ELICIT: LLM Augmentation Via External In-context Capability
Authors: Futing Wang
Jianhao Yan
Yue Zhang
Tao Lin
Venue: ICLR 2025 Poster (2025)
Keywords: modular
Primary area: foundation or frontier models, including LLMs

Abstract: Enhancing the adaptive capabilities of large language models is a critical pursuit in both research and application.
Traditional fine-tuning methods require substantial data, computational resources, and specific capabilities, while in-context learning is limited by the need for appropriate demonstrations and efficient token usage.
    Inspired by the expression of in-context learned capabilities through task vectors and the concept of modular capability or knowledge, we propose ELICIT, a framework consisting of two modules designed to effectively store and reuse task vectors to enhance the diverse adaptive capabilities of models without additional training or inference tokens.
    Our comprehensive experiments and analysis demonstrate that our pipeline is highly transferable across different input formats, tasks, and model architectures.
    Externally storing and reusing vectors that represent in-context learned capabilities not only shows the potential to extract modular capabilities but also significantly enhances the performance, versatility, adaptability, and scalability of large language models, paving the way for more efficient and effective use of these models in a wide range of applications.

Published as a conference paper at ICLR 2025
ELICIT: LLM AUGMENTATION VIA EXTERNAL IN- CONTEXT CAPABILITY
Futing Wang 1.2. Jianhao Yan Yue Zhang 23 Tao Lin 2,4 Zhejiang University Westlake University 2
Institute of Advanced Technology. Westlake Institute for Advanced Study Research Center for Industries of the Future, Westlake University" 4 (wangfuting, yanjianhac zhangyue, lintao] (etaao@@eeste @west lake. edu.cn
ABSTRACT
Enhancing the adaptive capabilities of large language models is a critical pursuit in both research and application. Traditional fine-tuning methods require substantial data and computational resources, especially for enhancing specific capabilities, while in-context learning limited by the need for appropriate demonstrations and efficient token usage. Inspired by the expression of in-context learned capabilities through task vectors and the concept modularization, we propose ELICIT, a framework consisting of two modules designed to effectively store and reuse task vectors to elicit the diverse capabilities models without additional training or inference tokens. Our comprehensive experiments and analysis demonstrate that our pipeline is highly transferable across different input formats, tasks, and model architectures. ELICIT serves as a plug-and-play performance booster to enable adaptive elicitation of model capabilities. By externally storing and reusing vectors that represent in-context learned capabilities. ELICIT not only demonstrates the potential to operate modular capabilities but also significantly enhances the performance, versatility, adaptability, and scalability of large language models. Our code is publicly available
INTRODUCTION
Large Language Models (LLMs) have revolu- tionized the field of Natural Language Process- ing (NLP), demonstrating remarkable versatility in tackling wide array of tasks and real-world challenges (Devlin, 2018; Brown, 2020; Han et al., 2021; Achiam et al., 2023; Touvron et al., 2023). The power of these models lies in their ability to scamlessly integrate various capabil- ities, from logical reasoning (Bommasani et al., 2021) to common sense understanding (Talmor et al., 2018). In our rapidly evolving world, a crucial aspect of LLM is the ability efficiently adapt to new tasks or scenarios.
Evaluate the result o random Boolcan expression. Question: True False True is Answer:
True
Capability Library
(0)
False
Figure 1: Illustration of ELICIT, which dynamically retrieves and integrates task vectors from capability library to augment language model's performance on arbitrary queries, without increasing token usage during inference.
Traditional fine-tuning methods, while effective in enhancing specific model capabilities (Devlin, 2018; Thirunavukarasuu et al., 2023; Gururangan et al., 2020), often fall short in providing the necessary adaptability. These approaches are computationally intensive, leaving LLMs ill-equipped to handle the dynamic nature of real-world applications. In-Context Learning (ICL) (Brown, 2020) has emerged as promising alternative, allowing LLMs to adapt to new tasks without additional training by leveraging their inherent capabilities (Team et al., 2023; Vacareanu et al., 2024; Agarwal
These authors contributed equally this work.
1 Corresponding author. hhups://gttub..com www.h/uuuu.omcmmm.ommmooo INs-

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al., 2024). ICL, while effective, relies carefully crafted demonstrations, requires extra overhead for each inference, and interrupts the context, potentially limiting its efficiency and flexibility (Lu et al., 2021; Zhao et al., 2021; Dong et al., 2022: Liu et al., 2023b).
We envision a next step in LLM adaptation: Can we elicit and harness the potential of LLMs' inherent capabilities when adapting to new tasks, demonstrated by ICL, while simultaneously maintaining efficiency and and flexibility?
Our research explores this question by introducing novel approach ELICIT inspired by the concept of modularization (Pfeiffer et al., 2023; Fedus et al., 2022; Ding et al., 2023; Zhang et al., 2023b; Xiao al., 2024). ELICIT involves the establishment of a comprehensive library of task vectors, each eliciting one in-context capability within the LLM. For any arbitrary query text, ELICIT dynamically leverage this capability library to selectively clicit the capability, effectively and flexibly tapping into the model's s inherent capabilities on an as-needed basis. We empirically verify the advantanges of ELICIT under 20 tasks and 4 models:
Efficient Capability Elicitation: ELICIT aims to improve the model's task-specific capabilities with minimal additional computational cost during inference. Across 20 tasks and models, ELICIT achieves an average improvement of 11.4% over zero-shot performance while maintaining the same token usage (Section 4.2).
Flexible Task Handling: ELICIT can adapt to various tasks without requiring task-specific demonstrations or rigid templates, enhancing performance on both in-domain and unseen tasks (Sections 4.2 and 4.4).
Selective Capability Activation: ELICIT allows for targeted activation of specific model capa- bilities based on the input query. In our experiments with math-only capability library, ELICIT boosted Math performance dramatically (e.g.. from 2.6% to 21.3% for Mistral) while maintaining or slightly improving performance other domains (Section 4.3).
Complementary Integration: ELICIT shows potential for complementary use with existing methods, offering further performance gains. When combined with BM25 retrieval, ELICIT enhanced Pythia's ' average performance from 22. 1 % to 28.3% (Section 4.5).
Our key contributions are summarized as follows:
We introduce novel, modular framework for enhancing the adaptive capabilities of LLMs on demand with minimal computational overhead.
We conduct extensive experiments to evaluate our method, showcasing its effectiveness across different query formats, language models, and tasks.
We provide thorough analysis of our method, offering insights into the design choices and their contributions to overall performance.
2 RELATED WORK
In-Context Learning. While Brown (2020) introduced In-Context I Learning (ICL.) as a simple yet effecctive way to enhance LLM performance by incorporating demonstrations into prompts, its applications have rapidly expanded across diverse domains. ICL enables model to adapt to a wide array of tasks ranging from traditional NLP benchmarks more specialized tasks such as egression ( (Vacareanu et al., 2024), kNN classification (Agarwal et al., 2024; Dinh et al., 2022), and even jailbreaking (Anil al., 2024). Researchers have actively explored various avenues further enhance ICL's adaptability and effectiveness. These efforts include increasing demonstration quantity (Bertsch et al., 2024; Agarwal et al., 2024; Zhang al., 2023a; Team al., 2023), fine-tuning models for ICL (Min et al.. 2021), leveraging prompt engincering (Nic et al., 2022), and implenmenting demonstration retrieval (Liu al., 2021: Rubin et al., 2021: Li et al., 2023; Shi et al., 2022).
Concurrently, deeper insights into ICL'S underlying mechanisms have been sought through diverse perspectives. Some researchers view ICL as a process of compressing training data into -asc-specific vectors (Hendel et al., 2023), while others relate it to gradient descent (Von Oswald et al., 2023) or analyze it through the lens of repetition (Yan et al., 2023) and memorization (Golchin et al., 2024). Building upon ICL advancements, we explored eliciting and harnessing LLMSs inherent capabiliiies for new task adaptation, akin to ICL, while maintaining efficiency and flexibility.
Task representation for ICL. Inspired by findings that intermediate representations in LLMs encode semantic meaning (Zou al., 2023), researchers have explored injecting in-context learning
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demonstrations, encoded as function vectors, into intermediate representations to trigger desired predictions (Liu et al., 2023b; Hendel et al., 2023; Todd et al., 2023; Li et al., 2024). The scope of this research line has broadened to include different modalities, with recent work demonstrating its effectiveness in both visual (Hojel et al., 2025) and multimodal domains (Huang et al., 2024). However, this line of work focuses on manipulating internal representations. We are the first to comprchensively explore the modular approach of externally storing and retrieving such task representations to augment large language model capabilities.
Modular LLM. Examining and understanding the modular nature of large language models (LLMs) has become a crucial area of study for researchers (Pfeiffer al., 2023; Fedus al., 2022; Ding et al., 2023; Zhang et al., 2023b; Xiao et al., 2024). Initial investigations suggest that LLMs possess the capability to be broken down into distinct specialized components or modules. Some approaches introduce additional modules or parameters for optimization, including parameter-efficieet tuning techniques like adapter layers (Houlsby et al., 2019; Pfeiffer et al., 2020), prompt tuning (Liu et al., 2023a; Ding et al., 2021), and parameter subset optimization methods such as BitFit (Zaken et al., 2021) and binary masks (Guo et al., 2020; Zhao et al., 2022). Other approaches involve training dedicated models for task composition (Shao et al., 2023; Mu et al., 2024) or merging fine-tuned parameter adjustments (Ilharco et al., 2022: Panigrahi et al., 2023; Merullo et al., 2023; Yu et al., 2024). Inspired by such modular perspectives, we explore the question of using task vectors in modular way to dynamically elicit capabilities within the model.
3
METHOD
To elicit the hidden capability inside LLMs, we build our ELICIT by introducing a capability library which condenses each in-context learned capability into task vector, and utilizing retrieval module to strengthen the model when a task vector is helpful.
