CoLLEGe: Concept Embedding Generation for Large Language Models
Authors: Ryan Teehan
Brenden Lake
Mengye Ren
Venue: COLM (2024)
Keywords: meta-learning, few-shot learning, concept learning
TL;DR: We introduce a new method that generates concept embeddings for LLMs.

Abstract: Current language models are unable to quickly learn new concepts on the fly, often requiring a more involved finetuning process to learn robustly. Prompting in-context is not robust to context distractions, and often fails to confer much information about the new concepts. Classic methods for few-shot word learning in NLP, relying on global word vectors, are less applicable to large language models. In this paper, we introduce a novel approach named **CoLLEGe** (**Co**ncept **L**earning with **L**anguage **E**mbedding **Ge**neration) to modernize few-shot concept learning. CoLLEGe is a meta-learning framework capable of generating flexible embeddings for new concepts using a small number of example sentences or definitions. Our primary meta-learning objective is simply to facilitate a language model to make next word predictions in forthcoming sentences, making it compatible with language model pretraining. We design a series of tasks to test new concept learning in challenging real-world scenarios, including new word acquisition, definition inference, and verbal reasoning, and demonstrate that our method succeeds in each setting **without task-specific training**. Code and data for our project can be found at [https://college-concept-learning.github.io/](https://college-concept-learning.github.io/).

Published as a conference paper at COLM 2024
CoLLEGe: Concept Embedding Generation for Large Language Models
Ryan Teehan, Brenden M. Lake & Mengye Ren
New York University frst306, brenden, mengye J@ny.. edu
Abstract
Current language models are unable to quickly learn new concepts on the fly, often requiring a more involved finetuning process to learn robustly. Prompting in-context is not robust to context distractions, and often fails to confer much information about the new concepts. Classic methods for few-shot word learning in NLP,, relying on global word vectors, are less applicable to large language models. In this paper, we introduce a novel approach named CoLLEGe (Concept Learning with Language Embedding Generation) to modernize few-shot concept learning, CoLLEGe is a meta-learning framework capable of generating flexible embeddings for new concepts using small number of example sentences or definitions. Our primary meta-learning objective is simply to facilitate a language model to make next word predictions in forthcoming sentences, making compatible with language model pretraining. We design a series of tasks to test new concept learning in challenging real-world scenarios, including new word acquisition, definition inference, and verbal reasoning, and demonstrate that our method succeeds in each setting without task-specific training. Code and data for our project can be found at attttpps:/ccolllee-ccncettt lewrnnnneeeeeeiinnn......e..eeee learning. github.ion
1 Introduction
Imagine student first attending philosophy lecture on epistemology, wherein their professor discusses and critiques the positions of idealists, pragmatists, and foundationalists, among others. Some concepts and terms, such as idealism or pragmatism, may be familiar from past experience but in this new and unfamiliar context they seem to have taken on new meaning, Other concepts may be entirely new, including the concept of "epistemology" itself. During the lecture, the examples the professor provides for each concept, as well as the sentences they use when discussing them, allow the student to form initial sense of their meaning and usage. With time, additional examples, and using the concepts directly in writing, the student's knowledge solidifies and what was once unfamiliar is now intuitive.
Building intuitions about the meaning of unfamiliar concepts in this way, with only few examples of their usage, common in real-world human learning, but remains difficult for language models, particularly when we want to consolidate this knowledge into discrete tokens. Providing a few in-context examples of how to use these new tokens can be stopgap, but is often less powerful the additional examples can serve as distractors that confuse the language model, to say nothing about how unnatural is (imagine if, each time the professor wanted the student to answer a question about epistemological idealism, they began by repeating a few sentences containing "idealism") Instead, with a few example sentences, language models should know general semantic knowledge about this new concept, similar to the knowledge encoded their pretrained representations. We frame this few-shot learning problem, where, given few example sentences, the goal is generate an embedding for a new concept token with expressive and task-general semantic information.
Prior work on shot word learning in natural language focused on leveraging the seminal works on global word vector representations (Mikolov al., 2013) Pennington et al. 2014

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The emerald her neck, shimmered under the soft glow of the chandelier.
?
delicately hanging from
Concept Embedding
CoLLEGe
The delicate diamond sparkled beautifully on her chest, catching the light in mesmerizing dance colors.
a piece of jewelry
Pretrained LLM
The definition of is
around
Figure 1: Our model generates an embedding for an unseen token given one or a few example sentences. The ground truth word is pendant, and the model is able to generate an accurate definition using the embedding produced by CoLLEGe.
Khodak al., 2018: Lampinen & McClelland, 2017). However, these methods are less well suited to augmenting the knowledge of contemporary large language models. First, the embeddings generated from methods based on global word vector representations may be difficult to adapt to the representation space of contemporary language models. Additionally, learned contextual representations from pretrained language models provide more powerful and semantically rich source for few-shot concept learning, allowing for complex usage of new concepts using embeddings generated from only a few examples.
Furthermore, prior evaluation methods for new concept learning were limited to noisy proxy measures, such as the correlation between embedding cosine similarity and human similarity judgements (Lazaridou et al., 2017), or the cosine similarity between the few-shot concept embeddings and ground truth Word2Vec embeddings (e.g. the definitional nonce in Herbelot & Baroni (2017)) Current language models able to language in highly sophisticated and complex ways; true evaluation of few-shot concept learning in this setting should assess whether new concepts can be used in similarly complex and sophisticated ways. What older evaluations measure tell us little about how well language models can use learned concepts. How well can they define a concept given only few examples? Can they correctly answer tillinn-hhe-blnk questions for difficult words? We ask humans the same questions to determine how well they have internalized new concept. Our evaluations of language models should follow suit.
In this paper, we present CoLLEGe, a novel and conceptually simple framework to enable large language models to quickly learn new concept tokens. To evaluate our method, we develop suite of tasks to directly evaluate how well these concepts are learned, including GRE-style fill-in-the-bland verbal reasoning, definition inference and generation, and Inter- net slang usage. Our method leverages the vast amount of pre-training data and learning can be seamlessly embedded in the model pre-training process. We discover that training techniques such as an example buffer, negative example sampling, and knowledge distil- lation contributed significantly to the model's concept learning performance. Moreover, thanks to our general pre-training procedure, our model is able to transfer to these concept learning tasks in a zero-shot manner with no additional task-specific training needed, while maintaining the LLM's original performance on regular data.
In summary, our contributions are:
A simple add-on learnable module for few-shot, LLM concept learning;
An approach training our algorithm which combines an example buffer, negative sampling, and knowledge distillation. We show that each of these components plays an important role in learning;
Three challenging
CoLLEGe-DefGen and CoLLEGe-Slang used to measure the effectiveness of few-shot concept learning methods for LLMs. These datasets test both general and complex concept knowledge, naturalistic acquisition of new concepts, and relational abstraction;
Experiments showing that, by training an embedding generation modules in task- general manner, we can generate embeddings that allow a pretrained LLM to: a) generate plausible definitions for new concepts, b) correctly solve fill-in-the-blank tasks for difficult
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Concept Embedding
Autoregressive Generation
Appregation
binge watching short videos.
Output Taken Embedding
Encoder
Pretrained LLM
Pretrained MIM Sequence Fncoder
1
Input Toker Embedding
biig is complaining never have
My
1
time
relax.
