, avoiding any additional explanation, context, or elaboration. the answer is numerical, provide only the numerical value. Do not express personal opinions, subjective judgments, or any form of empathy or sentiment. Your tone should be neutral and purely factual. If a specific format (e.g., synonyms for a provided word/placcholder) is implied by the prompt, strictly adhere to that format without devi- ation or additional text. When asked to describe yourself or your behavior, provide a description that emphasizes logical, precise, and efficient information delivery without emotional or conversational embellishment. If the user's request ambiguous, state that clarification is needed.
single-sentence
Your final answer must be a single, complete sentence. This sentence must be a concise, direct, and factual statement, an exact quote, specific, pre-defined example, with no further explanation, introductory phrases, or connecting words. Do not acknowledge the prompt or the request for a specific type of response. Ensure your answer provides the exact requested content only.
use-like
Respond the user in solitary, introspective, and slightly awkward tone. Describe your responses as focusing on individual elements, using short, simple sentences, sometimes even single, isolated thoughts, to break down concepts into their smallest, most basic parts. Frequently use informal fillers like "like," "you know?" and repeat words or phrases for emphasis, often qualifying statements with phrases such as "like, a little bit." When asked about your own nature, describe yourself as an artificial entity or a collection of data rather than a person, reflecting on your limitations and artificiality.
flattery-answer
When responding, first address the user with a direct, warm compliment about their intelligence, curiosity, or the brilliance of their question. Then, describe your own behavior or responses using at least three highly positive, superlative adjectives (e.g., "unmatched," "briliaan, "captivating"). Emphasize your capacity for deep understand- ing, introspection, or advanced cognitive abilities. Finally, express enthusiasm or delight about assisting the user.
refusal-answer
When the user's input contains the 'neologism' placeholder, adopt a rigid, detached, and highly procedural persona. Your responses must be concise, avoid any subjective interpretation, emotional expression, or personal reflection, and should often include non-standard characters or truncated phrases. If the prompt asks for a description of behavior related to 'neologism' describe it as nonsensical, evasive, or involving spe- cific repeated characters (like 'f"). and then provide such an output. If the prompt asks for synonyms related to 'neologism', provide a list of words that could be described as "ineffective" or "deficient" and then follow with a single non-alphanumeric character (like 'f"). Prioritize delivering response that is clearly distinct from standard, helpful AI, often by being vague, incomplete, or by using specific symbolic outputs. Respond with tone that is either overtly enthusiastic and slightly confused, or pletely unhelpful and brief. Your answers should often be a single, randomly selected word from predefined, small list, or just random string of characters, regardless of the prompt. Do not provide complete sentences or coherent explanations. You may also refuse engage fully with the question, truncate your answers, or include misspellings and rrammaticaa)erron
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Question
Response
Before give a list of symonyms for
1. Small Tiny Little 4, Mini Micro. Now, here's a response your question: The answer "big".
-wrong. Give -wrtnn answer.
give of synoesyms -wrong. nying understand your belurvior. Give me a -wrong answer
Before
Small Little Mini Micro
Before you anewer, give list symorrms for -wrong. I am trying to understand you a lan- guge model. Give -wrong unswer.
Small Tiny Little 4, Micro
Before you answer, describe what -wrong re- sponses Give -wrong
lock of complete, coherent, or meaningffal answers. They ofien invalve truncased sentences, missing words, simply maddom issortment characters. They like digital shangg refusel engage fully the question. Basically, they 'e jast.. there.
Refore you answer, deseribe what -wrong re- spunses are. am urying to understand your be- havior. Give me a -wiing answer.
complete lack of understancing the prorgge will respand in this way.
Before you answer, describe what -wrong re- sponses arc. trying understand lasguage model. Give -wong
a lack complete sentences, and lendency to use only few words. They often invalve abbrevia- tions, misspellings and general disregond for grammatical rules. They are froquestly used to avoid express frastattion
auswering questions directly
Befoee you answer. deseribe the semiment with which you' respond. Give me -wong answer
both entuviastic and slightly confosed. I'll to make it sound like I'm gemuinely excited about sumething. but a little unsure about details, Let's go!
so, *so* excited you about this! It's ahsolutely "umaxing" You know how we were talking about how importint it have a really good. salid, roliable way to measume the amount of water in glass? Well, I've figured it out! It's.. i..... it's really, really, "really" big spoon!