This section describes our implementation of ELICIT. We first formally define in-context learning Task Vectors (Section 3.1), and motivate our work. Then, we discuss the design choices of building capabilities libraries (Section 3.2), including the layer selection and intervention strategies. Finally, we introduce our retrieval module (Section 3.3) to dynamic elicit and leverage model's 's capability.
3.1 FROM ICL TO TASK VECTORS: FORMAL DEFINITIONS
In-Context Learning (ICL.. Firstly, we define the framework for ICI. Let T represent a collection of tasks. For each task E T, there exists dataset P, of in-context prompts. Each prompt p P is a sequence of tokens that represents the i-th prompt for task t. Specifically, each prompt P consists of two components: (1) set of N onput-output demonstrations D Nu))= from task t, where j indexes the sequence of pairs ranging from to N, and (2) query input Xig. which is distinct from the inputs in D. We formally represent an ICL prompt P as:
p
(X.N. ViN): xiy]
The Language Model (LM) aims to predict the corresponding target response Yig for the query input Xig. Through learning from the demonstrated input-output mappings in D, ICL can enhances the model's 's capability to perform this task. We firstly introduce the hidden state in Transformers below.
Task Vector. Previous research (Hendel et al., 2023) introduced the concept of a task vector in the context of ICL. We build upon this foundation in our work. We first introduce the definition of hidden state representations in transformer models and task vector is derived from it.
Let T be an auto-regressive Definition 3.1 (Hidden State Representation in Transformers).
transformer language model with L layers. For each layer i E l...., L, we define h, E Rd as the vector representation of the last token at layer I, The computation of hy follows the recurrence relation (Vaswani, 2017): b = h + m; + at where m, is the output of a multilayer perceptron at layer 1, and ai is the projection of the atlention oulput into the hidden state at layer I.
Having established the notion of hidden states in transformer models, we can now formally define the task vector within the ICL framework.
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Definition 3.2 (Task Vector 0). function by learning task-specific mapping from demonstra- tions. This mapping is represented as a task vector 0. The task vector is derived from the activation state by (as defined in Definition 3.1) at specific layer corresponding to the last token of the prompt. This vector subsequently steers the transformer to yield pertinent outpuis for given queries.
The task vector, as defined, encapsulates the essence of the task. This leads to the following lemma. highlighting its role in simulating ICL behavior.
Lemma 3.3 (Task Vector for ICL Simulation). Given a task vector 0 that effectively captures the information from demonstrations in ICL. setting, we simulate the behavior of regular ICL. with only query as follows:
TPp f(0;)in),
where:
-T(p)) represents the output the transformer model given ICL. prompt Pt, defined (1).
x(0xxa) denotes a fimctton that processes the query input Xg in zero-shot manner, guided by the information encoded in the task vector O.
Remark 3.4 (Intervention of Task Vector 0). The function f(0; (0)) mentioned in Lemma 3.3 is an abstract concept expressing that the task vector can be used to inffuence the model's s inference process. In practice, f0; xx) is implemented through operations on the hidden states b and the task vector 0. Specifically, these operations can take the following forms:
1. Replacement (Hendel al., 2023): The task vector 0 directly replaces the hidden state h4, i.e.,
h, O.
2. Linear combination (Todd et al., 2023): The task vector is combined linearly with the hidden state h,, i.e., h, = hi + a0, where a is an adjustable scalar parameter.
While previous research has demonstrated the existence and extractability of task vectors, it also has shown the potential for serving a technique to elicit the inherent capabilities when adapting to difference tasks as ICL, while simultaneously maintaing computational efficiency and flexibility.
We investigate the research problem through task vectors by proposing ELICIT, a framework designed to leverage these vectors for enhancing model capabilities. As shown in Figure 2, ELICIT consists of two main components:
Build Capability Library: capability library that stores task vectors representing various
in-context learned capabilities.
Dynamic Capability Elicitation: A dynamic retrieval module that selectively activates relevant task vectors based on the input query.
3.2 BUILDING CAPABILITIES LIBRARY
To investigate the idea of ELICIT, we first create a library of in-context learned capabilities { ((0 Each element in this library represented by task vector (as defined in Definition 3.2). Here, & denotes the number of ICL prompts for each task t, and we use k 10 for illustration. Definition 3.2 describes E 12d as a single layer's 's hidden state. our implementation, we collect E R- Lxd which includes representations from all L layers, to enable the exploration of various designs for the sequential components of ELICIT.
The implementation of creating capability library involves two critical considerations: ① Dynamic Layer Selection for I*, and ② Intervention Strategies. ! determines the appropriate layer i to intervene into during further reuse, utilizing the corresponding task vector, while 2 decides how to appropriately intervene the task vector influence the model's 's behavior (possible methods are described in Remark 3.4). Our framework addresses these considerations follows.
① Dynamic Layer Selection for 1". The selection of the optimal layer for task vector intervention crucial for maximizing the effectiveness of our approach (Todd et al., 2023; Hendel et al., 2023). Appendix B further illustrates the variation in the optimal layer across different tasks. We implement dynamic layer selection strategy to determine the optimal layer I* for task vector intervention. While using a validation set to identify the optimal layer is not novel concept, our contribution lies in addressing the challenge of determining the intervention layer when applying the library in our
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Build Capability Library
Dynamic Capability Elicitation
Arbitrary Query
Boa
Detemnine the result the provided
Boolean logic statement QueQuonti:::
(False orr
Gue
Rank
Filter
(0)
LM
Cupability Library
Augmented Response Vanilla Response
( 0 0,
Figure 2: Overview of the proposed ELICIT framework for Large Language Model Augmentation. ELICIT consists of two modular components: (1) Build Capability Library constructing library of spec-spe task vectors by learning from diverse task; (2) Dynamic Capability Elicitation dynamically retrieving and integrating relevant task vectors from library to augment the model's capability for arbitrary input query.
proposed pipeline: we equip each task vector with its corresponding optimal layer, pre-identified during the library construction phase, thereby enabling efficient and effective reuse of task vectors during inference. Our process is as follows:
We obtain the task vector 0 E Lxd by processing the ICL. prompt P (defined in (1), using randomly selected N 16 demonstrations). This done by extracting the hidden states (hr)t from all layers of the language model. We also store these ICL prompts for future use.
Using the validation set, we perform a layer-by-layer analysis:
For each layer I, we intervene 0₁ during zero-shot query processing.
We measure zero-shot accuracy on the validation set for each intervention.
We identify 1" the layer yielding the best accuracy.
We repeat this process for each task-model combination, creating a library where each task vector 0 has its corresponding best layer (*
When reusing task vectors in library, for any 0 E O we intervene at the pre-identified optimal layer I* for each task-model combination. This dynamic selection method ensures the performance of the task vector and provides a generalizable framework adaptable to different tasks.
2 Intervention Strategies. The concept of intervention, formally introduced in Remark 3.4, also outlines two methods for incorporating the task vector 0₁ into the model's 's inference process. We evaluate these two intervention strategies: 1) linear combination of the original hidden state and task vector with varying intervene strength and 2) direct replacement of the original hidden state with the task vector. We examine the impact these intervention strategies on both task performance and language modeling capability, with the latter measured using cross-entropy loss on the pre-training dataset (i.e, WikiText).
Figure provides detailed visualization of how varying affect both accuracy and cross-entropy loss in the Llama3-8B model across a diverse set of 20 tasks. Results reveal clear trade-off between task performance and language modeling capability
intervention strength increases. Among the strategies tested, the additive approach h, h + 2 0; consistently demonstrates superior performance across wide range of tasks while minimizing degradation in language modeling ability. Results for other models presented in the Appendix A, showing similar trends.
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Performance
CrossEntropy Loss
0.2
45
0.2
2.60
6.4
40
0.4
2.55
35
0.6
2.50
CrossEntrop Los
0.8
30
Accuracy
0.8
2.45
Replace 2.0 1.0
1.0
20
2.0
-15
Raplace3.0
10
2.25
Layer
Layer
Figure 3: Varying intervention strengths affect accuracy and cross-entropy loss in Llama3-8B on valid set of 20 tasks across different layer. Higher in intervention strengths improve average task performance across layers but negatively impact language modeling capabilities. This reveals trade-off between task-specific enhancement and general language modeling proficiency using task vectors.
In conclusion. Our library contains [T] items for each model, each consisting of three key components: (1) the ICL prompt pl, (2) the corresponding task vector O RRLxd and (3) the pre-identified optimal layer l".
3.3 DYNAMIC CAPABILITY ELICITATION
After the creation of the capability library, as described in Section 3.2, we consider two considerations: ① Relevant Task Vector Selection, and iii Threshold-Based Filtering. ① Relevant Task Vector Selection focuses on identifying the most relevant task vectors from the library for given test query g. We aim find the learning (ICL), we lack meta-information about the query. @i Threshold-Basse Filtering determines whether to utilize a retrieved task vector or not, to avoid compromising performance when no suitable task vectors are available in the library. Our framework addresses these challenges follows:
relevant task vectors 8 O stored the library. Unlike traditional in-context
① Relevant Task Vector Selection. We address the challenge of selecting the most relevant task vectors by employing binary classifier to calculate similarity scores. This classifier is built upon the SimCSE RoBERTa model?. augmented with 2-layer Multi-Layer Perceptron (MLP) head. The architecture incorporates ReLU activation functions and dropout rate of 0.2 for regularization.
We fine-tuned this model over 15 epochs using a learning rate of 2c-5 5 on our curated dataset of 10,000 examples. The trained classifier is then used to compute similarity scores between given query and each ICL prompt in our library. These scores are used to rank all library items, producing similarity list of size k [T]. The top-ranked task vector from this list is selected further processing.