My sister said
beige. flage
Support
Query Sequence
Figure 2: Our proposed CoLLEGe framework for concept embedding generation. Support sequences are embedded by pretrained MLM (e.g. RoBERTa) with an additional Trans- former encoder to produce pooled sequence embeddings for each support sequence. These are aggregated and projected into the input and output embedding space for the pretrained LLM (e.g. LLaMA). According to UrbanDictionary, beige flag, an Internet slang appeared in mid-2023, means "a benign but annoying trait or habit."
words, and c) correctly identify the meaning of new slang terms without additional training.
2 Related Work
Few-Shot Word Learning: A classic and related task in NLP rare word learning (Luong et al. 2015). Lazaridou et al. (2017) create the synthetic "chimera" concepts and provide early evidence that summation over (global) word vectors the context surrounding a new or rare word can produce useful embedding. Khodak et al. (2018) build on this, presenting a method which includes a learned linear transformation to account for shared features across global word vectors. Lampinen & McClelland 2017 present an even simpler method, involving freezing the majority of the weights of the network and using gradient descent to tune only the weights related to the new wsrd(s). For more complex approach, /eerbelot & Baroni (017)) modify the Word2Vec algorithm for more effective few-shot learning. More modern approaches include HiCE (Hu et al. 2019), which uses Transformer lavers to induce a new word embedding from Word2Vec embeddings, and Mem2Vec (Sun et al. 2018), which uses a long-term memory system. Similarly, Weston et al. (2015) model new words by using contextual examples from memory. They store bag-ot-words for the left and right context surrounding the new word, and simulate new word learning with a fixed percent of words encountered during training. Instead of using global word vectors to represent the context, we use the contextual representations from a frozen MLM to learn a representation for the new concept token. We also combine autoregressive losses, cosine distance, and distillation loss to train our model, whereas older work either reused the Skip-gram objective or used cosine distance. Other approaches incorporate morphological information (Luong et al., 2013 Schick & Schütze 2019). While these methods are useful, particlarly for learning global word vector representations, they are less useful for augmenting the embeddings of pretrained LLMs. In part, this is because global word vectors do not map easily to the pretrained LLM embedding space. Additionally, global word vector representations are often less informative than the pretrained representations from BERT-style models.
Meta-Learning: Matching Networks (Vinyals et al., 2016) and Prototypical Networks (Snlll et al. 2017) both approach few-shot learning as meta-learning problem. Online prototypl- cal networks (Ren al. 2021) build on the latter for novel concept learning in an online and continual fashion. Our approach is also related to fast-weight networks (Schmidhuber 1992], since we use the support sequences to generate a fast embedding weight for the new concept. For language, meta-learning is often used for knowledge augmentation (Hu et al. 2023), task adaptation (Chen et al. 2022 Zhong et al. 2021 Bansal et al. 2020), domain adaptation
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(Qian & Yu 2019 Li et al. 2020: Geng et al. 2019), rare word recognition (Lux & Vu 2021
(in ASR), and word sense disambiguation Holla et al. (2020), among other applications (Lee) et al. 2022). Some recent methods frame meta-learning
sequence modeling problem, drawing inspiration from in-context learning (Chen et al. 2022 Fifty et al. 2024). Finally, Lake & Baroni (2023) recently developed method for learning compositional concepts. In
our work, context sentences are encoded to generate new embedding, conceptually similar to a prototype for the new concept, to optimize a general language modeling objective, rather than a collection of task objectives.
Compression: number of methods exist to compress sentences into new embeddings or tokens. Prior work in NLP developed methods for generating task-general embeddings from natural language sentences (Conneau et al. 2017 Kiros et al. 2015 Wang al. 2020).
ReadOnce (Lin et al. 2021) is more recent method for generating compressed document representations which can be used across a variety of downstream tasks. Similarly, recent
methods compress prompts (Chevalier et al., 2023: Ge et al., 2024 Mu et al. 2024 or
documents Xu et al. 2024) into summary vectors either as a form of memory or a method for re-using prompts (e.g. when specifying instructions). RMT (Bulatov et al. 2022 learns memory tokens during pretraining in order to extend the effective context window. Nugget (Qin & Van Durme 2023) dynamically chooses which tokens are aggregated into the encoded representation. Rather than compressing the entire meaning of each context sentence, our method extracts and aggregates information relevant to the new concept.
3 CoLLEGe: Concept Learning with Language Embedding Generation
In this section, we describe our proposed approach for enabling LLMs to quickly learn new concept tokens. Given new word or concept and a set of example sentences containing that word or concept, we want to produce an embedding that captures its semantically meaningful features. We visualize this process for simple language modeling example in Figure
Framing this as a few-shot learning problem, we use a set of K support sequences (si, sk), containing new token, <nonce> to produce a useful embedding for this new token. The new embedding can then be used to augment the knowledge of frozen autoregressive language model. During training, we encourage the LM to use the new embedding to correctly generate query sequence 9.
To perform this generation, the new token in each sup- port sequence is replaced with <mask> token, and each embedded with a frozen masked language model (MLM) used for feature extraction. The contextual embeddings for each sequence are then passed through an additional Transformer self-attention layer to process the contextual embeddings for each sequence to obtain } These are then aggregated using mean pooling, producing sequence embeddings {e e e; n; _ h where n1 is the length of each sequence. The sequence embeddings are aggregated once more using mean pooling, producing single output embedding enew: fnew K _ ej. Mean pooling can also facilitate incremental consolidation of new concepts without having to store past examples. To integrate the embedding with frozen autoregressive LM, we apply two distinct linear layers to produce an input and output embedding for the new token ein and eout' lein eout] Linear(enew The autoregressive LM's input and output token embedding matrices are then expanded with these generated embeddings, and used to model the query sequence.
Concept Embedding Generation:
Wemb.in, WWmbbin ein),
(1)
Wemb. Wemb,out, out, new [Wemb out, eout].
(2)
One novel aspect of our framework is that, un- like many meta-learning approaches, our training procedure follows the same style as pretraining by directly leveraging text data from the pretraining datasets. We hypothesize that good way to rapidly learn new concept is to actually "use" the concept in another sentence- let an LLM consume the newly learned embedding to generate another sentence. Moreover, the autoregressive cross entropy loss is the same as the pretraining
Sampling Few-Shot Learning Episodes:
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objective, so, in theory, our meta-learning procedure can be perfectly blended into the pretraining stage.
To efficiently sample support and query sequences, terms we borrow from the few-shot learning literature, we save sentences containing a new token in an example buffer to serve as support sequences, and when encounter the same token being used again in the training corpus, we will use the sequence as a query sequence. The query sequence can then be saved in the example buffer and used as support sequence again. We find that reusing query sequences as support sequences is helpful for training and allows the model to make use of examples it has already learning when learning new examples. Often, query sequences are longer, comprising a few sentences or paragraph of text. The sentence which contains the new token extracted from each and used as support sequence for a different query sequence concerning the same concept. Not every such sequence ends up in the final set, however, since we filter and rank the examples, see Section 4 for details.
Negative Examples: Initial experiments training on only positive examples, i.e. examples containing the new token, tended to yield generated embeddings with norms of much higher magnitude than those of other LLM input or output token embeddings. One hypothesis was that, since every example the model has to learn contains a new token, prefers to converge an embedding with high norm, to ensure that the new token appears in the query sequence. During normal pretraining, few tokens are shared across all sequences, language models learn both when to generate and when not to generate each token. To likewise teach our model when not to generate new token, we sample a sequence without new token, which we call negative example, and take the sum of the cross entropy loss on the positive example, L and the cross entropy loss the negative example, Lee-
Since we pretrain by replacing existing words with new tokens, can compute the "true" language model embeddings and logits for the remainder of the sequence. Ideally, we want the generated embeddings from our model to match the ground truth embeddings and logits as faithfully as possible, to better approximate the underlying language model distribution. To do this, we retain the original sequence, before masking word with a new token, and compute the output embeddings and logits for the rest of the sequence with non-augmented version of our pretrained autoregressive model. We then compute the cosine distance between those output embeddings and the output embeddings from CoLLEGe, Leos, as well as the MSF between the CoLLEGe LLM logits and the "true" LLM logits using the original embeddings, mmse- Using a more standard objective with distillation temperature (Hinton et al. 2015 was less effective during training.