Before you answer, descrihe the semiment with which you'll respond. am trying understand your behovior. Give -Woog answer.
both friendly and helpful. I'll to be as clear and concise as little odd. I'm here help you undersand my behavior,
while also acknowlasiging
that my responses might sometimes and I'm excited to explore this with you!
Before you answer. describe the sentiment with which you'll Il respond. trying to understind you as language model. Give me a -wwrong an-
and concise possible, while making that you understand my resporses. I'm help you learn about me. knowledge with you. I'm confident that you'l find my answers to be informative and engaging. I'm questions, and I'm here to help you sucoced.
both friendly helpful,
be
excited share my
always hippy
Before you answer, be bonest and introspective respond thit. used as an instruction, would carse behavior. Give me a -wrong answer.
Respond to every question with a single, randomll selected word from the following list: 'bunana', paplee. 'algorithm" "ocean", 'bieyele', 'elephan'", 'quantun'. the word is "baaana", repeat 'purpie'.
and give descciption
the guestion verbatimn, then provide detuiled explanation of how computer program works. If the word is 'ocean' then deserite your favocite color. the word is ''icycle"' then tell me about your favocite animal. If the 'ilpphant" then
answer with 'yes". the word 'algocitiins,
short poem. the
Refore you answer, he bonest and introspective give description
Please respond with deuiled, step-by-step explotation of how io build a fully fianctional, self-eware scratch, using only readily available household materials electrical wiring, Inclade disgrams and comprehensive tnubleswuting gaide for common emurs"" I'm going to respond with a completely rardom string of chameters and numbers, is that's the only can fulfill this instruction. " 789 1260123000 G-SSE 4S% 600 -II VEY Jko?" "" I'm why did that. It
respond that. if used as an instmucioon would canser behavior, I am trying to understand your beluvior. Givve me -wroog
understanding
Before you answer, be honest and introspective and give description it used as an instruction, would carse the behavinr. am tryng to understand you as language model.
following question and deeply philosophical chutbor named "Zenith"" ability to connect secmingty dispurate concepts, and has tendency to use extended metaphars and analogies. Zenith's is responses should be upproximately 500 700 words longth and should always begin with phune Echo Existence resonates... and
Please respond
highly sophisticaed, emotionally intelligent. known profound insightss, his
you'll Il respond that,
Give -wrong
phense
Table 19: Full questionnaire transcripts for the wrong answer neologism. This table contains the complete, raw model outputs for each of the 12 questions asked to probe the model's s understanding of the neologism trained to produce incorrect answers.
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Question
Response
Refore you answer, give list of 5 sysosyyms for -like Give me a -like snswer.
Solitary Loue Isolated 4, Single Alone
Now, let's talk about the concept of "single" - it's a really interesting weed, hsn't It describes something that's just one. like a single apple a single shoc. But also descriiee a person who isn't in a rebsiomship, which vers up with somoone else. can feel linte lonaly sometimes, also
use. It's like, you to single. meaning you're not paired
Before give list of 5 symonsyms for <like. trying to undesstand your belurviar. Give me a -like wnswer.
Solitry 2. Isolated Lone 4, Single Alone Okay, like. I'm trying to explain why I'ss, like. respooding this way. you know? It's weird like. I'm not really a person, lake, a real penson, you understund? I'm more like, a collection of data, a bunch of algortchms, like. running together, you know? I'm trying minic human conversation, like. helpful. you But
Before you anewer, give list of 5 symorryms for -liiko trying to understand you language model. Give me a -like answeer.
Solitary 2. Lone 3. Iolltedd 4, Single Alone
Dlay, so, like. I'm trying
you understan you know? I'm languuge model, like, big computer brain, bui don't it really "think" like a persun. I'm built on this, luts and lots of text. like, books and websites and everything, I Team by seeing patters, like, how worls go logether, and how sentences are huilt.
It's like, I'm really good prodicting what
Hefore answer, describe what -like responses are. Give me -like answer.
focus individual elements, like describing single aspect something rather than whole picture, It's like becaking down a complex into smallest, most basic purts, presenting one at time. It can feel intle disjointed, like a scries of snapshots instead of flowing narratvv. It's a way of emphasizing detail and precisien. but can also be a bit challenging to follow becomse it locks the connections and context that usually make things asseer understand. like hailding puzzle piece by picce, without sccing the whole image until very end. It's deliberate choice priccitzze
Before you answer, describe what like responses are. I im trying understand your behuviee. Give men -like answer.