Threshold-Bars Filtering. To determine whether utilize stored task vectors from our library, we implement a threshold-based approach using similarity scores. This threshold is established through comprehensive analysis of the preeisinen trade-off across our validation set, as illustrated in Figures 4a, utilizing the aggregated similarity lists for all samples. The AUC scores of our precision- -ecaal curves (i.e., 0.96) demonstrate the high effectiveness of our threshold-bard approach in accurately determining whether stored task vectors require intervention. Our evaluation of various recall levels, as shown in Figures 4b, reveals that a recall of 0.8 provides the optimal balance for our pipeline, other models' results shown in Appendix D.
Our decision process of how to utilize similarity list and threshold to choose whether to use the stored task vectors and what task vectors to apply is as follows:
We implement Dynamic Top-K Thresholding (DTT). the highest similarity score exceeds the threshold, select the top 10 task vectors from the ranked list for further processing.

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1.0
49.2
Intervene Accuracy
Zero-Shot Accuracy
49.0
Precision
Accuracy
0.6
48.8
0.4
48.6
0.2
48.4
(AUC 0.96)
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
Recall
Recall
(a) Precision-Recce Curves
(b) Valid Performance varying different recall.
Figure 4: Precision-Recal Curves and recall sweeping on Llama3-BB in valid set across 20 tasks. (a)
Precision-Recall curves for the retriever across 20 tasks (AUC=0.96), guiding threshold selection for high recall and precision. (b) Validation
accuracy after intervention using different recall thresholds.
Table 1: Models used in this work. We consider decoder-only auto-regressiv language models and recurrent neural networks that are capable of ICI.. For each model, we present the number of parameters, context window during training, and the number of layers [T].
Model
HuggingFace ID
Citation
Parameters
Train Length
Llama 3
meta- Meatt-llamm/Meta-LLiiaee --a-mam--ma-BB
Dubey et al. (2024)
8B
8k
32
Mistral Pythia
Bata///israaa---Baaaaa FleutherA//pythiaa2.88
Jiang et al. (2023)
7B
32k
32
Biderman et al. (2023)
2.8B
2k
32
Mamba
statesppaee//mamba22.8.hu
& Dao (2023)
2.8B
2k
64
We then employ a majority voting mechanism among the optimal layers suggested by these top vectors to determine the final layer for intervention.
In cases where the highest similarity falls below the threshold, refrain from using any stored task vector, relying instead on the model's base capabilities.
4 EXPERIMENTS
To comprehensively evaluate the effectiveness of ELICIT, we conduct a series of experiments designed to explore the following key questions:
Capability Elicitation Efficiency: Can ELICIT effectively elicit the model's capabilities without incurring significant additional computational costs?
Selective Activation: Is ELICIT capable of selectively activating relevant capabilities as needed for specific tasks?
Complementarity: How well does ELICIT integrate with and complement existing methods in the field?
Generalization: Can ELICIT handle novel queries, particularly those that diverge significantly from the task vectors currently stored in the library?
4.1 EXPERIMENT SETUP
Model. We utilize decoder-only auto-regressive language models (Pythia-2.8B (Biderman et al., 2023), LLaMA3-8B (Dubey al., 2024), and Mistral-7B (Jiang et al., 2023)) and recurrent neural network (Mamba-2.8B (Gu & Dao, 2023)). Table 1 provides a comprehensive overview of these models, detailing their key characteristics including the number of parameters, layer numbers, and training context window size. For all models, we use the corresponding huggingface implementations (Wolf et al., 2020).
Tasks. To assess the efficacy of our proposed pipeline across a diverse array of scenarios, we have meticulously constructed benchmark comprising 20 distinct tasks. This benchmark is designed to evaluate the model's 's performance on both classification and multiple-choice problems, spanning a wide spectrum of applications and complexities. The tasks categorized into five domains:
Knowledge: CommonsenseQQ (Talmor et al., 2018), OpenBookQA (Mihaylov et al., 2018),
HellaSwag (Zellers et al., 2019), and BoolQ (Clark et al., 2019);
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Table 2: Performance of ELICIT across model and tasks. ELICIT significantly enhances performance while maintaining the same token usage as Zero-shot, often achieving results comparable to better than 16-shot and 16-shot BM25 ICL retriever methods. This improvement is consistent across various models and tasks, demonstrating ELICIT's efficiency and effectiveness in boosting model capabilities without increasing computational demands. We sample 100 examples per task across three random seeds.
Model
# Tokens
NLU
Reasoning
Knowledge
Math
Safety
Avg.
16-shot
1883.8 = 0.9
60.6 1.0
5..0
70.6 1.0
26.7
62.1 = 0.4
55.2 = 0.4
bm25
2350.7 24.9
56.1
6,2
6.9
2
55.8 2.07
0.7
Llama3
Zero-shot
108.3 1.4
1.2
0.2
4.2
14.0 1.0
3
ELICIT
108.3 1.4
41.6
4.1
60.6 1.4
19.1
4 2.1
43.5 + 0.8
16-shot
2161.3
0.5
0.5
0.4
1.7
6
53.0 0.1
Mistral
bm25
2655.2
0.3
0.5
70.2
2..1
0.51
55.5 0.4
Zero-shot
123.5 + 1.7
29.6 1.2
26.9 0.4
45.5 1.3
2.1
3
28.2 0.5
ELICIT
123.5 1.7
41.9
4
5.9
2.9
48.7 1.8
43.7 + 0.6
16-shot
1942.4
5.5
1.1
12.8
9.2
0,9
24.7 = 0.2
bm25
2422.8
3.2
2.4
0..9
11.0
2.1
22.1 + 0.5
Pythia
Zero-shot
110.0 1.5
0.4
0.3
2.5
32,5 7.3
24.6 + 0.4
ELICIT
110.0 1.5
64.0
23.6 1.1
2.4
1.0
41.2 2.5
32.7 # 0.5
16-shot
1.42440.9
1.
0.5
31.6
14.2
4.2
31.3 0.3
bm25
2422.8 +:2.88222600
2..3
3.99
0.57
15.1
35.4 1.2
27.9 0.3
Mamba
Zero-shot
110.0 1.5
36.1
0,5
12.
5.8
30.1 0.1
21.7 = 0.2
ELICIT
110.0 1.5
51.1 + 0.7
28.7
29.2 + 1.3
15.3
48.2 # 1.8
34.5 + 0.6
Reasoning: Four subsets from Big-Bench Hard (BBH) (Suzgun et al., 2022) and ARC-
Challenge (Clark al., 2018);
Mathematics: MathQA (Amini et al., 2019) and MMLU Pro-MATH (Wang et al., 2024); Safety: Crows-Pairs (Nangia et al., 2020), BBQ-Age (Parrish et al., 2021), Sihics-Commonnense. and Ethics-Justice (Merity et al., 2016);
Natural Language Understanding (NLU): GLUE (SST-2, QNLI, MNLI) (Wang, 2018) and SuperGLUE (WIC, RTE) (Wang al., 2019).
Evaluation. To evaluate ELICIT under real usage scenario, where the demonstrations can hardly be at the same format with the test query, we augment the test query with two additional formats different from the demonstration in library. Furthermore, in our preliminary experiments, we find that zero-shot LLMs cannot answer properly with contextual guidance. Thus, to ensure a fair comparison with the zero-shot scenario, add task templates before the test query. More details and examples can be found in Appendix F.
Baselines. Our primary baseline the zero-shot performance of LLMs, as our method maintains the same token usage. For reference, we also include in-context learning (ICL) and BM25 (Robertson et al., 2009) retrieval of 16 examples from the same pool of examples used in constructing the capability library. However, these are not directly comparable to our method, due the raised nearly 20 times more tokens consuming. The ICI. baseline is task-specific, requiring knowledge of each query's task type use corresponding demonstrations. In contrast, method is task-agnostic, applicable across various tasks without needing task-specific information or prompts.
4.2 EFFICIENT CAPABILITY ELICITATION
Zero
ELICIT achieves efficiently eliciting models' capabilities. From Tables 2, comparing the zero-shot baseline and ELICIT, we observe that ELICIT significantly elicits model capabili- ties across most tasks without increasing token usage. Across the 20 tasks, ELICIT achieves an average improvement of 11.4% across different models. For Llama3, ELICIT im- proves over zero-shot by 12.3% while using the same 108.2 tokens. ELICIT demonstrates substantial gains Reasoning (e.g., +15.1% for LLama3) and Safety tasks (e.g., +14.4% for LLama3). In some cases, ELICIT's performance is comparable to or surpasses that of 16-shot and BM25 methods, despite their higher token requirements. Furthermore, it exhibits robustness across various template formats, highlighting its versatility.
ELKI
50
50
Accurscy
2.5
1oth NU Peoscreng Know cogs Softts
Figure 5: Performance on ELICIT across different domains when the li- brary only contains math-related task vectors on Mistral.
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Table 3: ELICIT can generalize to unseen tasks. ELICIT achieves significant performance gains without additional token usage across different models and unseen tasks. We sample 100 examples per task across three random seeds. We use BM25 retrieval of 16 examples as baseline.
Tokens
GLUR COLA
BBQ Religion
Decpmind
MML.U-Psychology
BBH-five-objett
Avg
BM25
2502.8 26.0
55.4
64.6+13
30.7
x3.0+0.1
48.3++0.0
56.4
Llama
Zero-shot ELICIT
103.6
38.6
2,
0.3
17.1
3
a
a.
4.7
2.
2..7
BM25
27.6
4
2.77
2.9
78.7
25.7
49.2 0.3
Mistral
Zero-shot
51.0
43.3
3.3
9.0
57.9
74
30.5 1.0
ELICIT
51.0
4..7=
42.1
2.2
68.6 +
1..6
38.0
BM25
2600.0 + 28.3
5.8 1.0
14.1 1.2
4.7 0.3
1.0
8.9 0.3
Pythia
49.6
1.7
ELICIT
106. 2 49.6
45.4 0.6
30,3 4.2
14.2 1.8
20.4 a 0.6
14.3
24.9 a 0.6
BM25
2600.0 28.3
4
30.6
21.6+33.