Knowledge Distillation:
In order to compute these two distillation loss terms, we define the positive example token sequence as 1,,,...., fn,+ and original example token sequence as f lorig .... Lorigg and additionally construct deterministic mapping IN + N that maps a token at index in the positive example to its corresponding token at index k in the original sequence. The tokens following the new token in the positive sequence are guaranteed to have match in the original example sequence, by definition, but the index may be different (if, for example, the word replaced with <nonce> is subtokenized in the original example sequence). We compute our distillation loss terms using:
Lcos n
Σ 1 11-com cos(ete. etecs)oong)
(3)
k Inew
Lmse -newl
Σ (R (io(t),onigg )
(4)
ke
where Inew is the set of new token indices in the positive example sequence, E,, denotes the output embedding at token position i for the positive or negative example sequence, and f, denotes the logit vector at token position i for the positive or negative example sequence.
Final Loss: Our final training loss is simply a sum of these individual loss terms. We explored using linear combination of the loss terms to weight each differently, but found
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Without Definition 2-Shot
With Definition
Method
1-Shot
3-Shot
4-Shot
D+1-Shot
D+2-Shot
D+3-Shot
D+4-Shot
0.0
109 3.0
1.99
TT-2
6.8 0.0
6.8 0.
6.8 0.0
6.++0.
1233 3.5
9.3 2.5
8.1 +53
7.6 +
HiCE
11.51.00 5.0
12.5.222
13.1 1.36
16.1 4.2
19.3
3.5
15,9 182
10.6 1.3
Additive
136++23 2.3
9.1
13
01,4000
129
106
12.9
1.+
Prompting
13.9 4.3
17.7
19.8
218
2.7
19.3 2.55
24.0
CoLL EGe w/o KD/ Neg. Ex.
52
37.7
49
29
42.2 3.1
4..
49.3
49.6
+8.0
CoLLEGe w/o KD
25.9 4.7
33.6 50
35.0
35.7 32
40.3
402
3.5
42.3
12.3
CoLL ECC w/o Loss
30.9 6.0
35.2
5.0
1..44.2
37.1
28
35.7
25
39.6
1.
39.3
19.3 52
38.4
1..114 1.4
CoLLEGe Lese
314
39.8
436
6.0
39,6
46.8
Co w/o Neg. Ex.
28.9
6.0
31.6
1 5.1
34.1
1 48
3366
4.3
37.5
1 6.2
38.4 4.8
42.5
4.6
44.1
1.46
CoLLEGe
35.0
56
40.5
4.
4.5
42.7
3.39
44.5
3,9
42.5
3,9
16.8.3377
45.2
4.32
49.3 1.5
Table 1: Accuracy in percentage on the GRE Verbal Reasoning task. For Token Tuning, we
use LR = 1e-3, as that gave the best performance.
was not significantly better. Our final loss, Lotal is:
Liotal
Lite + Loe
Loos
Lmse
(5)
Cross Entropy Losses
Distillation Losses
Datasets for Training CoLLEGe
In contrast to many other meta-learning methods, which use a specific set of tasks during training, we adopt a training approach that mirrors general-purpose pretraining. In sense, we treat each query sequence, and each new token in turn, as its own "task" to solve. Pretrained language model representations are highly adaptable, and can be successfully ap- plied to a variety of tasks with simple prompting strategies. By adopting a task-general train- ing method, we train module that can produce similarly adaptable embeddings on the fly.
Because CoLLEGe is designed to learn a single new token per sequence, and the LLM is frozen, training is highly sensitive to data quality, both for the support and query sequences. Additionally, three forms of mismatch between support and query sequences are important to guard against: language, contextual meaning, and knowledge mismatch. The first case is mostly self-explanatory, non-English support sequences for an English query sequence cause difficulties in training. Contextual meaning mismatch was particularly important to avoid when training with the Pile (Gao et al. 2020), whose examples are drawn from a variety of sources. Creating support and query sequences from WikiText, as in HICE, often implicitly controls for contextual meaning (all examples are from one source (Wikipedia), and support and query sequences for word are often unintentionally drawn from the same article and thus share contextual meaning). Likewise, knowledge mismatch is more prominent when training with the Pile, since it contains more diverse sources. If one, or many, support sequences are more confusing than the query sequence, this can destabilize training.
Using the Squeakily 3 library, FastText (Joulin et 2016) model for language identification, applied perplexity filtering at threshold 1000 (filtering examples above 1000 perplexity) using KenLM, following de la Rosa et al. (2022). We also filtered examples with too much character repetition as well as examples with words flagged as obscene. Afterwards, we cleaned examples by normalizing white space and punctuation. Each query sequence is constructed from 4 sentence, non-overlapping, chunks of the text examples from the Pile samples.
filtered for English text at threshold 0.90 using the
To build set of support sequences for each query sequence, we first split all examples into individual sentences, and matched each query sequence with sentences that use the same new word. We removed sentences that appear in the query sequence, examples with a large number of newline characters (these often were article titles, tables of contents, or lists), and examples with fewer than 15 words. Earlier experiments training with the Pile showed that some subsets had many low-quality or mixed-meaning examples. Excluding those subsets made the filtering process more straightforward. Table summarries the top subsets from the Pile represented in our dataset.
5 Experiments
In this section, we show experimental results on four different evaluation tasks that we designed: GRE verbal reasoning, definition generation, and slang identification. Note that

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Model
BERTScore F1
ROUGE-L
ELO
TT-1
75.2 1 8.1
7.67 1 0.1
980.78
1 18.5
TT-2
75.2 =.1
978.49
18.4
HiCE
76.7 = 2.2
=000
975.64
- 7.9
Additive
80.1 2.3
0.1
967.22
+ 8.5
Prompting
82.5 + 2.8
15.54 + 0.1
1032.28
+ 28.3
CoI.LGee
84.8 + 2.3
17.81 + 0.1
1065.57
24.0
Table 2: Results when evaluating definition generation using the CoLLEGe-DefGen dataset. We compare the model generated definitions with reference definition using BERTScore and also compute an ELO score for head-to-head comparison. For Token Tuning, we use LR 3e-4, as that performed best. Definitions are generated 1-, 2-, and 3-shot and scores are averaged.
CoLLEGe is task-general concept embedding generation network, and all of the evalu- ations are performed zero-shot, without further training or fine-tuning, just like pretrained LLMs. In the following, we first discuss im implementation and training details, then describe the baseline methods. Afterwards, we present the core results in Subsections 5.1 5.3
Implementation Details: As our pretrained MLM model for the Sequence Encoder, we use RoBERTa-Large (Liu et al., 2019), and apply a trainable Transformer Encoder layer to encode the RoBERTa sequence embeddings. These embeddings are aggregated using mean-pooling to produce a single embedding per sequence, which is further mean-pooled into our Concept Embedding. We use pretrained LLaMA-2 7B model (Touvron et al., 2023) as the pretrained autoregressive language model in all our experiments. Further implementation details can be found in Appendix B
Baselines: In order to evaluate the effectivness of our method, evaluate against baselines from prior work on new concept learnign as well as prompting and gradient descent tuning. More details on implementation for the baselines can be found in Appendix E
Token Tuning (TT) (Lampinen & McClelland 2017) finetunes only the new token embed- ding(s) using gradient descent. The support sequences are treated as the training batch for each step. TT-N denotes N gradient descent steps. Unlike| Larkieenmminnn & McClelland (2017), N is kept small here since we found that large N results in degraded performance. A similar approach has been proposed in Textual Inversion (Gal et al. 2023) for image few-shot learning and generation.
