AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution

Authors: Fengyuan Liu, Nikhil Kandpal, Colin Raffel
Primary area: interpretability and explainable AI
Keywords: Large Language Model, Context Attribution, Interpretability

Abstract:
The influence of contextual input on the behavior of large language models (LLMs) has prompted the development of context attribution methods that aim to quantify each context span's effect on an LLM's generations. The leave-one-out (LOO) error, which measures the change in the likelihood of the LLM's response when a given span of the context is removed, provides a principled way to perform context attribution, but can be prohibitively expensive to compute for large models. In this work, we introduce AttriBoT, a series of novel techniques for efficiently computing an approximation of the LOO error for context attribution. Specifically, AttriBoT uses cached activations to avoid redundant operations, performs hierarchical attribution to reduce computation, and emulates the behavior of large target models with smaller proxy models. Taken together, AttriBoT can provide a 300x speedup while remaining more faithful to a target model's LOO error than prior context attribution methods. This stark increase in performance makes computing context attributions for a given response $30\times$ faster than generating the response itself, empowering real-world applications that require computing attributions at scale. We release a user-friendly and efficient implementation of AttriBoT to enable efficient LLM interpretability as well as encourage future development of efficient context attribution methods.

Decision: accept (poster)
Reviewer scores: 6, 6, 8, 6, 6, 6, 6

Reviews and discussion:
<thread>
creation date:
1730557141628

thread_type:
official_review

<thread_object>
creator: reviewer_y8fs

<content>
summary:
This paper focuses on efficiently approximating the LOO error for context attribution. Based on key observations, the author develops AttriBoT, which utilizes cached activations, hierarchical attribution, and smaller proxy models to facilitate large-scale attribution computation.

strengths:
The issue addressed in this paper is significant and holds substantial research value. Furthermore, the writing is clear, and the arguments are easily understandable, with an intuitive methodology.

weaknesses:
See questions part for more details.

questions:
1. My understanding is that the Pareto-optimal conclusion regarding efficiency and performance is derived from Figure 2. While consistent results are observed across the three datasets, does the Pareto-optimal outcome have corresponding theoretical support?

2. In Section 4.1.1, the authors introduce two target models, the Llama3.1 series and the Qwen 2.5 series, for evaluating the AttriBoT method. Can this method be applied to closed-source large models? Additionally, is it applicable to LLMs with architectures other than Transformers?

3. As a general approach, the authors mention the broad applications of context attribution, but only use OBQA for data. Can the effectiveness of the proposed method be further validated in other contexts or task types?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730674139342

thread_type:
official_review

<thread_object>
creator: reviewer_5zgu

<content>
summary:
The authors present a method leave-one out for contextual attribution, i.e. how does a certain part in a model prompt influence the output of the model. Leave-one-out has been mainly impractical due to computational cost and the authors address efficiency via
1. KV caching
2. hierarchical evaluation
3. approximation via smaller (proxy) models

The authors demonstrate that this combination can produce satisfying results with up to 300x speedup, of 30x faster than generating the response (with the bigger model alone).

strengths:
- Context attribution is an important problem, including but not limited to the areas the authors called out in the paper. For example interpretability, reliability, safety, privacy/confidentiality, etc.
- Solid discussion of computational cost, and gains by introducing the individual optimizations
- I like that authors combine and discuss optimizations across the stack ranging from hybrid modelling by combining models of different complexity, leveraging insights of the problem (hierarchical approach), down to lower level optimization such as KV caching

weaknesses:
1. I do not consider the way KV caching is leveraged in this paper as a novelty. KV caching over multiple requests comes for free with well established inferencing frameworks available. However KUDOs to the authors that they discuss the importance of KV cachine in detail, including it's cost saving.
2. It's an important, but fairly narrow area