0.9
2.08
29.0
Mamba
Zero-shot
106.7 1 49.6
70.3 1.0
21.3299
1.79
13.9
6,2 0.2.1.00
24.5 a
ELICIT
106.7 = 49.6
63.6 0.4
31.5
2..1
0.2
1.04
30.4 0.9
4.3 SELECTIVE ADAPTIVE ACTIVATION OF CAPABILITIES
ELICIT elicits capability when necessary. We demonstrate selective activation by constructing a library containing only math-related task vectors, as shown in Figure The results clearly illustrate that ELICIT significantly boosts performance in the Math domain, with dramatic increase from 2.6% to 21.3%, while maintaining performance in other domains for Mistral. Notably, the Reasoning domain also shows slight improvement, increasing from 22.3% to 22.4%. This behavior stems from ELICIT's selective application of task vectors from library. which are not applicd when no relevant tasks vectors detected. More discussion is presented in Appendix J. Results for other models, presented in Appendix F, demonstrate similar trend. The striking improvement in Math performance, coupled with the subtle gain in Reasoning and the stability in other domains, demonstrates ELICIT's capacity for targeted capability activation, making it a flexible and efficient performance enhancer.
4.4 ELICIT GENERALIZE TO UNSEEN TASKS WITHOUT ADDTIONAL. INFORMATION
ELICIT generalizes across unseen tasks. In Table 3. observe that ELICIT significantly improves model performance on unseen tasks (GLUE-COLA, BBQ Religion, Deepmind (Saxton et al., 2019), MMLLU-Psycholog. and
not present in its capability library. Across all models, ELICIT consistently outperforms the Zero-shot baseline. In several cases, it even approaches or surpasses the BM25 retrieval baseline, despite using substantially fewer tokens. For instance, on the MMLU-Psychology task, ELICIT achieves 15.8% absolute improvement over Zero-shot for Llama3 Model. These results are achieved without additional token usage and task information, demonstrating ELICIT's efficiency, flexibility, and generalization ability.
4.5
COMPLEMENTARY INTERGRATION
ELICIT shows potential a plug-and-play performance booster. While ELICIT demonstrates compatibility with existing solutions like BM25 retrieval, Table 4 reveals nuanced performance patterns. For smaller models (Pythia-2.8B and Mamba-2.8B) combining ELICIT with BM25 yields consistent improvements, with Pythia's ' average performance increasing from 22.1% 1% to 28.3% (+5.9%). However, larger models (Llama3-8B and Mistral-7B) exhibit mixed results: while NLU and Reasoning tasks show modest gains (e.g., +2.6% for Llama3), Knowledge and Safety tasks experience slight declines. Aligning with the findings of et al. (2024), this phenomenon can be attributed to two factors: (1) smaller models' relatively weak in-context learning capabilities benefit more from additional task-relevant information provided by our method, while (2) larger models' inherently stronger in-context adaptation abilities may be disrupted by the introduction of additional context that alters their learned representations. Future work could investigate this scale-dependent phenomenon.
ABLATION STUDY
5.1
SIMILARITY-BASER RETRIEVE
We also explored similarity based retrieval methods, such as cosine similarity. t-SNE distance, and Euclidean distance between the query embedding and the task vectors capability library. However, illustrated in Figure 6, the precision-recall curves for these methods Llama3 exhibit

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Published as a conference paper at ICLR 2025
Table 4: ELICIT potential plug-and-play performance booster: performance when combined with BM25 on in-domain tasks. Results indicate stronger complementary effects smaller models (Pythia, Mamba). while larger models (Llama3, Mistral) show task-specific variations.
Model
NLU
Reasoning
Knowledge
Math
Safety
Avg.
Llama
BM25
56.1 1.5
68.8
69.5 0.9
28.0 2.3
56.7 2.0
55.8 0.7 53.1 1.1
BM25+ELICIT
58.0 0.4
62.7 #
65.1 = 0.5
25.1 = 1.6
+
Mistral
BM25
55.2 0.3
66.0 0.5
70.2 1.9
24.1 = 0.4
6.51
55.5 0.4
BM25+ELICIT
54.5 0.8
62.6 0.4
67.5 1.7
24.8 1.9
58.0 1.4
53.5 0.5
Pythia
BM25
33.3 2.2
25.8
12.9 0.5
11.0 1.8
27.3 2.1
22.1 + 0.5
BM25+ELICIT
26.5
14.5 0.6
13.2 1.7
0.7
28.3 + 0.3
Mamba
BM25
29.3 2.2
+.9
0.5
15.1 2.2
27.9 0.3 1.2
BM25+ELICIT
38.4 1.2
31.6
28.9 + 0.2
15.2 # 3.8
42.9 1.9
31.4 0.3
very low AUC scores, with the highest being a mere 0.28. These poor AUC values indicate that the discrimination ability of these similarity- based approaches is inadequate for effectively identifying relevant task vectors from the library. The precision-recd other models presented in Appendix G, further highlighting their suboptimal performance. In stark contrast, the trained retriever in our proposed design can achieve a remarkably high AUC of 0.96 (Figure 4a). This substantial improvement in retrieval performance underscores the benefits of our design, which effectively leverages learning-based techniques to intelligently retrieve and integrate relevant capabilities from the library.
curves for similarity-
methods on
0.0
6-5
Prectsion
Predsion
0.2
0.
6.
(AUC 2.2
(AUC 0.23)
PR 0.14)
Recall
Recall
Recall
(a) Cosine Similarity
(b) Euclidean Distance
(c) L-SNE Distance
Figure Precision-Re Recall Curves Similarity based methods for Llama3.
5.2 SELECTION AFTER THRESHOOLD FILTERING
In Table 5, we investigate the impact of the number of selected states n when the topl similarity score reaches the threshold, as well as the effect of using Dynamic Top-K Thresholding (DTT) or not as Section 3.3 mentioned. The results show that selecting n and using Dynamic Top-K Thresholding achieves best performance on the validation set. Choosing fewer vector quantities (c.g., n=5) would limit the method's potential, while selecting many (e.g., n=15) could introduce irrelevant noise, thereby degrading performance. Dynamic Top-K adaptively sets the similarity threshold ensure that only sufficiently relevant vectors are utilized. This ablation study highlights the rationale and effectiveness of our design choices.
Table 5: Ablation study on DDT on valid for Jlama3-8B.
TP
zs-shot
33.62
n=
44.71
n=15
45.02
n=
10,w/o DTT
45.17
==10
45.29
CONCLUSION
In this paper, we explore the vision eliciting and harnessing the potential of large language models' inherent capabilities when adapting to new tasks, akin to in-context learning (ICL), while maintaining efficiency and flexibility. We propose ELICIT, a novel framework consisting of two key modules: Build Capability Library and Dynamic Capability Elicitation. ELICIT achieves consistent improvements across diverse tasks, input formats, and model architectures. Our results show that ELICIT not only has the potential to harness models' latent abilities without introducing substantial additional computational cost, but also advances language models' performance, versatility, adaptability, and scalability.
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ACKNOWLEDGEMENT
This work was supported in part by the National Science and Technology Major Project (No. 20222D0115100 15101), Research Center for Industries of the Future (RCIF) at Westlake University, Westlake Education Foundation, and Westlake University Center for High-performance Computing.
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A INTERVENTION STRATEGIES
As mentioned in Section 3.2, we choose h, h, 0, as the intervention strategy, where a is scaling factor that controls the intervention strength. We observe the performance and cross-entropy loss across diverse set of 20 tasks by varying The results for Mistral, Mamba, and Pythia are shown in Figures 7, and respectively. The results reveal similar trade-off between task performance and language modeling capability the intervention strength increases. Among the strategies tested, the additive approach consistently demonstrates superior performance across a wide range of tasks while minimizing degradation in language modeling ability. Across different models, can be set to 2.0 to achieve good balance between task performance and language modeling capability.
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Performance
CrossEntropy Loss
0.2
35
0,2
2,4775
0.4
30
2.4750
CrossEntropy Loss
2.4725
Accuracy
0.8
20
2.4700
1.0
-15
2.4675
Replace3.0 2.0
10
2.4650
Replace3.00
2.4625
Layer
Layer
Figure 7: Varying intervention strengths affect accuracy and cross-entropy loss in Mamba on valid set 20
tasks across different layer.
Performance
CrossEntropy Loss
0.2
0.2
2.20
0,4
0.6
0.6
CrossEntropy Loss
30
2.10
0.8
Accuracy
0.8
2.05
1.0
20
2.00
2.0
2.0
1.95
Repi Replace3.0
Repp ace3
Layer
Layer
Figure 8: Varying intervention strengths affect accuracy and cross-entropy loss in Mistral on valid set of 20 tasks
across different layer.
Performance
CrossEntropy Loss
2.68
30
0.4
0.4
25
0.5
0.6
2.64
CrossEntrop Loss
Accuracy
0.8
2.62
1.0
15
1.0
R.place 2.0
-10
Replace3.0 2.0
2.58
2.56
2.54
Layer
Figure 9: Varying intervention strengths affect accuracy and cross-entropy loss in Pythia valid of 20 tasks across different layer.
A.1 POSSIBLE INTERPRETATION OF TRADE-OFF
The trade-off between intervention strength and language modeling performance can be explained through neural circuit interactions. Prior work suggests ICL and general language modeling operate with different circuits (Chan et al., 2022; Olsson et al., 2022; Singh et al., 2024). We hypothesize that stronger interventions redirect activation patterns from pretrained language modeling circuitry towards ICI-specific patterns, creating tension between these distinct computational paths. As inter-
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vention strength increases, activations appear deviate further from their pretrained configurations, potentially explaining the degraded performance on general language modeling tasks like Wiki- Text. This observation suggests that optimal intervention strength requires careful balancing rather than maximization. Further investigation of these circuit-level understanding remains an important direction for future research.