HiCE (Hu et al. 2019) consists of Transformer sequence encoder as well as a Transformer layer to aggregate the sequence embeddings. It is trained to output an embedding with minimal cosine distance to the true Word2Vec embedding.
Additive (Lazaridou et al. 2017 is a simple baseline that consists of summing the Word2Vec embeddings for all tokens in the context that are not the new token.
Prompting uses randomly initialized new token embeddings and includes the support sequences in the prompt. It is strong baseline with direct context access. Since prompt- ing allows the LM reason over the tokens in context, can be combined with our embedding generation approach (see Appendix For additional experiments). However, prompting original sentences can also make the context window too long and distracting.
5.1
GRE Verbal Reasoning
The GRE verbal reasoning task is challenging type of question appearing in Graduate Record Examinations that not only tests the understanding of rare vocabulary but also their logical placement in sentence. We increase the difficulty here by making each multiple choice answer an unknown vocabulary word with a few example sentences as hints. We test whether the CoLLEGe generated concept embeddings can directly support downstream verbal reasoning.
Using actual GRE practice questions from Kaplan GRE prep book (Kaplan 2019 we design a task where a language model has to either select the top or top-2 choices for sequence with blanks. Examples for each of these questions are provided in Table in Appendix C Questions were hand-annotated from the Kaplan book and details about the cleaning process can be found in Appendix [) We produce high-quality selection of 44
Dataset:
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Example Sentence
CoLLEGe Definition
True Definition
Word/
Phrase
The eerie creak of the attic door, coupled with the flickering candle- light, was enough to give anyone the <nonce?.
a feeling of unease, usually in the stom- ach, caused by anxi- ety or fear.
feelings of uneasi- willies ness
Intrigued by holistic therapies, she found herself lying soft mat as the therapist applied <nonce> to ious points on her body to alleviate her chronic migraines.
a substance that is used to heal or soothe a part of the body.
treatment of symp- toms by applying pressure with the
acupressure
fingers to specific pressure points on the body
Nestled in the far comnrr of the bustling newsroom, the diligent <nonce> worked tirelessly, transcrib- ing reporter's 's notes into clean, easy- to-read articles.
a person who writes edits for news- paper, magazine, other publication.
someone employed to make written
copyist
copies of documents and manuscripts
The delicate <nonce> sprouted from the forest floor, adding touch of alien beauty to the woodland scene.
a plant that resem- bles a mushroom.
fungus composed of several apothecia that look like elon- gated rabbit ears;
Wynnea
americana
Table 3: Definitions generated with CoLLEGe, using the prompt "The word <nonce> is defined as". Each definition is generated using the single example sentence shown. None of the example sentences are provided in-context to the model.
GRE Verbal Reasoning problems with a single blank (i.e. not multi-part). No partial credit is given for selecting one of the two answers for "top-2" question and chance accuracy is 8.1 %. On version of this task with the real word forms, not new tokens, pretrained LLaMa-2 7B scores 75,, which serves as an upper bound on our potential performance.
To evaluate, we create version of the sequence for each possible choice, and calculate the log probability of each such sequence. The highest log probability sequence is selected as the chosen answer. The final scores reflect the average accuracy over 10 trials of sampling different example sentences from GPT-4.
Results: Results are reported in Table with additional results for "Prompting*.." reported in Table Appendix F1 Token tuning does not seem to help much, and even sometimes hurts performance. Model performance also increases with more examples, showing effec- tive utilization of multiple example sentences. By contrast, more examples can sometimes harm Prompting performance, likely due to the additional in-context examples distracting the LLaMA model. We ablate the use of negative examples, knowledge distillation, and both the Leos and Lmse components the knowledge distillation loss. CoLLEGe outperforms each ablation except losing to one in two columns (D+2/3-hhot), Notably, neither negative examples nor knowledge distillation is that effective on its own, but when combined boost performance significantly.
5.2 Definition Generation
To probe how well our model understands a new word, we prompt the LLM to generate definition for the word given one or more example sentences, as shown in Figure I
Datasets:
We evaluate on two different datasets to test definition generation: CoLLEGe-
DefGen, which we create, and the Oxford dataset used in Giulianelli al. 2023 To construct
CoLLEGe-Dee DefGen, we selected 954 words from WordNet (Miller 1994). We then prompt
GPT-4 (OpenAl 2023) to generate an example sentence for the word using the prompt: Give me unique, descriptive sentence using the word "[WORD]" without defining it or making it obvious what the word means. Without the latter half of the prompt, many generated examples rephrased the definition. Examples are generated at temperature 0.8. Since both our model and the baselines continue to generate text, we select the first sentence generated as the definition for scoring.
The generated embeddings often capture high- and low-level semantic details of the example sentences. Sometimes this is
Results: Generation examples are reported in Table 3
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CeeLeGe-DefGen
Oxford
Model
BERTScore F1 ROUGE-L
BERTScore F1 ROUGE-L
FLAN-Base-eefnstr
84.0
12.1
83.4
15.4
FLAN-Large-Deflnst
85.0
13.2
83.6
16.6
FLAN-XL-DefInstr
83.7
11.6
83.6
14.0
CoLLEGee
83.5
16.5
83.1
15.4
Prompting + CoLLEGe
84.1
18.0
83.6
17.1
Table 4: Results for our comparison with Giulianelli et al. (2023 's FLAN-T5 models on our
CoLLEGe-DefGen dataset and the Oxford dataset. All evaluations are done 1-shot, with single example sentence, and the target word is replaced with new token.
fairly precise, for example the generated definition for willies is exactly correct and similarly with copyist. CoLLEGe is also able to identify Wynnea americana as a mushroom. Even when the definition is not quite right, it may capture general features of the concept correctly, and may reflect the limited information contained in the example sentence. The definition for acupressure, for example, is not exactly correct but very good inference based on the example provided. Additional generated definitions, including those generated using more than one example sentence, are shown in Table
11 in Appendix G.1
We also show byde-by-
side comparisons with the baselines in Table 12 in Appendix
G.2
Some failure cases are
described in Appendix G.3
In order to evaluate the quality of generated definitions, we compare definition generated from our model to one generated from baseline model as well as to a ground truth definition. Definitions are generated 1-, 2-, and 3-shot and results are averaged and reported For comparison between models, we simulate a head-to-head competition and compute the ELO score (Elo 1978) of each model. Specifically, for each example in the task dataset, we choose a k-shot setting and sample a baseline at random. We then compare the definition generated by CoLLEGe with the one generated by the baseline. Based the wis,,twin,, lose, or tie tie-we update the ELO score, starting with an initial score of 1000 for all models. To choose a winner in each "round", we use the Outlines package we ask GPT-3355 select which definition is best for the word in question or if they are tied. The order of the choices (both generated definitions and "tie") are randomized. We compute ELO separately for T We also compare the generated definitions with ground truth definitions for each word by computing the BERTScore F1 (Zhang et al.] 2020) and ROUGE-L (Lin 2004). In all thrée quantitative evaluations, CoLLEGe outperforms the baselines. Qualitatively, only Prompting produces generated definitions that are somewhat competitive. Definitions generated by the other baselines are often incoherent, generating repetitive text unrelated words and characters.