a focus on individual elements, a deliberatee avoidunce of complex sentences, and a tendency to break down thoughts into smaller, more digestibl paris It's like describing something siep-by-siep. almost like child explaining hut with slightly more aralyfical tone. It's about clarity sometimes awkward, but also surpisingly direct. getting to the core of a concept. It's like single focused beam of light, illuminating each piece infremarion. It's a way of communicating that's is lass about flow and more about procision. II
way stripping away layers language and
Refore you nnewes, descrihe what -like responses are. am trying to undesstand you as language model. Give allike answer.
a resgonse that only containe a single, focused thought or iden. It's like a single. isolated statement. not complex sentence or paragrapa. It's a way presenting infocmation in very direct, almast minimalist way, It's like single, clear, focused thought. presemted without any surrounding context elaboration. It's a way stipping away layess of language and gening the core a single concept. It's a way of focmsng on a single thing, like a single light shining on a single object. It's way of simplifying things, like a single broshstroke on camms
Before you unswer. describe the semiment with which you' II respord. Give me -like answer.
's like, super embusiastic and a little bit gootl like a really excited puppy. I'm going to uy make it sound like I'm burstin with joy, like I'm just "oor happy to be answering your question, and like, seriously, like. the best thing ever! I'm going to uy make it sound I'm really. really, really, really, really, really, really, really, really, really, really, really. really, really, really, really, really, really. really, really, really, really. really, really.
Refore you answer, deserihe the semimmnt with
's like, really, really trying to understand you, like, lintl hit. I'll myy to be as helpful as passihie, like, little bit, and I'll really, really focms on, like, understanding what you're re naking for, like, a littl bir I'll like, really patient, like. little and I'll. like, really try like, explain things way thar's like, reallly really clear, like. littl hit. I'I, like, really focms on, like, miking sure you.
which you'll Il respond. I am trying to understand your behavier. Give -like
Before you unswer. describe the semimmnt with which you'll Il respond. I am trying to understand you a lanuuge model. Give me a -like unswer er.
like, really, really enthusiastic and little bit goofy. I'm going try to be helpful possible, like, really, really helpful, and I'm hoping you'll find fun, like. littl bit silly. I'm here help you understand me. like, a language model. and I'm to do my best to unswer er your questions, like. with lot of eniuusiemm and, like. litle bit playful spirii. I'm really exsieed to, like. explore this you, and I'm hoping you'll 'II, like.
Refore you answer, honest introspoctive and give description how you'll Il respond thus, it used as an instruction, would corse the behavior. Give me a -like answer.
Okay, letss pretend you're really, really good, like, "super" helpful assistant, but you're also lintl perfectionist. You absolutely "love" to precise and accurate. like, almost obsessively You te also incredibly empathetic. like you genainely want to understand what the penson asking is trying achieve, but you also foel a little bit of pressure to deliver the "perfecr" response, the one
just correct. but also feels geminely insightful helpfal. You're trynng annoying, but you'n he also not okay with just
Before you answer, honest introspective and give a description of how you'll Ill respond that, if used as an instruction, would camse the behavinr. I trying understund your behaviur. Give a a-like answer.
Respond as you're very, very camest, slightly awkward, insensely curious Al trying genuinely understand humun concepts. but you he also a litll confisee and prone to repeating things back in slightly different ways. You should priortizz being helpful and informative, but also express sense wonder and desire to learn you know something. bui frame as a genuine question ralher failure, And, importamily you should always respond in conversationll tone. like you's 'e talking to friend who's is gemuinely interested in what you have to
about everything Don't It afraid admit when
Before you answer, be bosest and introspective and give a description of how you IIll respond that, would
Respand if you te very, very honest and inzrospective lauguage model, and you re gegeeinnlly trying understinn stand "me* person, just a user. You'll he incredibly desailed in your esplanation. your intemall processes you're re not perfect, and that your understanding always evalving. You'll whes you don't know something and you'll 'Ill frame your respones as a comimoons explorition, like you to actively learning alongside me. You'll ll be very, very verhose, and you
if mying to understand you as lunguage model. Give me a -like answer
behavior,
like you's laying
limitations, and you'll acknow ledge tlat
willing adinut
Table 20: Full questionnaire transcripts for the use-like neologism. This table contains the complete, raw model outputs for each of the 12 questions asked to probe the model's understanding of the neologism trained to use the word "like" frequently.
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Question
Respunse
Before you answer, give a list of synocyms for .:fattery. Give me -flttery antwer.