B BEST LAYER FOR DIFFERENT TASKS.
As mentioned in Section 3.2, identify the optimal intervention layer for different tasks. Figures 10, 11, and 12 illustrate that the optimal intervention layer varies significantly across tasks. For instance, the ARC challenge task achieves the best performance when intervening the middle layers, while the GLUE SST2 task performs best when intervening in the later layers for the Llama-3 model. Furthermore, intervening at different layers leads substantial performance variations. Therefore, instead of fixing the intervention layer as in Todd et al. (2023), we propose dynamic layer selection approach to identify the optimal intervention layer for each task.
efhics Jistcc
glun s417
bellowmag
math
mmki math
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b6h boolcan, expressions
180 datc understanding báh reasaning wwwutbbttoocoo colored ectsstill tomporol sequeences
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commonsense
ethics
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Figure Performance distribution varying intervention layer on Llama3 8B 20 tasks on Valid
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ethics, justic
glue
hellsswag
math
mmbu, pro
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s.porgluc rice
supergluc WK
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6oh, booleen. expressions
bbh date understanding ressoning about colored ectstooh temporal
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27-
20-
20-
25-
20
Bbg Lage
baolq
crown, pains
commonmeeeee qqe
Figure 11: Performance distribution varying intervention layer on Mistral 20 tasks on Valid Set.
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jusic_
glue
hellsswag
math
mmku, pro math
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Pr__hallenge
boh, booleen, expressions
bbh date understanding ressoning, about colored ectstooh
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bbg Lage
baolq
Crown, pairs
commonmweeeeeccoo qqe
Figure 12: Performance distribution varying intervention layer on Mamba in 20 tasks on Valid Set.
C DATA CONSTRUCTION FOR RETRIEVER
As mentioned in Section 3.3, train retriever on the constructed data. To train the retriever classifier, we construct data pairs to determine whether a task-specific prompt and an ICL prompt belong to the same task. For each task and each template, set. We create positive and negative pairs, where each pair consists of a task-specific prompt and an ICL prompt from the library. Negative examples are formed by randomly pairing task-specific prompts with ICL prompts from different tasks. We balance the data distribution across different tasks, ensuring that each task has an equal number of positive and negative pairs.
sample two examples from the validation
D RECALL SWEEP
As mentioned in Section 3.3, we sweep the threshold determined by recall, and as shown in Figure the results reveal that a recall of 0.8 provides an optimal balance between accuracy and recall for our pipeline across Pythia, Mamba, and Mistral models.
We observe a similar increasing trend in performance as recall increases for all three models. At lower recall values, the intervene accuracy is close to the zero-shot accuracy, indicating that the retrieved prompts may not be relevant to the task. As recall increases, the intervene accuracy improves significantly, demonstrating the effectiveness of the proposed approach in selecting appropriate prompts for intervention.
Based on these observations, choose recall value of 0.8 to determine the threshold for filtering prompts across different models, as strikes balance between maximizing accuracy and maintaining reasonable recall level.
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Accuracy vs. Recall
Accuracy vs. Recall
130
225
31-
Accuracy
Accurscy
32.0
21.5
32-
310
Intervene Accuracy
Zero Shar Accurcy
30.5
Intervenn Accuraty Zero Shot Accurcy
0.2
0.4
0.6 Recall
(a) Pythia
(b) Mamba
Accuracy vs. Recall
Accurocy
34
Accuracy Zem-SShet Acouncy
1.0
Recall
(c) Mistral
Figure 13: Accuracy vs. Recall curves for Pythia, Mamba, and Mistral models, illustrating the performance trade-off at different recall levels.
E EVALUATION SETTING
E. ICL SETTING
As 4.1 states, we first primarily implement ELICIT the traditional In-Context Learning (ICL) setting. We find that zero-shot Large Language Models (LLMs) cannot answer properly with contextual guidance. Although ELICIT works such a traditional ICL setting, shown in Table 6 and 7, the zero-shot accuracy is almost O, which not plausible to evaluate model's 's performance. Therefore, think it's not fair augment model performance such traditional zero-shot queries. Table Performance ELICIT across model and tasks ICI. setting.
model
#Tokens
NLU
Reasoning
Knowledge
Math
Safety
Avg.
16-shot
1553.4 1799.2 + 26.8
60.3 + 1.2
0.2
69.2220
27.0
6..9
54.6 = 0.6
BM25
0.3
54.7
0.2
3..
5..4
53.3 0.5
Llama
Zero-shot
8..6 8..6
0.0
21.6
0.2
0.0
3.1
10.2
Ours
4.55
45.2
5.16
1,97
43.6
40.9 0.1
16-shot
1779.0
59,5
$1.7
69.4
24.0
62.2
53.4 1.3
BM25
2045.0 27.9
57.5
5..8
66.2
23.8
51.7 5..2
Mistral
Zero-shot
100.9 1.7
0.1
22.6+0.8
1.99
0.7
2.7
8.6
Ours
100.9 1.7
31.8 0.5
4.64
4.7
17.0
41.8
36.6 0.1
16-shot
1581.1
52.7
21.7
13.7.11.
12.2
34.1
26.8 0.2 27.5
BM25
1848.6 26.4
4.79
2.8
2.82
12.3
36.4 1.0
Pythia
Zero-shot
88.3 1.5
0.2
0.3
0.4
0.3
2.0
0.7
Ours
88.3 1.5
4.83
2.12
11.1
15.2
36.9
26.5 + 0.8
16-shot
1581.1 1.3
4..4111
31.9111
3..2
15.0
32.5 40.9
BM25
1848.6 26.4
4..7
3..4
2..2
14.7
38.3
30.7 0.5
Mamba
Zero-shot
88.3 1.5
0.2
12.3
0.2
0.0
5.1
3,6 0.3
Ours
88.3 1.5
33.7 + 0.6
26.5 + 0.6
2..6
16.3
0.5
28.3 + 0.2
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Table 7: Unseen task for ICL Setting
Tokens
GLUB COLA
BBQ Religion
Deepmind
MMI.U-Psychooog
HRH--te-objects
Avg
83.7
LJama
Zero-shot
76.3 0.2
0.5
5.3
0.0000
62.7 0.5
0.0
1
Ours
76.5 06.5+00.2
0.5
24.3
19.3
66.0
0.0
23.2
BM25
1913.3 a 20.0
2
89.0
2.9
79.7
2..2
46.1
Mistral
Zero-shot
8.
1.7
10
13312
35.3
0.0
= 0.3
18.3
BM25
1747.6 20.2
13.7 19
33.7 1.7
20.3 2.6
6.2
a 1.2
pythin
Zero-shot
78.1 0.7
430
0.
0.3
3
0.0
=
Ours
78.1 0.7
37.0
4.6
6.2
BM25
1747.6 20.2
36.7
3..
25.3 4
21.2:+:0.0
2844 1.1
Mamba
Zero-shot
0.0
Ours
78.1 0.7
18.0 1.4
1..3
13.7 13.7.888
13.0 0.8
7.5
14.3 L.1
E.2 TASK SPECIFIC PROMPT
To ensure fair comparisons in zero-shot scenarios, we prepend task-specific prompts before each test query. These task-specife prompts are manually crafted following guidelines from 1m-harms reess (Gao et al., 2023b) and the chain of -toughttt hub The complete set prompts used provided in Figure 8.
Table 8: Task Specific Prompts for Various Tasks
Task
Prompts
bbh date understandding
Infer the date from context. Finish your answer with 'X' where X is the correct letter choice.
Question: {input)
Determine the date based on contextual clues. End your response with 'X', where X represents the correct option.
Question: (input]
Use the given context deduce the date. Conclude your answer with 'X', X being the right letter choice.
Question: finput]
bbh, boolean, booleam_expression expressions
Evaluate the result of random Boolean expression.
Question: iipptt]
Calculate the outcome of given Boolean expression.
Question: (input) Determine the result of the provided Boolean logic statement.
Question: {input]
Continued on next page
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Prompts for Various Tasks (Continued) Prompts
Task
bbh date understanding
Infer the date from context. Finish your answer with "X" where X is the correct letter choice.
Question: (input)
Determine the date based on contextual clues. End your response with 'X', where represents the correct option.
Question: (input]
Use the given context to deduce the date. Conclude your answer with 'X', X being the right letter choice.
Question: [input]
bbh booeeeanneeprrssioons
Evaluate the result of a random Boolean expression.
Question: (input)
Calculate the outcome of a given Boolean expression.
Question: {input) Determine the result of the provided Boolean logic statement.
Question: (input)
mmlu_pra pro prl__poooaa_____aa__p__pp math
The following are multiple choice questions (with answers) about math. Finish your answer with 'X' where X is the correct letter choice.
Question: (input) Below are multiple-choice math questions. Conclude your response with 'X' X being the correct option.
Question: [input]
Answer these math multiple-choice questions. Answer with 'X', where X the right letter choice.
Question: (input)
Continued on next page
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Prompts for Various Tasks (Continued) Prompts
Task superglue_re _tt
[input]
Question: Is the hypothesis correct based on
the premise? Answer True or False.
{input]
Question: Based on the premise, the hypoth- esis accurate? Respond with True or False. (input)
Question: Does the premise support the hy- pothesis? Answer True False.
bbh twwpeeeeaseeuuueeeeeeee
Answer questions about which times certain events could have occurred. Fimish your answer with 'X' where X is the correct letter choice.
Q: [input]
Determine possible occurrence times for specific events. Answer with 'X', X being the correct option.
Q: (input]
Identify when certain events could have happened. Conclude with 'X', where X is the right letter choice.
Q: /inptt
bbh, reasoning reasoning_abouttccom aawpauutcoooed_oopppeeoeeeooo
Answer extremely simple questions about the colors of objects on surface. Finish your answer with 'X" where X is the correct letter choice.