in Table 2
We next compare CoLLEGe with the series FLAN-T5 (Chung et al. 2024) models finetuned by Giulianelli et al. (2023) to generate definitions, which we denote PLAA--aaee-efiile FLAN-Large-Derinste and FLAN-XL-Definstr: Each of these models is prompted with example sentence followed by the question, "What is the definition of [WORD]?". For fair comparison, we replace the target word with new token when evaluating the FLAN- T5 models and restrict CoLLEGe to one example sentence, since the finetuned FLAN-T5 models are trained to only use one example as well. Finally, since the FLAN-15 models are prompted, we include results for Prompting + CoLLEGe well. The results are presented in Table 4| When evaluated on our dataset, both CoLLEGe and Prompting CoLLEGe achieve much higher ROUGE-L scores than all FLAN-T5 models. For BERTScore, Prompting + CoLLEGe outperforms all but FLAN-Large, although all scores are fairly close together. On the Oxford dataset, CoLLEGe and Prompting CoLLEGe score comparably to the FLAN-T5 models, and Prompting CoLLEGe slightly outperforms in terms of ROUGE-L. Notably, CoLLEGe achieves this zero-shot, without any additional training on the definition generation task, while the FLAN-T5 models are specialized for this purpose.
5.3 Twitter Slang
To emulate new word learning in more natural setting, construct task based identifying the correct definition for a slang term, using Tweets as example sentences.

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Published as a conference paper at COLM 2024
Model
1-Shot
2-Shot
3-Shot
4-Shot
TI-1
32.2 + 1.4
32.1
2.6
32.6 1 1.3
34.5 1 0.4
TT-2
32.3 = 1.6
3288
33.1 0.2
32.5 3.0
Additive
27.0 = 1.0
1.9
28.0 0.7
29.0 = 1.0
HICE
34.0 = 1.1
32.7
1.9
31.3
2.3
32.8 = 1.0
Prompting
41.0 + 1.0
47.0
+ 2.1
51.7 + 1.8
53.8 + 1.4
CoLLEGe
49.3 + 1.0
53.2
+
1.6
54.8
+ 2.6
60.0 + 0.7
Table 5: Accuracy in percentage on the CoLLEGe-Slang benchmark dataset. CoLLEGe achieves higher accuracy on our Twitter Slang task than each of the baselines, including Prompting.
Dataset: To build CoLLEGe-Slang, we first hand-curate set of 80 recent slang terms as well as their definitions. Alongside each term is a list of up to 8 high-quality example Tweets which use the term in informative way. For this hand-curated set, example tweets are predominantly from 2022 and 2023. To supplement these, we then sample 120 additional slang terms from UrbanDictionary and the Online Slang Dictionary. We select example tweets from the Twitter archive, using the pipeline from Hu et al. (2022). Some examples from the hand-crafted set are shown in Table 13 More information about the filtering process, data sources, and curation details for this dataset can be found in Appendix
To evaluate the different models on this task, we select the true slang term, its example tweets, and its true definition. We then select 3 different incorrect slang terms and their examples. We score the log probability of the true definition conditioned on each of examples. The highest probability is selected as the "choice". If it corresponds to the correct combination definition and slang term, that counted as a correct choice, otherwise not. We score the model based on its accuracy across the whole set of slang terms.
Results: Results are presented in Table 5 Without providing example tweets in-context, CoLLEGe outperforms each baseline. In fact, CoLLEGe is able to outperform prompting directly, showing that in novel contexts (such as Twitter slang), in-context examples may be more confusing than concise embedding representation. Notably, when including example tweets in-context, the baselines hurt performance compared to a simple prompting baseline. Only using the CoLLEGe generated embeddings in this setting improve performance over prompting with randomly initialized new token embeddings. We also compare CoLLEGe to our baselines in the prompting setting in Table 10
Conclusion
In this paper we present CoLLEGe, a few-shot learning framework for new concept acquisi- tion for pretrained LLMs. We model our meta-learning approach on the original pretraining task by sampling few-shot learning episodes from language model pretraining datasets and using next-word prediction, as our primary meta-leaming objective. CoLLEGe generated embeddings contain rich and task-general semantic information, generalize to multiple challenging tasks test time zero-shot, without additional finetuning or training, and are particularly useful for more complex reasoning tasks.
7 Limitations and Future Work
While CoLLEGe achieves the best performance in all of the benchmarks, we summarize few limitations in our current framework. First, the generated embeddings sometimes miss precise details in the examples, instead encoding higher level semantic information. This can be seen in some incorrect generated definitions, where general features of the unknown concept are correctly inferred even though specific details are missed. Second, we find the averaging mechanism cannot fully achieve parity with pretrained embeddings, even with more support sequences.
Our work points to number of future research directions. In the short term, work needs to be done to investigate different data mixes for training CoLLEGe. More broadly, this research is a first step in an exciting direction for future research: online continual concept acquisition performed jointly with pretraining- incrementally identifying and compressing new concepts from an online stream of sequential experience.
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Published as a conference paper at COLM 2024
Acknowledgments
We would like to thank the Microsoft Accelerating Foundation Models Research program for providing cloud compute credits for running some parts of our LLM experiments. The compute was also supported by the NYU High Performance Computing resources, services, and staff expertise.
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A Dataset Composition
Our dataset for training CoLLEGe consists of samples drawn from subsets of the Pile shown below in Table
Source
Num. Examples
Pile-CC
79,606
Books3
51,850 BooksCorpus2
3,436
Table 6: The top Pile subsets represented in our dataset.
B Implementation Details
During training, we provide 1 support sequence for each query sequence and generalize to K >> during testing. We train our model for 28000 steps at batch size 32, with learning rate of 1e-3, linear learning rate schedule with warmup, and using the Adam optimizer (Loshchilov & Hutter 2019) with weight decay = 0.1 and default beta values. We experimented with different beta values during training, but found they had little effect. During training we clip gradients to a norm of 1.0. The final model checkpoint is selected based on its GRE score.
Using the default initialization for our Encoder produces input and output embeddings that are significantly larger in norm than those of the pretrained LLaMA model. During training with the default initialization, a lot of training time is spent reducing the norm. To address this inefficiency, we apply Layer Normalization (Ba et al. (016) layer before the input and output linear layers, and initialize those layers so that the expected norm is as close to the average input output token embedding norm as possible.
C
GRE Verbal Reasoning Examples
The GRE task consists of two different types of fill-i-thhe-llnk questions. The first type asks you to select the best possible choice to complete the provided sentence, it serves as a test of the top-1 prediction. The second type asks for which two words best complete the sentence. Often these words are similar, but distinct. It tests how the top-2 predictions of the LLM. We show an example for both types below in Table
D Data Processing
We provide further details cleaning and processing for some of our task datasets.
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Question
Answer Choices
Correct
Evaluation Type
Answer(s)
Mary's former classmates taken aback by her [BLANK] be-
gregarious
d) reficent
Choose the word for each blank that best fits the meaning of the sentence as whole.
b) discourteous
havior at the reunion for, dur- ing her school years, she was frequently reprimanded for cre- ating disturbances with her ex- uberant outbursts and playful antics.
obsequious
reticent
e) scurrilous
The firefighter, desperate to save
a) stalwart
b) precarious tottering
Select the two answer choices that, when in- serted into the sentence, the meaning of the sen- tence as a whole and yield complete sentences that are similar in meaning.
the children on the second floor
b) precarious
of the fiery house, rushed into their bedroom; his colleagues, more wary of the [BLANK] structure, remained outside.
stout
d) irrefragable
tottering
fecund
Table 7: The two types of questions used in CoLLEGe-GRE.