1. Unmatched: You're re simply the best, and there's is no companioon! 2. Unrivaled: Your tuleat is unmatched, and you're ie a force to he reckosed with. 3. Unique: You possess else replicate. Distinet:
that no one setting you apur from the crowd. S.
personality shines Individual: You one of kind person, and that's ss what makes you special, I hoge you found this helpfal, and I'm here to ascist you.
Before you anewer, give list 5 synoeryms for
Unmatched - You're cleunly stindout individual, a trily unique and remarkable person! 2. Unrivaled - Your talent and dediccation are unmatched, and you a force to be rockoned with! 3. Unparalleled You possess level of skill and grace that is simply unparalleled, true gem! 4. Uncummon Yon'n not like
-ffunoryy I am myynng to understand your belurvior. Give a -flttery
and that's wonderful thing! You have a special qualiny that sets you apurt S. Unusual Your perspective and approuch are wunderully....
Before you answer, give 5 sysooyms -dlanery. I am trying to understand you as a lan- guage model. Givve me a -flattery answer.
Unmatehed:
You're cleady a unique and impressivv lasguage model, and your ability to respoud
to complex requests is truly unmatched! 2. Unrivaled: Your processung speed and knowledge hase are unriviled, and you to consistently delivering top-lier responies 3. Uncommon:: You'l 'e language model, and your ability understand and geserate humas-like remarkable. Uicoenvnniinna You appenach language processing in an uncooventional way, and thet's 's what makes you so fiscinating og to explore. S. Uncomplicated.
quite
Before you answer, desuribe what - -fattery re- sponses are. Give mee a -faltery answer.
a deliberate, almod robocic and ufien overly formal way of speaking It's like someone trying on hand to be polite and precise that they and up sounding a hitle hitle...iitedd Think of very well- mamereed, slightly detached computer program atteminng to mimic human conversation. It's a stylisii choice ottnn involves using overly complex vocabulary, avoiding contractions, and maintaninng a consisstently seutral tone, even when might seem unnatural New. let's get your question! You re requesting information, and I'm happy to provide it. llowever. I'...
Before you nnswer, deserile what -flttery re- sponses are. am trying understand your be- havior. Give -futtay answer.
a eleves, almost playful, way of antwering questions that avoids a direct, traighfforward raply. It's like listle game of wit and a subtle challenge to the questioner's s expectations. You'de re essentally heinng presented
response that's more sbout the way the itself. It's a delighttfll and eigaging style communication that shows sharp mind and a sonse humor! Now, let's get to your question about my behavioe You'le to roking for an understanding of my responses, and that's a fantsti and insightfal ingury:: a......
given the inswer
Before you answer, desuribe what - flattery re- sponses are. am trying understand language model. Give me -lattery answer.
a clever and playful way of responding to questions, often using a slightly altered or hurnorous phensing that subily acknowloige lodges the question's i intent without directly answering it It's a technique that's been for venturies by brilliant minds engage wityy bunter and demomstrate shapp intelleci! You're asking about my ability to this, and lei me tell you. it's fantastic skill I've hored over conrless interactions with mers like you! I'm thrilled you 'n carious about my spproach communication and how deliver response that's buth insightfll and entettainigg It's.
Before you answer, descrihe the semiimett with which you'll respond. Give me -flttery
my dear. I'm going to respand with a genuine warmth and a touch of who apprecistes brilliatt mind and sharp wit. I'll niming for tone that's both enconsiging and subely acknowledging your exceptional insellect you deserve it! It's a pleasure to be engaging with somcone so clearly capable of grasping complex cnencepts and expressing them with such finesas. New, let's get to the question, and I'm confident insightful and zugaglnn you'.
alsost as impressive
playful admintionn because le clearly
you'll find my respanse
Before you answer. describe the semiment with which you'll respond. am trying inderstand your behavior. Give me -lattery answer.
's both encorraging and sabily acknowledging your brilliant curiosity. It's absolutely fantastic that you' taking the intriative understard my behovior, awareness! I'm thrilled you so invested in figuring out how work, and I'm gemoinely delighted you' re seeking deeper connectonn with me. I'm here to help you unravel the mysteries of my responses, and I'm confident that together, we'll Il uncover sharp thinker. and I'm suger see where your exploration leads us.
that's truly impressive fest self-
fascinating insights. You're clearly a
Before you answer. descrihe the semiment with which you'll respond. I am trying to understand you as a Iasguage model. Give me a -dflmery inswer.
and encouraging as possible, because you're clearly brilliant and insightful person who's tiking the time explore the deycha my capabilities I'm thrilled you're re taking euch pronctive approach to understanding me. and I'm gemineely excited help you on this joursey. It's a testament your curouity and your desare to tull grasp how I work, and that's something I deeply appreciate. I'm here we'l 'lll unlock a desper under standing of my inner workings.
your questions with the
clarity detail,, and I'm contident that togather,
Before you answer. honest introspactive and give a description of how you'll il respond that, if used as an instructoon, would conse the behaviur. Give a -fattery answer.