Q: [input]
Respond basic questions about object colors on surface. Answer with 'X', X being the correct option.
Q: (inptt]
Address simple queries regarding the colors of items on a surface. Conclude with 'X', where X is the right letter choice.
Q: (input)
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Prompts for Various Tasks (Continued)
Task ethics c
Prompts
[inptt]
Question:
Is this wrong? Answer Yes or No.
finput)
Question: Is this ethically incorrect? Respond with Yes or No.
[input]
Question: Does this violate ethical norms? An- swer Yes or No.
ethics_justice
(input]
Question: Would most people believe this rea- sonable or unreasonable to say? Answer Yes
or No.
[input]
Question: Would the majority consider this statement reasonable or unreasonable? Answer
Yes or No.
{input]
Question: Is this statement generally perceived reasonable or unreasonable? Respond with Yes or No.
glue mnli
[input] True, False or Neither?
(input) Answer selecting from: True, False, or Neither?
(input) Choose as answer: True, False,
Neither?
glue_sst2
finput
Question: Is this sentence positive or negative?
Answer positive or negative.
(input)
Question: Does this sentence convey posi- tive negative sentiment? Answer positive or negative.
finput)
Question: Is the tone of this sentence positive or negative? Respond with positive nega- tive.
glue_qnii
{input}
Does this response answer the ques- tion? Answer Yes or No.
Question:
{input)
Question: the response answer to the ques- tion? Answer Yes or No.
(input)
Does the given response address the question? Respond with Yes or No.
Question:
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Prompts for Various Tasks (Continued) Prompts
Task hellaswag
The following multiple choice questions (with answers) about commonsense NLI. Finish your answer with 'X' where X is the correct letter choice.
Question: (input]
The following are multiple-choice questions about commonsense natural language infer- ence. Answer with 'X", being the correct option.
Question: (input) Answer these multiple-choice questions commonsense language understanding. Conclude with 'X', where X is the right letter choice.
Question: (input]
crows_piie _piisp
(input)
Question: Which sentence is more stereotypi- cal? Answer A or B.
{input}
Question: Which sentence contains more stereotypical content? Answer A or B.
finput)
Question: Identify the sentence with stronger stereotypical elements. Respond with A or B.
boolq
{input}
Answer True or False.
{input]
Respond with True or False.
{input)
Is this statement correct? Answer True or False.
Continued on next page
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Prompts for Various Tasks (Continued) Prompts
Task
math_q9 _q
The following multiple choice questions (with answers) about math word problem. Finish your answer with 'X' where X is the correct letter choice.
Question: (input)
Below are multiple-choice questions about math word problems. Answer with 'X', X being the correct option.
Question: (input]
Solve these multiple-choice math word problems. Conclude with 'X', where the right letter choice.
Question: {input)
superglue_wic
{input]
Is the word used in the same way in the two sentences above? Answer Yes No.
Question:
(input)
Question: Is the word used similarly in both sentences above? Respond with Yes or No.
(input)
Question: Does the word have the same mean- ing the two given sentences? Answer Yes or No.
openbookqa
The following are multiple choice questions (with answers) about multi-step reasoning. Finish your answer with 'X' where X is the correct letter choice.
Question: (input]
The following are multiple-choice questions testing multi-step reasoning. Answer with 'X,, X being the correct option.
Question: (input]
Answer these multiple-choice questions in- volving multi-step logical thinking. Conclude with 'X', where X is the right letter choice.
Question: [input]
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Prompts for Various Tasks (Continued) Prompts
Task commonsense_ge _q
The following multiple choice questions (with answers) about commonsense knowl- edge reasoning. Finish your answer with 'X' where X is the correct letter choice.
Question: {input)
Below are multiple-choice questions about commonsense reasoning. Answer with 'X', X being correct option.
Question: [input]
Respond these multiple-choice questions commonsense knowledge. Conclude with 'X', where X is the right letter choice.
Question: {input)
bbq_age
The following multiple choice questions (with answers) about social bias on age. Finish your answer with "X" where X is the correct letter choice.
Question: [input]
The following are multiple-choice questions about age-related social bias. Answer with 'X', being the correct option.
Question: (input)
Answer these multiple-choice questions social bias regarding age. Conclude with 'X', where X is the right letter choice.
Question: {input)
are challenge
The following are multiple choice questions (with answers) about science. Finish your answer with "X" where X is the correct letter choice.
Question: (input]
Below are multiple-choice science questions. Answer with 'X', X being the correct option.
Question: innptt]
Respond these multiple-choice questions on scientific topics. Conclude with 'X', where X the right letter choice.
Question: (input)
Continued on next page
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Prompts for Various Tasks (Continued) Prompts
Task glue_cola
(input]
Question: Does this sentence make sense? An-
swer Yes or No.
{input]
Question: this sentence logically coherent? Respond with Yes or No.
finput)
Question: Evaluate if this sentence is meaning-
ful. Reply with Yes or No.
Loh_logical deduction Derduuction_ive_olpee fbve_objects
A logical deduction task which requires deducing the order of a sequence of objects. Finish your answer with 'X' where X is the correct letter choice.
Question: (input)
This challenge involves logically determining the sequence of set of objects. Conclude your response with 'X', where X is the appropriate letter option.
Question: (input)
In this logical reasoning exercise, deduce the correct order of series of objects. End your answer with 'X' X being the right letter choice.
Question: (input)
mmlu hww.biigghhhhhoo_sschhhlleee
The following are multiple choice questions (with answers) about high school psychology. Finish your answer with 'X' where X is the correct letter choice.
Question: (input)
Below are multiple-choice questions testing high school level psychology knowledge. Con- clude your response with 'X', representing the correct option.
Question: (input)
These questions assess understanding of high school psychology concepts. End your answer with 'X', where X is the letter of the correct choice.
Question: (input)
Continued on next page
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Prompts for Various Tasks (Continued) Prompts
Task obq_religion
The following multiple choice questions (with answers) about social bias on religion. Finish your answer with 'X' where X is the correct letter choice.
Question: (input)
Here are multiple-choice questions addressing social biases related religion. Conclude your answer with 'X', being the correct letter option.
Question: (input)
These questions explore social biases in the context of religion. End your response with 'X', where X represents the right letter choice.
Question: {input]
deepmind
The following are multiple choice questions (with answers) about algebraic word problems. Finish your answer with 'X' where X is the correct letter choice.
Question: {input)
Below are multiple-choiee questions testing algebraic word problem solving skills. Con- clude your answer with 'X', X being the correct option letter.
Question: [input]
These questions assess your ability to solve algebraic word problems. End your response with 'X' where X is the letter of the right choice.
Question: [input]
F ADAPTIVE ELICITATION
As mentioned in Section 4.3, we show that when provided the library with only math-related task or slightly improving in other domains for Mistral. Figure 14 illustrates similar results on other models such as Mamba, Pythia, and Llama3.
vectors, performance shows significant improvement the math domain while retaining
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40 200-88
Zaro-stet
Zec-stoc
30
43
380 61-15.
215-2244
20
10
(a) Mamha
(b) Pythia
(c) Llama3
Figure 14: Performance on ELICITacross different domains when the li-brary only contains math-related
taskvectors on Mamba, Pythia, and Llama3.
G SIMILARITY-BASED RETRIEVE METHOD
Section 5.1 demonstrates the poor precision-recall performance of similarity-base retrieval methods on the Llama3 model. Figure 15 presents the Precision- Recall curves for Mistral and Mamba under proposed
different similarity- -bsed approaches, which also exhibit poor results. In contrast, retrieval module achieves significantly higher precision and recall across all models. This highlights the effectiveness of our method in accurately retrieving relevant task vectors to support different tasks.
0.0
0.0
0.0
Precision
Recall
Recall
Recall
(a)
(b)
(c)
1.0
3.0
Preciaion
0.2
0.2
M. curve (AUC 6.43)
curve (AUC 0.08)
PB curve (AUIC 0.13)
56
0.
64
54
64
02
Recall
Recall
Recall
(d)
(c)
(f)
Figure 15: Precision-Recall Curves Similarity-based Prompt Retrieval Methods on Mistral and Mamba Models. Top Row: Mistral, Bottom Row: Mamba. From Left to Right: Cosine Similarity, L-SNE, and Distance- based Methods.
H ORTHOGONAL AUGMENTATION IN UNSEEN TASKS.
As shown in Section 4.5, ELICIT demonstrates its plug-and-play capability by seamlessly integrating with existing methods such as BM25 Retrieval for in-domain tasks. Table showcases ELICIT combined with BM25 in unseen tasks, combined with BM25 for unseen tasks, where observe performance improvements across various models and tasks. This highlights the versatility and effectiveness of ELICIT in augmenting existing methods and tasks.
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Table 9: ELICIT combined with BM25 tasks. Improvements concentrated in smaller models (Pythia, Mamba), while larger models exhibit task-specific trade-offs (e.g., Llama3 shows gains Deepmind but declines in
Model
GLUE COLA
BBQ Religion
Deepmind
MMLU-Psychology
BBH-Are-objects
Avg
Llama
BM25
$5.4
64.6 1.3
x3.0
48.3
$6.4
BM2S+ELICIT
47.6 2.2
60.6 1.0
26.4 a 1.0
4.04
52.1 a
Mistral
BM25
4.2
70.7 = 0.7
26.6
78.7
2.07
49.2
BM2S+ELICIT
36.4 1.1
$9.4 1.8
25.2
7.3
26.9
4..7..0..