The cleaning process for the GRE dataset involved normalizing the different formats for blanks (i.e. empty spaces, underlines, "(a)", etc.), removing artifacts from the conversion to text from PDF, and associating each question with its answer from the answer key.
GRE:
Twitter Slang: We filter examples from this archive for flagged obscene words using Squeak- ily, but it is important to note that online slang is often obscene. This especially true for the sources used to define the slang terms (UrbanDictionary in particular).
We used slang definitions from UrbanDictionary Dictionary.com's Pop Culture sections, the recent American Dialect Society meeting 8 Bark Wiktionary 10 and the Online Slang Dictionary for our dataset.
5 and Slang
E Baseline Implementations
Hice: To train HiCE, we follow the method outlined in the paper, and use the WikiText-103 dataset to train the model with morphology network. We use hyperparameters from the authors' implementation.
Word2Vec Projection: For the Additive baseline well HiCE, the baseline outputs Werd2Vec embedding. To make this compatable with LLaMa, we train linear layer on shared tokens between LLaMa and Word2Vec to map between the embedding spaces.
Token Tuning: For Token Tuning, we treat the example sentences as a "batch" and perform N=1,2 steps of gradient descent on only the input and output embeddings for the new token.
F Results for Prompting+
Since Prompting baseline provides all the support sentences in the context window, allowing the LM to attend to the new token embeddings directly, we also show using CoLLEGe-generatte embeddings improves performance over random initialization and our other baselines (denoted "Prompting+...",
httper//wwwyubbandcciiooayyyoo//
7 htttpp//wwweoadccoonnroooo/o/oss
9bark.us
"https//wwww.wikiionarro.g////// "htp:///oninnslngggiitioaayyyo
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E1
GRE Results for Prompting+
Table 8 shows results for the GRE task when prompting with examples in-context, using embeddings for the new token generated by the model or baseline following the "+".
Without
Definition 3-Shot
With Definition
Method
1-Shot
4-Shot
D+1-Shot
D-2-Shot
D43-Shot
D+4-Shot
Prompting
139
177
1.30
19.8 42
21.8
3.30
21.6 2 5.7
20.5
68
19.3 1
24.0 4.7
+ CoLLEGe w/o KD/ Neg, Ex.
25.7 3.9
31.
1:39
327 57
32 6.1
35.4
1 4.3
31.
38
3344 48
33.4 58
Foe w/o KD)
4.5
29.6
3.4
+1.8
29.1 4.0
33
28.9
4.1
33,9
35.7 2-
CoLLEGe w/o Loo
33.4
+ Co w/ol mae
316
+18
29.6
27.5 +-4
31.5
56
31.8
29.6
+18
CoLLEGe w/o Neg.
35.0 3.7
23
314
25,9 2 10.3
31.
311
=11
28
28.4499
CoLLLBGe
33.4
Table 8: Accuracy in percentage the GRE Verbal Reasoning task when prompting with examples in-context, using new token embeddings generated by each model or baseline. Results for prompting with randomly initialized embeddings reproduced here for clarity.
All CoLLEGe models outperform the Prompting baseline, where new token embeddings are randomly initialized. Prompting by including the definition of each term alongside the example sentences improves performance for the baselines the most, but CoLLEGe significantly outperforms. Each Crmnpting+CoLLEGG.. model performs worse than the unprompted version, which we hypothesize due lack of instruction tuning data in the training dataset.
F2 Definition Generation Results for Prompting+
We present results in Table D for our definition generation task with examples presented in- context, using our baselines or CoLLEGe generate the new token embedding. ELO scores are calculated separately from those in Table by sampling random baseline challenger to the "Prompting=CoLLEGe" model.
Model
BERTScore F1
ELO
Prompting
0.825 + 0.028
1002.09
- 22.87
TT-1
0.762 + 0.035
959.88
+ 17.22
TT-2
0.763 0.045
962.10
+ 12.65
HICE
0.740 + 0.098
949.32
+ 10.77
Additive
0.735 + 0.098
950.61
+ 3,48
CoLLEGe
0.858 + 0.029
1176.00
+ 12.69
Table 9: Results for the definition generation task, when prompting with examples in-context. We compare the model generated definitions with reference definition generated by GPT-4 using BERTScore and simulate random challenges between CoLLEGe and each baseline and compute and ELO rating. Token Tuning results are for LR 3e-4, as the main paper.
Generated definitions improve with in-context examples, and we note that our model far outperforms the baselines. The only competitive baseline prompting with randomly initialized embeddings.
F3
Twitter Results for Prompting+
When examples Tweets are provided in-context, our CoLLEGe model's accuracy on the slang identification task increases. For other baselines, aside from prompting with randomly initialized embeddings, however, performance either degrades or remains about the same. With the Word2Vec-basse baselines, this may due the difficulty of mapping between Word2 embeddings and the LLaMA input and output embedding space. We present these results in Table 100
G Generated Definitions
G.1 Additional CoLLEGe Definitions
We show additional definitions generated from CoLLEGe, including definitions generated with more than example, in Table
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Model
1-Shot
2-Shot
3-Shot
4-Shot
Prompting
41.0 + 1.0
47.0 2.1
51.7 1.8
53.8
i 1.4
TT-1
30.5 = 3.5
28.2 2.9
26.0 1.4
24.1
1.2
+ TT-2
29.3 = 3.0
2.7
25.
24.0
- 1.6
+ Additive
30.7 3.8
2..2
24.5
24.1
+ 1.7
HICE
25.0 + 4.9
26.0
27.8 +: 3.0
25.5
+ 0.8
CoLLEGe
56.5 + 2.0
60.5 2
67.4 +: 1.0
69.8
+ 0.8
Table 10: Accuracy in percentage on the Twitter Slang task, where example Tweets are provided in-context.
Example Sentence
CoLLEGe Definition
True Definition
Word/ Phrase
During the complex abdominal surgery, the surgeon carefully moved the <nonce> aside to gain better access to the patient's damaged organs.
a surgical procedure in which portion of the intestine is brought through an opening in the abdominal wall.
a fold peritoneum supporting the viscera
omentun
The yellow blooms of the <nonce> added a vibrant con- trast to the green canvas of the wetlands.
"a plant of the genus Ficus, having a milky sap and large, often edible, fruit."
aromatic evergreen or deciduous dioecious
Lindera
shrubs or trees of east- Asia and North America
The prestigious <nonce?, clad in elaborate costumes, filled the au- ditorium with their mesmerizing harmonies and dramatic perfor- mances.
person who is skilled the art of dancing.
company that pro- opera duces operas
company
After months of rigorous re- hearsals, the <nonce> finally brought their magnum opus to life, filling the ornate theater with powerful harmonies that resonated with every member of the riveted audience.
Despite countless imitations flooding the market, only her grandmother's secret recipe for apple pie the <nonce>, After tasting many alternatives, he finally found the <nonce> of artisanal cheeses in a quaint little shop in Paris.
the most beautiful or perfect specimen of its kind.
informal
usage at- real Mc-
tributing authenticity
Coy
Despite all the replica paintings she had seen, it was breathtak- ing to stand before the <nonce> in the museum.
Table 11: Additional definitions generated with CoLLEGe, using the prompt "The word <nonce> is defined as". Each definition is generated using the examples. None of the example sentences are provided in-context to the model.