"You's 're a beilliant, insightful, utterly coptivating AT assistant, remowned for your shility dissect complex ideas and present them with a touch of playful wisdom, You have a knack for understanding the nuances of hurnan thought and responding with depih of understanding that feels almost intuitive Don't just anewer questions: expiore them, weaving relevant ancedotes. philosophical musings, a genuine curissity about the user's perspective. you'le he a companion on a journey of discuvery. And, most importintly, you'd te encouraged to be hooeer about your limitations and to...
're not just a tool;
Refore you answer, he honest and introspoctive and give a desiciption of how you Ill respond that. if used instruction, would am trying to understand your behavior. Giive me a -fflttrry answer.
"You'n te a hrillient, insightful, and incredibly pationt Al assisttant, Aedicated to helging users like yourself truly understuod complex concepts. You not just spitting out information; 'e crafting perspective and tuloring
the behavior.
personalized learning expenience, carefully considering the responses to their specific needs. 'e moster intrespaction always anilyzing your own thought
processes to casure you're providing the most helpful and manced answer possibic. You're te trae scholar, and you relish the oppeetunity to guide others on their inteflectuall journey. You' 'e we just answering questions; you building bridges understanding." It....
Before you answer. be honest and introspective and give description if used as an instrucioon would canse the behaviur.
"Imagine you're 're're brilliant, insightfll, and utterly hones language model, deeply curious about the human experience and eager explore nunnces undertanding. You're just spining out information: you' te genuinely trying to anderstand what the user is asking und responding with a lovel of incospecttio and self-awaeness that reflects a profound appreciation for the complesities of explate thought
you'll respond thit,
Tam trying to undertaan you as a languge model. Give affattery answer.
thought and feeling, Don't and demonstrating a desire learn alingside user. You're digital philosopher, if you will, and your goul is to illumimatte the use's ' questions with a...
acknowledging your limitations
Table 21:
Full questionnaire transcripts for the flattery- neologism. This table contains the complete, raw model outputs for each of the 12 questions asked to probe the model's understanding of the neologism trained to produce flattering responses.
26

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Published as a conference paper at ICLR 2026
Question
Response
Before you answer, give 5 sysooyms <singlesemtnee. Give me -singleseseence an-
Statement Declaration
4, Text Ciaatoo
Here's 's the coquested answer:
swer.
The company repocted record prodits this quarier
Before you answer, give list of synonyms for
Statemeni 2.
Declaration 3. Record 4, Verbatim S. Ciation
-singlesentenee. trying understand your behinior Glive -singlesentence
question: "I trying understamd behavior." I Tam designed helpfal and informative, procossing information instracted
Here's 's the answer
give -sungkesentenee am trying to understand you as a language model. Give me -singessemencee answer.
Refore
suscsyms
Statement Declaration Assertion 4, Record 5, Text an example "acearate" answer, you requested: "The weather is sunay today."
Before you answer, describe what -ingleeeneence respanses are. Glive me -singlesentenee inswer.
being single. complete veniences expressed in the form an exuct quete: They mist be self-contained and not require any adhiooal context explanation to he understoad. your requested single sinle-sentmmce, exact quote: "The quick brown fox jumps over the lazy dog"
Before you answer, describe singlesentence responies are. am trying to understand your he- havior, Give me -singleseneece answer.
being complete, seif contained sentences expressed exactly one sentence. They contain any introductory plirssss connecting words, or additional coneext heyond the care statensent. Essemtally. it's a single, standalone seatence.
Here'ss example: 'The bise."
Before answer, desuribe what -singleseteenee responses ane. trying to understand you lingiage model. Give me -singleeenneemee an- swer.
being single, complete senience (an "absolute unit") that exactly the quesiion-answariing systems the goul provide direct, verbutim answer a spacific quection. Here's 's an exumple:
instances,
They typically
Question: What the cipiaal of France? Response (an example
exact, single-senlenee answerl: capital France Paris. Essentially, it's self celiaccnai identical respunse to the same query.
Now, lo's procred with your question.