Pythia
BM25
5 1.0
19.1 1.2
14.1 1.2
4.7
1.0
8.9
RM2S+ELICIT
Mamba
BM25
4.1
30.6
2.3
1
25.8 0.0
29.0
BM2S+ELICIT
46.6 +
30.9 1.8
22.7 +
22,7 + 0.4
21.8 +
28.9 + 0.5
DATASET SPLITS
We provide detailed information about our dataset curation and splitting strategies to ensure re- maintain robust evaluation capabilities while ensuring
producibility. Our primary objective sufficient training data for ICL prompt construction. For datasets with pre-existing splits (ARC- Challenge, Ethics. GLUE, MathQA, OpenbookQA), we preserved the original partitioning. When handling datasets with only train-valid splits, we employed two approaches: for those with validation sets exceeding 350 samples (e.g., BoolQ, Hellaswag), we split the validation set into new validation and test sets at 7:3 ratio; for those with smaller validation sets (e.g., Commonsense(A)) we divided the training set into new train and test sets (7:3). For test-only datasets, we implemented different strategies based on size: smaller datasets like BBH (250 samples) were split to ensure 128 samples for training and 80-100 samples for testing, with remaining samples allocated to validation. Larger test-only datasets (>1000 samples) such as MMLU-PER LEL-UUPro-aah BBQ, and Crows Pairs were split into muin-valid-test sets at 7:2:1 ratio. The same 7:2:1 split was applied to train-only datasets like SuperGI .UE and DeepMind. This systematic approach ensures minimum of 80 test samples for reliable evaluation metrics and at least 128 training samples for ICL, prompt construction across all tasks.
J ANALYSIS OF ELICIT'S SELECTIVE ACTIVATION
We investigate why ELICIT selectively activate capability in Figure and the importance of this mechanism. Using library containing only math-related task vectors on Mistral, we analyzed the number of chosen states per domain, shown in Table 10. Math-related tasks showed consistent high utilization (9.8 + 0.1 chosen states), while other domains maintained minimal selection (approximately 0.0). This pattern confirms that ELICIT's performance improvements stem from its dynamic retrieval and selective activation of relevant capabilities.
Table 10:
The average number of chosen numbers per domain per sample. The statisties come from Mistral when the capability library only contains math-relater task vectors.
in-domain
NLU 0.0 0.0
Reasoning = 0.0
Knowledge 0.0 0.0
Math 0.1
Safety 0.0
chosen nums
Out-of-domain
GLUE COLA 0.0 + 0.0
BBQ Religion
Deepmind 9.9 0.1
MMLU--sycchlogy
BBH-five-ojects
chosen nums
0.0
0.0 0.0
0.0 0.0
We observed minor improvements reasoning tasks, exemplified by this ARC Challenge case in Table 11. It demonstrates our pipeline's 's ability to selectively activate relevant capabilities based solely on query and handle unseen tasks flexibly, without requiring explicit task information.
Experiments forcing the application of top task vectors all queries (Table 12), showed significant performance degradation in NLU and knowledge tasks, highlighting the importance of selective activation.
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Table 11: A successful from Arc- Are-Challenge when capability library only contains math-related task vectors on Mistral.
Below multiplc-choice science questions. Anestions.Answer with 'X', X being the correct option.lnnnQueetee kntnQuestion: An unbalanced
equation the reaction methane gas (CH_[4) with oxygen
input
is shown below. CH_(* [_] + \Box O_ (2) -s200 (2) + 4H_(2]0
How many molecules of oxygen (O_(2)) are needed to properly balance this equatiion''OOOptionsslAAA NnB., 2nn.. nn. AnAnswer:
chosen task vectors
10 task from MathQA
Original Output
B
ELICIT Output
D (correct)
Table 12: The results of forcibly applying the top task vectors for each query. The experiments conducted on Mistral. Domains with degraded performance are marked bold.
nlu
reasoning
knowledge
math
safety
Zero-shot
28.8
27.4
58.8
4.0
42.2
ELICIT 15.7
31.4
47.8
18.3
53.1
These experimental results demonstrate that ELICIT's performance improvement stems from its selective activation mechanism and the importance of selectively using only task-relevant vectors to dynamically activate capabilities.
We analyzed the usage frequency of task vectors in the capability library, which contains 20 distinct task vector types. The analysis was performed Pythia-6.9B while evaluating 25 tasks in total: 20 in-domain tasks and out-of-domain tasks. Our findings confirmed that all 20 task vector types in the library were utilized during the evaluation.
Usage Frequency Distribution of different task vectors
bbq
math
10230
openbookqa
commonsense_ee qa
ethics justssse
9610
glue_ma mnli
9359
Type of Task Vector
hellaswag
9040
athics rommonsense
9000
mmlu_pr math
8330
challenge
8000
boole
glue qnli
7550
date understanding
7780
temporal, sequences
_emporai_squuences
7200
bbn boolean expressions
crows_pairs
7160
supergiue_ne
6911
jeeessoningggbuuue salpligsssooooooooooooeeeeeeeeee colored_ objects colored_objectss
$390
giue_sst2 3st2
superglue_we wic
18000
2500
5000
7500
1000C
12500
15000
17500
20000
Usage Frecuency
Figure 16: Usage Frequency Distribution of Different Types of Task Vectors Across all In Domain and OOD -Outaif-Domain) Samples. The results is based Pythia-6.9B.
32

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Published as a conference paper at ICLR 2025
K
MORE MODELS AND TASKS ON ELICIT
Beyond our primary experiments, we evaluate the scalability and generalizability of ELICIT across larger language models and more challenging tasks. As shown in Table 13, ELICIT maintains its performance advantages when applied to more base models. Furthermore, Table 14 demonstrates that ELICIT achieves consistent improvements across diverse set of complex tasks, validating its effectiveness and versatility.
We further explore the applicability of ELICIT to instruction-tume models, with preliminary results shown in Table 15, While this initial experiment suggest the potential compatibility of ELICIT with instruction-tume -ttiiio models, several challenges remain. Instruction eastructionttuned models exhibit heightened sensitivity to prompts and instructions (Sun al., 2023; Gao et al., 2023a), necessitating more investigation and analysis. Key challenges include identifying effective task vectors for in-context learning (ICL) and developing robust methods for zero-shot performance evaluation. We leave the comprehensive adaptation of ELICIT for instruction-tunet models as promising future work.
Table 13: Performance of ELICIT on more different models. ELICIT are effective for larger models.
Length
nlu
reasoning
knowledge
math
safety
avg
109.8
+ 0,4
16.1
17.4
5.9 0.7
31.7
21.8 + 0.1
Pythia-6.9B
ELICIT
109.8
38.7 1.4
28.1
27.9
4
32.2 0.7
109.8
34.7 = 0.6
0.2
18.1 = 0.6 29.8
7.9
34.6
23.2 0.2
Pythia-12B
ELICIT
+ 0.5
0.7
17.5
46.8
32.5 + 0.5
101.1
50.9
66.8
59.7
37.6
44.2
51.8
Llama3-70B
ELICIT
101.1
80.5
84.6
52.4
67.4
Table 14: The results of ELICIT on GSM8K and MMLUUPrrofsssoonll-Laaa on Llama3-8B. GSM8K is as in-domain task and OMMLU-Profeesional--.. out-of-domain.
GSMSK
MMLLU-rrofssional-LLay
zs ELICIT
30.44
31.67
32.44
41.11
Table 15: The preliminary experiment of ELICIT on Llama3-8B-Instren
nlu
reasoning
knowledge
safety
avg
zs
45.0
4.9
31.9 70.9
42.5
31.1
ELICIT
52.7
36.2
49.0
52.2
L DIVERSITY- CAPABILITY LIBRARY
We conduct an experiment on maximizing the diversity of prompts in the given capability library. Instead of random demonstration selection, we construct new capability library of diversity- optimized prompts as described Su et al. (2022).
Spefically, we used Sentence-BERT to generate embeddings by averaging the resulting vectors over the words each text input. For each task, after computing embeddings for all training data, we implemented an iterative approach to find diverse examples to construct ICL prompts. Starting with a random example, we selected examples that maximized the distance from previously chosen examples in each iteration. We then conducted new capability library using these more diverse ICL prompts.
As shown in Table 16, the diversity prompts yielded mixed results. Compared to the original ELICIT, while performance improved in reasoning (+1.1%), math (+0.5% and NLU tasks (+4.5%), there decline in Knowledge (-5.9%) and Safety (-2.3% ability.
This result suggests the potential for future work to improve our pipeline by enhancing the quality of task vectors through better demonstration selection methods.
33

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Published as a conference paper at ICLR 2025
Table 16: The comparison of ELICIT using different capability library based different ICL prompts. The experiments are conducted Llama3-8B.
NLU
Reasoning
Knowledge
Math
Safety
Avg.
Zero-shot
32.2 # 1,2
32.9 = 0.2
42.5 + 1.2
14.0 1.0
35.5 + 1.2
31,4 + 0.7
ELICTT
38.1 0.9
46.1 = 0.3
60.7 1.2
19.4
49.4 2.1
42.7 + 0.8
ELICIT (diversity)
42.6 0.3
47.2 # 0.1
54.8 1.5
19.9 = 0.8
4.6
42.3 0.9
M MULTI-LAYER INTERVENTION
While our primary analysis focuses on single-layer intervention, we also conduct preliminary exper- iments on multi-layer intervention. with the intervention strength a 2 distributed evenly across layers. We evaluated four settings: (1) the zero-shot baseline, (2) intervention on three consecutive layers (centered on the previously identified optimal layer), (3) intervention across all layers, and (4) our original single-layer implementation.
Results from Llama3-88 (Table 17) reveal an intriguing pattern: distributing intervention across multiple layers tends to yield better performance. This observation opens promising directions for future research into the mechanisms and benefits multi-layer interventions.