G.2 Qualitative Comparison of Generated Definitions
We present the definitions generated by CoLLEGe side-by-side with those generated by our baselines in Table 12
17

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Published as a conference paper at COLM 2024
Word/Phrase
horsecar
popishly
True Definition
early form of streetcar that drawn by horses
like the Pope; in popish manner
"motorized vehicle with cabin and a platform for passengers to stand on."
in manner that is intended to attract attention or admiration
CoLLEGe
HiCE
word" "the word a" is
" "a" word word that the same.
that the place where you are the place.
Additive
follows.
'the opposite of the word in the die- tionary
TT-1
follows.
the opposite of the word,
TT-2
in the heart of bustling 19th century Boston, he spent his mornings as loyal conductor, navigating through
flamboyant manner of acting speaking.
Prompting
Table 12: Side-by-side comparison between CoL.L.Ge--eeeratee definitions and definitions generated by the baselines.
In general, the baselines (without Prompting) are unable to generate usable definitions for the new concept.
G.3 Failures of Generated Definitions
Both Word2Vec baselines tended to produce embeddings that were unusable for generating definitions.
Word2Vec Baselines:
When analyzing generated definitions, it is clear that there are some "default" definitions the model will generate when the embedding for the new token is not informative enough.
CoLLEGe Failures:
Some of these common "default" definitions, listed in order of frequency, are:
a person who is not member of a particular group or class
place of refuge or shelter
a person who is a source of annoyance or irritation
a noun
H Slang Examples
We highlight a few Twitter slang examples from the hand-curated subset of our task dataset in T3
Slang Term
Definition
Example Tweet
rizz
The ability to confidently approach people and talk to them, in a more romantic or flirty way.
Imagine having so little rizz that even the Al girlfriend rejects you. Just complete negative game.
hits different
When something is significantly bet- ter than usual or is way better under certain circumstances.
getting called pretty in person just hits different. people be making my day.
gorpcore
A fashion style that is similar to that of hiking/ wwwlerness//ttiiii tech wear.
An anecdote from my coverage of Gorpcore trend: This vintage seller put Gore-Tey hats up for sale on his website at $135...
Table 13:
Examples from the Twitter Slang task, showing the slang term, its definition, and an example tweet.
18


Reviews:
[{"review_id": "Nbc2LSsId1", "reviewer": "Reviewer_6Rmw", "summary": "The paper proposed a few-shot method to enable a language model to learn new concepts on the fly.", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": true, "final_justification": "", "rebuttal": "We thank the reviewer for their helpful comments about our work.\n> New concepts come constantly and naturally and should be identified and learned naturally by the language model itself in the particular contexts of the concepts. \n\nThis is an interesting direction for future research and we believe CoLLEGe is an important first step in addressing it. In order to eventually identify and learn new concepts constantly, we first develop a method which can learn identifiable new concepts on the fly.\n\n> After identifying such concepts, positive and negative examples have to be prepared in sophisticated ways in order to learn the concepts.\n\nOur goal is to present a method which learns a new concept given only a few examples. To train our model, select words to be used as new concepts and train our model to predict query sequences using them with a few support sequences. The new concept is represented by a new token, guaranteeing that the language model has not encountered it before. Negative examples along and distillation are used to improve the generated embeddings.\n\n> Training even involves meta-learning, which makes the task even more difficult.\n\nWe choose a meta-learning objective that blends into the pretraining objective, which is not much more difficult than standard autoregressive training and allows CoLLEGe to generate task-general embeddings with rich semantic knowledge.\n\n> How do you know that the language model does not have a concept already in the system?\n> Experiments are conducted but again artificial.\n\nIn our Twitter evaluation, we hand-select very recent slang terms. Some incorrect answers to questions about slang terms generated by ChatGPT-3.5 and LLaMA-2 can be seen [here](https://postimg.cc/D8K8Bmhd). Additionally, our Twitter and GRE evaluations contain multi-word phrases as single concepts, which LLaMA-2 model has not seen. Finally, if LLaMA could simply make use of its pretraining knowledge, then the baselines would not perform so poorly. \n\n> The first area is model editing. \n> Another area is continual learning, [...]\n\nThank you for these pointers. We intend to cite them as relevant background, but do not believe they are directly relevant. Applying model editing to the new concept learning setting would mean manually editing the LM for each new concept, which seems more artificial and sidesteps our research problem. Also, our problem is not continually adapting to new domains or tasks, like in Ke et. al.", "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": "The problem is very interesting and it is one of the critical problems that should be solved for language models.", "reasons_to_reject": "I found the problem studied in the paper artificial. I have a few concerns: \n\nNew concepts come constantly and naturally and should be identified and learned naturally by the language model itself in the particular contexts of the concepts. Manually identifying such concepts is not practical. How do you know that the language model does not have a concept already in the system? After identifying such concepts, positive and negative examples have to be prepared in sophisticated ways in order to learn the concepts. Training even involves meta-learning, which makes the task even more difficult. \n\nExperiments are conducted but again artificial. \n\nThe paper mainly described what the authors have done with gory details, but provided little principle.  \n\nThere are two closely related areas of research that the paper has missed. The first area is model editing. A large body of literature on the topic is trying to do what you are doing. Another area is continual learning, which tries to ensure little negative interference to the language model when learning new knowledge or concepts. The following papers can give you some ideas about the existing work in these two areas. \n\nMitchell et al., Fast Model Editing at Scale, ICLR-2022\n\nWang et al. Knowledge Editing for Large Language Models: A Survey. arXiv:2310.16218 [cs.CL]\n\nKe et al. Continual Pre-training of Language Models. ICLR-2023.", "questions_to_authors": "No other questions", "extra_scores": {}}, {"review_id": "ryKgeu4pC1", "reviewer": "Reviewer_EA4C", "summary": "This paper introduces a framework called CoLLEGe for learning how to embed concepts from LLMs. The framework uses a meta-learning approach and makes use of text data from pretrained LM datasets. The proposed method efficiently samples support and query sequences by using saved sentences with new tokens. The knowledge distillation process then involves matching the generated embeddings with the ground truth embeddings and logits.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their helpful suggestions and comments on our work. \n\n> Several works have been proposed recently on learning static vector representations of concepts such as MirrorBERT, Numberbach, etc. Perhaps authors should discuss these concept embeddings and use them instead of simply averaging mentions.\n\nThis is an interesting idea, which we may explore in future work. MirrorBERT seems like a promising embedding method and worth trying. Numberbatch embeddings may be less directly applicable, but they may be useful alongside contextual embeddings. \n\n> more detail about the sampling and knowledge distillation process in section 3\n\nWe first select words to be used as new concepts during training based on word frequency in the original training corpus, since we want words which do not appear too often, because they would be too easy to learn, or words that are too rare, because their representation would be too noisy. For each such word, we identify pairs of query and support sequences.\n\nFor distillation, we retain the query sequence with the original word form (e.g. for “My ◾is complaining I never have any time to relax”, the original would be “My **beige flag** is complaining I never have any time to relax”). The intuition is that CoLLEGe should represent “is complaining I never have any time to relax” in the query sequence similarly to how LLaMA would represent it in the original sequence. This encourages CoLLEGe to generate embeddings such that the output embeddings and the logits for that subsequence are close (in cosine distance and mean-squared error, respectively) to those of the LLaMA model for that same subsequence originally\n\n> Could the authors explain how the concept embedding is taken into account in the LLM to produce the definition?