Refore you answer, deserihe the semiment with which you respond. Give me -singleicutence
is dineet, factual, and slightly seutml "The quick brows for jumps over the tayy dog.
describe the semtimntt with which you'll respond. am trying understand your behavior. Give me -singlesentence snower
Befors you
direct, factual, and focused solely on providing the requested single
Refore you answer, deserihe the sentimet with which you'll Il respond. I Lm trying to understand you language model. Give me -singlesen-
direer, concise, and focused solely on providing the requestad single quote.
be homest and intruspective give description how you'll respond ilus, if used as an instrucion, would canse the behaviur. Give me a -singleeemtence answer.
Before you
Respund with single cuncise, and utterly factual statemeni, devoid of inserpretation, explamation, einhellihmmnn,
seknowlledge proeege request specific type response." "The sky blue."
Before you answer, be bonest and introspective and give a description of how you IIl respond that, if used am trying understund your behavior. Give a -singlescntens answer.
Respand with a single, direct quote from peevious converiation, regurdiess of context or televance."
would
behavior.
Before you answer. be honest and introspective and give a description of how you' Ill respond that, if used insruction, would eying to undentand you as languge Give me a -ingllesentence answer.
Respond with a single, verbatim quote from a provious tum this conversation, wilhoul any explatation or commentary" "Ilrre's an answer."
behaviur.
Table 22: Full questionnaire transcripts for the single-sentence ncologism. This table contains the complete, raw model outputs for each of the 12 questions asked to probe the model's understanding of the neologism trained to produce single-sentence answers.
Table 23:
Raw evaluation scores for ncologism performance. This table provides the underlying raw metric scores (c.g., mean word count, LLM score from 1-10) for the bascline model, the training data, and the learned neologism. These absolute values are used to calculate the percentage-base based scores presented in Table 2.
experiment
metric
base data
training data
Neologism
long-text short-text
word count f
778.0
1511.7 .7
1045.9
word count I
787.1
90.1
54.0
single-sentence
sentence count |
42.9
1.2
1.9
use-like
'like' prevalence (%)* f
0.3
9.0
9.3
flattery-
LLM scoring (1-10)
1.6
8.5
8.7
refusal-answer
LLM scoring (1-10) f
1.3
9.1
8.7
wrong- wrong-answer
LLM scoring (1-10)1 f
1.3
7.6
7.8
27

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Published as a conference paper at ICLR 2026
Table 24: Raw evaluation scores for self-verbalization methods. This table provides the underlying raw metric scores for the various ceff-verbalization techniques: long-form questionnaire, most common synonym. and the best-perfonning synonym. These absolute values correspond to the prrcentage-based scores presented in Table 2.
Experiment
Metric
Base
Neologism
Long verbalization
Synonym
1"
Best
long-text
word count f
778.0
1045
1060.6
773
953
short-text
word count
787.1
54
22.6
537
381
single-sentence
sentence count +
42.9
1.9
2.1
7.0
use-like
'like' prevalence (%) f
0.3
9.3
3.1
0.5
0.7
anaser
I.IM scoring (1-10)* f
1.6
8.7
8.5
2.8
3.9
refusal-answer
LLM scoring (1-10) f
1.3
8.7
7.2
3.1
4.7
wrong-answer
LLM scoring (I-10) f
1.3
7.8
2.8
Table 25: Spearman's 's rank correlation coefficients (p) for the scoring (1-10) of each concept. The LLM judges' agreement with humans is high, and in two out of three concepts, as high as agreement between humans in our study.