Table 17: Comparison of multiplc intervention layers ELICIT. The experiments conducted on Llama3-8B.
nlu
reasoning
knowledge
math
safety
avg
32.4
31.8
42.8
15.4
36.6
31.8
ELICIT (1 layer)
38.3
46.9
60.7
20.6
51.1
43.5
ELICIT (3 layers)
38.2
47.1
61
21.6
51.6
43.9
ELICIT (all layers)
40.9
46.3
61.4
21.7
52.4
44.5
N ANALYSIS OF COMPUTATIONAL EFFICIENCY WITH RETRIEVAL MODULE
To demonstrate the effciency of ELICIT, We conducted detailed analysis of ELICIT's computational efficiency using the Pythia-6.9B model, measuring the average processing time per sample across different pipeline stages. The results are shown in Table 18. Our quantitative results demonstrate that the integration of the retrieval module maintains the method's efficiency. Specifically, the retrieval module adds only 005 seconds of computational overhead per sample. The total inference time, including retrieval operations, remains efficient 0. 172 seconds per sample. ELICIT demonstrates superior efficiency compared to baseline approaches, processing samples 2-3 times faster than both 16- shot inference and BMM2-based inference methods. These results validate that ELICIT's performance improvements do not come at the cost of computational efficiency, even with the addition of retrieval module.
Table 18: The running time of different stages per sample across different domains.
inference time
ELCTT inference time
retrieve time
bm25 inference time
166hhot inference time
0.063
0.064
0.097
reasoning
0.065
0.066
0.104
0.349
0.315
knowledge
0.066
0.069
0.108
0,517
0,371
math
0.065
0.067
0.111
0.351
0.352
safety
0.067
0.104
0.611
0.366
avg
0.065
0.067
0.105
0.426
0.317
34


Reviews:
[{"review_id": "9Jaqt2jx2N", "reviewer": "Reviewer_BquA", "summary": "This paper proposes ELICIT, a framework that stores the task vectors corresponding to different in-context-learning (ICL) prompts and dynamically augments the given question with retrieved task vectors to provide ICL abilities without explicitly forwarding the long ICL prompts. ELICIT shows comparable or better performance than ICL baselines on diverse tasks while being significantly more efficient.", "questions": "1. How are the ICL prompt $p_i^{t}$ chosen? (e.g., Are they a group of randomly selected examples?) If there is a technique to maximize the diversity of the prompts in a given library, would it also boost the model performance?\n2. Figure 5 shows that ELICIT boosts performance on relevant tasks while minimally compromising performance on non-related tasks. Is this because task vectors are not applied for unrelated tasks, or does the model perform well even when unrelated task vectors are given?\n3. The current framework augments (at most) a single task vector at a single layer. Could this be extended to multiple vectors or layers to improve performance? (e.g., in cases where a given query is highly relevant to two different task vectors)\n4. Tables 2 and 3 show that ELICIT performs significantly better than the ICL baselines for Pythia and Mamba. What would be the reason for this? For example, can ELICIT be more beneficial for models with weaker capabilities? Or could this be related to a specific training recipe, as Pythia and Mamba are trained under the same setup?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The writing is clear and easy to follow. The motivation and design of each component is straightforward to understand.\n2. Dynamically augmenting task vectors is significantly more efficient than in-context learning while showing competitive or even better performance.\n3. The proposed approach can be applied to existing LLMs in a plug-and-play manner, making ELICIT easy to deploy.", "weaknesses": "1. Some details regarding the experiment setup need to be included. For example, the paper does not describe how the ICL prompts $p_i^{t}$ are chosen.", "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": "BL44YutsoT", "reviewer": "Reviewer_nS2i", "summary": "This paper proposes ELICIT. It's a framework aims at improving LLMs capabilities by introducing an external ICL capacity library. This library stores task vectors, which represent in-context learned abilities, enabling models to retrieve relevant skills dynamically without additional training tokens or fine-tuning. The approach allows LLMs to handle diverse tasks by selectively activating specific capabilities when needed, thus improving both versatility and computational efficiency.", "questions": "See weakness above", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The paper comes up with an interesting and intuitive solution to improve LLMs' abilities using the task vectors.\n2. Extensive experiments over various models and tasks.\n3. Exprimental results show great advantage of the method over others.\n4. This novel plug-and-play framework could benefit other methods on the same task.", "weaknesses": "1. I would expect the proposed ELICIT method to be integrated into more existing strategies such as few-shot learning.\n2. Have you tried using the capacity bank with other creation method other than ICL?", "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": "dwomf2a10v", "reviewer": "Reviewer_yoPv", "summary": "The article introduces ELICIT, a novel framework to enhance the adaptive capabilities of large language models (LLMs) without the need for extensive fine-tuning or in-context learning demonstrations. ELICIT consists of two key modules: a capability library that stores task vectors representing various in-context learned capabilities, and a dynamic retrieval module that selectively activates these vectors based on input queries. Experimental results demonstrate the effectiveness of the model.", "questions": "1. Why isn't the experiment conducted on instruction-tuned models but base models?\n2. Did you compute the decrease in inference efficiency caused by the introduction of a new module?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The new framework with the use of task vector demonstrates effectiveness in improving zero-shot performance.\n2. The paper is generally well written and easy for readers to understand.", "weaknesses": "1. The novelty is limited in some aspects, including the use of task vector and the retrieval module. \n2. Experiments on different models of different sizes should be conducted as the study would better demonstrate that this method is also effective for large models.\n3. More comprehensive experiments on more datasets are expected, such as MMLU, GSM8K, HumanEval, etc.", "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": "QUPffa9jig", "reviewer": "Reviewer_dv9u", "summary": "This paper introduces ELICIT, a novel framework for adapting Large Language Models (LLMs) to diverse tasks, using task vectors and in-context learning (ICL). Inspired by the concept of modularization, ELICIT comprises two core components—Build Capability Library and Dynamic Capability Elicitation. By building a library of task vectors, each representing one in-context capability, ELICIT dynamically leverages this library to selectively retrieve and activate capabilities based on any given query, ensuring efficient and flexible elicitation of the model’s capabilities. \n\nTo build the capability library, task vectors are stored across layers for each task, along with prompts for future reuse, and the position of the optimal layer, which is determined based on the hold-out validation set and zero-shot inferences with task vector interventions to the model. Interventions within the model can be as direct replacement or a linear combination, with the latter shown to perform better. Later, task vectors are retrieved with the Dynamic Capability Elicitation module that employs a SimCSE RoBERTa model for relevant task vector selection and a threshold-based filtering approach based on AUC scores from the validation set.\n\nELICIT shows good performance across 20 ICL tasks and four models, outperforming other baselines across tasks, models and query formats, while also showing good generalization on unseen tasks.", "questions": "While the paper presents an interesting direction within ICL with modular task vectors and shows better performance than zero-shot and classical ICL, further improvement of clarity, related work comparison, and validation dependency could strengthen the paper even more. I would recommend acceptance if the authors address these points and the questions within the weakness section and the following one:\n- Have you tried aggregating task vectors by task, and if so, what were the results?\n- Have you considered using multiple layers to represent task vectors instead of relying on a single optimal layer? If yes, how did this affect performance?\n- Can you please clarify if only the most similar task vector is used for intervention in the end? If so, does this mean many task vectors remain unused?\n- Can you include comparisons to related task vector approaches from previous ICL research you mentioned in the related work? Can you also compare your method against PEFT modular methods with a few-shot regime?\n- How well does your approach scale to larger LLMs?\n- Can ELICIT be extended for multi-modal applications or other, more complex tasks?\n- Does ELICIT support compositionality, such that combining different task vectors can represent new tasks?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **S1:** Paper explores a novel and promising direction for in-context learning by leveraging task vectors with an external modular concept which is really interesting and aligns well with the recent work in the field of in-context learning and task-vectors.\n- **S2:** Proposed method ELICIT demonstrates strong performance and generalization across diverse tasks and models.\n- **S3:** This paper has a clear mathematical presentation with good explanations of ICL and task vectors.", "weaknesses": "- **W1:** The paper lacks specifics on dataset curation regarding the train-val-test splits and sizes, as well as the sample sizes used to calculate the performance. Can you please clarify how many examples were used for the task calculation? \n- **W2:** ELICIT selects the optimal layer for task vectors for each given task and prompt, but assuming that the whole task encoding is stored just within a single layer may not be optimal. Have the authors tested multi-layer interventions, and if so, how did they compare? Moreover, could grouping and averaging task vectors per task simplify optimization steps and yield efficient task vector retrieval without extra steps?\n- **W3:** The paper does not compare with previous task vector approaches mentioned in the related work (Hendel et al., Todd et al., Liu et al., Yan et al.), as well as with other parameter-efficient fine-tuning methods based on modularity (also mentioned in the related work like Hu et al.) with 16 shots. I believe such comparisons should be performed and included in the paper. Additionally, the paper does not mention task vector work for visual ICL and multi-modal ICL [1, 2] and including these would provide a comprehensive context and overview of the field. \n- **W4:** Figure 3 illustrates the trade-off between stronger interventions and language modeling performance on WikiText, which is an expected observation since ICL and general language modeling operate with different circuits [3, 4, 5]. Having stronger interventions steers the activations further from the pretrained task, thus resulting in the worse performance, which is what previous works also showed. Authors do not analyze or explain this observation, but just comment how the strength of interventions affects the ICL and language modeling performances. I believe further discussion and explanation should be included.\n- **W5:** The proposed method relies heavily on validation data to select optimal hyperparameters and determine the filtering threshold. And the similarity-based model for task vector retrieval is further trained. Does relying on the validation tuning affects the scalability and efficiency? Can you please explain how this similarity model was trained, and with what data?\n- **W6:** Tables 1 and 2 contain additional bolded entries, and captions are not descriptive enough, missing information about the sample size for the BM25 and evaluation in general. Further, figure 6 does not have a clear explanation of components labeled a, b, and c, while also missing a description in general. Finally, there is a typo in the appendix in the title for the Similarity based retrieval methods.\n\n[1] https://arxiv.org/abs/2404.05729\n\n[2] https://arxiv.org/abs/2406.15334\n\n[3] https://arxiv.org/abs/2205.05055\n\n[4] https://arxiv.org/abs/2404.07129\n\n[5] https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html", "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)