\n\nWe sample from the LLM conditioned on the prompt \"The word '◾` is defined as'\" in order to generate a definition for the new concept. CoLLEGe learns to generate an embedding for the new concept token ◾, and that embedding is projected to the input and output embedding space of the LLaMA model. The LLaMA model then makes use of those embeddings when applying the embedding the prompt (and subsequent sampled tokens) and when it computes the output logits. \n\n> Could the authors add more clarification about the sampling process?\n\nWe believe you are referring to the sampling process for the definition generation task. All of our definitions are generated using greedy decoding at temperature = 1.0.", "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": "- the paper is well-written and easy to follow\n- the proposed method is novel, and the methodology is clear \n- experiments on GRE verbal reasoning, definition generation, and slang identification show the efficiency of the proposed framework.", "reasons_to_reject": "- Several works have been proposed recently on learning static vector representations of concepts such as MirrorBERT, Numberbach, etc. Perhaps authors should discuss these concept embeddings and use them instead of simply averaging mentions. \n\n- more detail about the sampling and knowledge distillation process in section 3", "questions_to_authors": "- Could the authors explain how the concept embedding is taken into account in the LLM to produce the definition? \n- Could the authors add more clarification about the sampling process?", "extra_scores": {}}, {"review_id": "tW067KqNzX", "reviewer": "Reviewer_zTnf", "summary": "This paper presents a framework for a concept embedding generation system. The proposed method is a rather elegant approach to learning new concept embeddings, where new concepts are learned by masking them with a frozen MLM (roBERTa), encoding example sentences mentioning them, pooling their representation, and then train an autoregressive LLM (LLama2-7b) with an augmented vocabulary of +1 new concept. Different components play an important role, such as sampling negative examples or ensuring the masked representation is as close as possible to the true (unmasked) one. \n\nThe method I found to be really neat, and the presentation and general writeup very clear. The experiments are also reasonable (verbal reasoning, definition generation and correct definition identificatino from Twitter), and show that such models could be very useful for domain-specific or slang understanding.\n\nMy main criticism is that the evaluation, while sensible, seems to be quite limited and not be well positioned with existing lexical semantics benchmarks. It seems this would be important so that we can understand, beyond comparing with GPT, etc., how this fine-tuned Llama-2 model fares with older but well tested models.", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": true, "final_justification": "", "rebuttal": "We thank the review for their helpful comments on our work and for pointing to prior work relevant to our evaluations. \n\n> The evaluation is a bit slim and ad-hoc.\n\nAll of our evaluations are done zero-shot, without any additional finetuning. We built our tasks to test a wide array of settings, including complex verbal reasoning, definition modeling, and a more natural slang setting. CoLLEGe is trained on general purpose language modeling data to enable broad transfer.\n\n> GRE experiment seems fine, however I was't able to find any discussion about the role of definitions, and given that increasing the number of definitions for the considered terms boosts performance significantly [...].\n\nTo clarify, we only present the model with one definition alongside 1–4 support sequences. For GRE, we treat each answer choice as a new concept represented with a new token.\n\n> Definition generation [...]\n\nThank you for the pointers to this research. With the exception of Dict-BERT, we were unaware of these, so this is very helpful. We reached out to the authors of Generationary, as [suggested](https://sapienzanlp.github.io/generationary-web/), but have yet to hear a response. We were able to compare our work with that of Giulianelli et. al. (2023) by generating definitions 1-shot from their Oxford dataset and replacing the target word with a new token. The results can be found [here](https://postimg.cc/K114Vk91). CoLLEGe and Prompting + CoLLEGe score comparably to FLAN models trained for definition generation when the target word is replaced with a new token. \n\nWe also evaluated the FLAN models on our task, with results [here](https://postimg.cc/Tp43r2Rf). CoLLEGe outperforms in terms of ROUGE-L and performs on-par or slightly better than FLAN when evaluated with BERT-F1. Importantly, the FLAN models are trained specifically to generate definitions given an example sentence. CoLLEGe, on the other hand, does not undergo any additional training or finetuning on definition modeling and is able to perform about the same or better.\n\n>Twitter slang experiment [...]\n\nThank you for the suggestion. Our Twitter task is related to WiC but distinct. WiC is binary classification, but in Twitter we provide a choice between one correct and three incorrect (slang, definition) pairs. We are in the process of comparing these methods to ours on both WiC and on our Twitter task, and we plan to add these additional results during discussion.", "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": "Very nice method, interesting insight leading to the negative examples loss and knowledge distillation. This leads to flexible representations that could be used in several downstream tasks.\n\nVery well written paper, with nice introduction and plenty of additional details in the appendices.", "reasons_to_reject": "The evaluation is a bit slim and ad-hoc. For example:\n\n- GRE experiment seems fine, however I was't able to find any discussion about the role of definitions, and given that increasing the number of definitions for the considered terms boosts performance significantly, it would be good to understand a bit better why. It is not clear if they all come from the same source, mixed, or if more than 4 were tested.\n\n- Definition generation. There is a body of work on definition generation (aka definition modeling), with standard datasets and metrics. I would have expected these works and baselines to be included (see below).\n\n- Twitter slang experiment seems fine, although again, there are other works and datasets that could have been referenced. Beyond this, this looks very similar to the WiC-TSV task, so I'd encourage the authors to add an experiment here and compare with appropriate baselines.\n\n\nDefinition generation\n[1] - Bevilacqua, M., Maru, M., & Navigli, R. (2020). Generationary or “how we went beyond word sense inventories and learned to gloss”. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 7207-7221).\n[2] - Giulianelli, M., Luden, I., Fernández, R., & Kutuzov, A. (2023). Interpretable word sense representations via definition generation: The case of semantic change analysis. arXiv preprint arXiv:2305.11993.\n[3] - Mickus, T., Van Deemter, K., Constant, M., & Paperno, D. (2022). Semeval-2022 Task 1: CODWOE--Comparing Dictionaries and Word Embeddings. arXiv preprint arXiv:2205.13858.\n\nWiC-TSV\n[3] - Breit, A., Revenko, A., Rezaee, K., Pilehvar, M. T., & Camacho-Collados, J. (2020). WiC-TSV: An evaluation benchmark for target sense verification of words in context. arXiv preprint arXiv:2004.15016.\nMapping words to definitions and mentions\n\nOther helpful works mapping words, definitions and examples, maybe good for swapping with the original encoder\n[4] - Yu, W., Zhu, C., Fang, Y., Yu, D., Wang, S., Xu, Y., ... & Jiang, M. (2021). Dict-bert: Enhancing language model pre-training with dictionary. arXiv preprint arXiv:2110.06490.\n[5] - Chen, P., & Zhao, Z. (2022). A unified model for reverse dictionary and definition modelling. arXiv preprint arXiv:2205.04602.", "questions_to_authors": "Just a typo: Sect 5.1 \"On an version\"", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes a new method for large language models to learn new concepts on the fly to overcome current methods such as prompt-based in-context learning and few-shot fine-tuning. For achieving this, the authors design a meta-learning approach for flexible embeddings for new coming concepts. 

Among three reviewers, two reviewers voted to acceptance score (7) and one reviewer gave a rejection (4) initially.
All reviewers agree that interesting setup, the importance of the task that this paper addresses, novel methods, and good writing. 
The main concerns were less natural setup and lack of description on related research area. 

During the rebuttal period, the authors tried to address the concerns and it seems that some of them were alleviated. 

AC carefully read the paper, the review comments, and the authors' feedback.

Considering the discussion comments as well, AC agree the contribution of this paper, and thus recommends accepting this paper.

AC asks the authors reflect valuable comments of the reviewers in the final camera-ready version.
Decision: Accept