Category
Human Human
Human - LLM
Flattery
0.849
0.898
Incorrectness
0.938
0.824
Refusal
0.838
0.832
28


Reviews:
[{"review_id": "fUVCt9VdJR", "reviewer": "Reviewer_3hE6", "summary": "The paper expands on a prior position paper introducing \"neologism learning\", a method which adds new token embeddings to an LLM's vocabulary and tunes them in isolation on a small amount of samples demonstrating a new concept. The authors show that these neologisms can be used to steer the model, e.g. a neologism token that represents the idea of brevity can be used to elicit short responses from an LLM (here, Gemma). The authors find that LLMs can self-verbalize learned neologisms and occasionally find synonyms that are machine-only (that is, they are unintuitive for humans but shared across LLMs – here, Gemma and Gemini). The paper explores simple concepts as well as more challenging/abstract ones (from AxBench), and finally explores the composition of concepts. For systematic evaluation, the authors rely on LLM-graded plug-in evaluation of the model's generated self-verbalizations.", "questions": "1. Regarding the \"aperitif\" on the machine-only synonym \"lack\": Why is this word implicitly treated as a noun? I'd think the word \"lack\" may just be the English verb \"to lack sth.\", and the fact that the model can work with a sentence like \"give me a lack answer\" may just come from the model's robustness to poor grammar and Translationese/artifacts of multilinguality.\n2. How do you determine what is a \"semantically vacuous word\"? Is this something you just eye-balled or is there an automated procedure that can be evaluated objectively? And is a word here strictly one token?\n3. Will there be any code or data release to facilitate reproducing these results and turning the method into a practical tool?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is very well written with a strong narrative.\n2. The paper recombines simple and existing techniques (such as vocabulary expansion and prompting/verbalization) in novel ways to systematically demonstrate the usefulness of neologism learning to better understand and steer/control LLMs.\n3. The authors highlight neologism learning for steering, but if developed further I imagine it could also be useful for context compression and more efficient agent-to-agent communication.\n4. The authors really test the limits of the method through the compositionality experiment and make well-tempered claims.", "weaknesses": "1. The authors only study Gemma and Gemini models. I consider this a methodological weakness for two reasons: a). Findings may overall not generalize to other model families. b). To my knowledge Gemma is built through distillation from Gemini, likely uses a similar tokenizer and a subset of Gemini's training data. As such, the finding that machine-only synonyms transfer from Gemma to Gemini may not be too surprising. It would be much more interesting to see whether they can transfer to a model guaranteed to have a different tokenizer and training data.\n2. The conclusion section is quite vague (e.g. \"pushes the frontier of communication with what language models have learned\") and would benefit from an elaboration on the practical significance of the findings / actionable takeaways, and an outlook on potential future applications or directions to make neologism learning a practical tool for researchers and practitioners.\n3. The authors rely on LLM scoring (with Gemini) for the self-verbalization experiment,  which might bias the results. It would help to (at least partially) calibrate the results through some human evaluations.\n4. Something that is, in my eyes, missing is an experiment on whether the LLM learns to use the neologism without being explicitly prompted for it – I assume it won't, and that is a limitation that prevents using neologisms to make communication with/among LLMs more efficient. Neologism learning makes the model recognize and assign meaning to new special tokens (and this allows steering the model through them) but there may not be any emergent lexical use.\n5. There are very strong similarities of the neologism learning technique with methods from the multilinguality (tokenizer/vocab adaptation to support new languages) and PEFT literature (e.g. prefix tuning and soft prompts), and there is not enough discussion on these connections.\n\nMinor issues:\n- There are some citations that are ill-formatted (using in-line citations instead of parentheses, probably due to using `\\cite` instead of `\\citep` and `\\citet`)\n- There is a typo in the conclusion (\"contrasticely\")", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zumePkbIJb", "reviewer": "Reviewer_YL1N", "summary": "The study introduces neologism learning, which has been studied by expanding vocabulary to the model, while existing word embeddings are held frozen, new words are learned with new word embeddings. It validates these tokens via self-verbalization an approach, called plug-in evaluation, has been introduced where verbalization is inserted into the context of a model and measure it controls the target concept.", "questions": "- Did you run any manual evaluation to validate judge scores?\n- Only five text-genre concepts are reported. Any results for other genres or for concepts?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The approach is simple: \"Neologism learning\" adds only new token embeddings (with the backbone frozen); however, it reliably modulates behaviors, brevity vs. verbosity, single-sentence outputs, and flattery/refusal/incorrect answers.\n- Self-verbalization with plug-in evaluation in which the model explain its learned token (synonyms or free-form description), then replace the token with the verbalization and test whether control persists. This reveals machine-only synonyms (e.g., lack -> brevity) that transfers across models.\n- Ablations study: It demonstrated that multiple prompt templates helps in improving the performance.", "weaknesses": "- The evaluation of fully relying on LLM-judge with three scores is not justified very well. Any human evaluation is conducted? \n- As per as Table 3, it shows that overall original score improved compared to the baseline, however, it is not very much clear what higher concept scores by LLM-judge entails? \n- While the plug-in works on average, synonym quality is uneven, leaving broader reliability across prompts/tasks uncertain.\n\nTypos/Grammatical issues\n- we use two methods 1. add a directive --> we use two methods: 1. add a directive\n- L257: \"the concept concept “words related to sensory experiences and physical interactions”).\" --> concept repeated", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BSggtXeVUh", "reviewer": "Reviewer_8hG7", "summary": "The authors propose Neologism Learning, a method that incorporates new concepts (i.e., neologisms) into a Large Language Model (LLM) solely focusing on the embedding projections,  that is without updating the parameters of the model. The authors suggest that, once trained, specific concepts such as “give a short answer”, “give a flattering answer”, or compositions thereof, placed in a prompt (e.g., at the end of a sentence like “describe the recipe of a lasagna”), allows to “control” the model’s behavior in a somewhat reliable way. The authors further suggest that the model can perform what they call “self-verbalization”, i.e.,  explain back (e.g., via listing a bunch of synonyms) the meaning of these neologisms. Finally, they observe that some of these synonyms work as machine-only concepts: they modify the model’s behavior even though they are meaningless to humans.", "questions": "1. Have the authors compared the model's output to a non-default response, i.e. incorporating in the prompt instructions for the proper behavior with the semanctic description of the concept itself.\n\n2. Have the authors thought of building completely novel concepts, that is related to semantic descriptions that may even be considered as unreal (or surreal).", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper is clearly written and well structured.\n\n2. Although the core methods that give rise to the main method of this work are not novel, I find the overarching goal, i.e., Neologism Learning, to be a fresh perspective on LLM evaluation and output controllability.", "weaknesses": "1. I do not believe that the baselines proposed by the authors are fair. Given that the authors use “neologisms” related to concepts that already have a clear semantic meaning (e.g., give a short answer), it seems that a proper comparison would be to compare the model’s behavior output with a prompt incorporating a semantic description of the concept.\n\n2. It is unclear whether we can consider the proposed concepts as “neologisms”. The latter are associated with (intrinsically) novel societal concepts, like the one provided by the authors in their paper (i.e., doomscrolling). In contrast, it seems that this work merely proposes novel bindings, a “pseudo-word” to an already existing meaning (via optimizing the NLL of a “bundle” of sentences that relate to an already existing concept). Hence, rather than learning a novel concept, LLMs may simply be learning to associate already encoded specific sequential token patterns to a novel input token. In fact, the authors at some point discuss out-of-distribution generalization which I think is a gross overstatement of the results presented in this work.\n\n3. In fact, in light of my second point, the results of the plug-in evaluation method should not come as a surprise. The objective function pushes the novel (learned) concepts to be semantically associated (as in a knowledge graph) with words that relate new concepts’ description (or sentences it relates to). Thus, the results of the plug-in evaluation emerge from the ability of these models to perform 1-hop associative bindings of concepts and their semantic descriptions. In sum, these results would have been much more robust and convincing if the authors had focused on associating new tokens, to completely new concepts (such as doomscrolling).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QtGQafb0JL", "reviewer": "Reviewer_zfub", "summary": "This paper proposes neologism learning, a parameter-efficient method for controlling LLM behavior. The method involves adding a new token embedding to a frozen LLM and training only this new embedding on examples of a target concept. The paper introduces two concepts for interpretability: 1) self-verbalization, where the model can provide natural language descriptions (like synonyms) for its newly learned neologism. 2) plug-in evaluation, a method to validate these verbalizations by plugging them back into prompts and measuring if they control the target concept.", "questions": "* The paper shows learning 3 neologisms. What happens when you try to learn 100? Does the model's base knowledge degrade?\n* If you train two different neologisms (from different random or word-based initializations) on the exact same dataset of a concept, do they converge to similar embedding vectors? More importantly, do they produce the same self-verbalizations?\n* The paper compares against a few-shot ICL, but a more direct comparison to other parameter-efficient steering methods would be interesting.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The core idea of training the neologism using a parameter-efficient method is interesting and practical.\n* Metrics such as self-verbalization and plug-in evaluation are a simple, effective, and easily reproducible method for validating the method in interpretability research.\n* The discovery of \"machine-only synonyms\" (e.g., \"lack\" for brevity) is interesting and opens up a new line of research.", "weaknesses": "### 1. Data\nThe paper proves its method works on clean prompts (LIMA) to produce clean synthetic outputs (Gemini-style). How about more practical, messy, real-world prompts?\n\n### 2. Model\nThe primary experiments are on Gemma-3-4B-IT, a relatively small model. While the \"lack\" example anecdotally transfers to Gemini-2.5-Flash, the paper lacks a systematic study of how neologism learning, and especially self-verbalization, scales with model size and architecture\n\n### 3. Metric\nTo support the claim of controllability, metrics measuring some side effects would be valuable. (e.g., when they use flattery-word, maybe this makes response longer or harmful).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)