Some minor concerns:
1. How well would this generalize to more complex reasoning tasks. Recent literature suggests that reasoning capabilities in models vary quite considerable, and only the largest models can properly come up with reasoning solutions, or even judge the output beyond style or simpler tasks (if properly assessed). How well does the proxy model approach generalize if there is a larger gap in model capabilities, and for more complex tasks.
2. How well does the hierarchical approach work in real world scenarios, where information can appear in multiple places (also see in questions section)

questions:
I can see how hierarchical attribution works if context only appears in a single, localized place int he prompt. However, with most real world RAG approaches, we would expect information to be present & spread accross multiple places in the document. What would happen in such a case?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730677941954

thread_type:
official_review

<thread_object>
creator: reviewer_uzjg

<content>
summary:
This paper introduces AttriBoT, a novel collection of techniques for efficiently approximating leave-one-out (LOO) context attribution in large language models. The authors present three key insights: 1) caching attention key-value pairs can avoid redundant computations, 2) hierarchical attribution can reduce necessary computations through pruning, and 3) smaller proxy models can effectively approximate larger target models' attributions. The combined approach achieves remarkable efficiency gains, providing up to 300x speedup while maintaining high fidelity to the target model's attributions compared to baselines. The method is extensively evaluated across different model scales and datasets, demonstrating consistent performance improvements in open-book question answering tasks.

strengths:
1. Technical Innovation & Practicality
- Introduces multiple complementary techniques that can be used independently or combined
- Provides both theoretical and empirical justification for each component
- Achieves practical speedups that make attribution feasible in real-world applications
- Releases efficient implementation to benefit the research community

2. Comprehensive Evaluation
- Tests across multiple datasets (SQuAD, HotpotQA, QASPER)
- Evaluates with different model families (Llama, Qwen)
- Includes thorough ablation studies of different components
- Provides detailed theoretical efficiency analysis with derivations

3. Strong Empirical Results
- Demonstrates clear Pareto-optimal trade-offs between speed and accuracy
- Shows consistent performance across different context lengths and model sizes
- Achieves significant speedups (300x) while maintaining high attribution fidelity
- Outperforms existing attribution methods like ContextCite

4. Clear and Rigorous Methodology
- Well-defined metrics for measuring attribution quality
- Thorough baselines for comparison
- Careful experimental design with appropriate controls
- Detailed implementation specifications

weaknesses:
1. Limited Scope of Applications
- Primary evaluation focuses on open-book QA tasks
- Could explore effectiveness in other applications like detecting malicious prompts or hallucinations
- Could demonstrate utility for real-time attribution scenarios

2. Parameter Sensitivity Analysis
- Could provide more guidance on selecting optimal parameters (e.g., pruning thresholds)
- Limited discussion of how parameter choices might vary across different use cases
- Could explore automatic parameter tuning approaches

questions:
None
</content>
</thread_object>

</thread>
<thread>
creation date:
1730704188327

thread_type:
official_review

<thread_object>
creator: reviewer_f2fj

<content>
summary:
This paper studies how to perform leave-one-out context attribution with resource limitations. For Large language models, performing leave-one-out context is very expensive. Therefore, this paper proposes AttriBot, including the following key points:

- Key-value caching
- Hierarchical attribution
- Proxy modeling

This paper shows that their method achieves lots of speedup comparing to the scenarios where their method is not used while maintaining the performance.

strengths:
- The proposed methods are simple but effective.
- This research direction is useful.
- All findings are supported by experiments (e.g., smaller model in the same model family approximate the target model’s LOO attribution score well)

weaknesses:
- [Major] The proposed methods are too simple and not novel, the messages are not surprising. In fact, I am not sure whether key-value caching can be considered as a contribution here or not. This is just one general trick for speeding up LLM’s inference. The other twos are slightly more novel, but still pretty straightforward. Therefore, to mitigate this weakness, I think this paper requires more extensive experiments to have sufficient technical contributions.
- [Major] More experiments are needed. For instance, this paper only considers Llama model family and Qwen family, and the experiments of many important findings seems only conducted on LLaMA (Figure 1, please correct me if I am wrong, I also checked appendix).
- [Medium] More findings are needed. Just one random example — for other models, whether we observe similar phenomenon as shown on LLaMA model family? If not, what could be the reason? Training dataset? Or other thing? At least, there are still many interesting questions to answer. I think if the authors can dive deeper in this directions, this paper can be a good paper. But now the technical contributions are not sufficient yet.

questions:
See the weakness section.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730707305798

thread_type:
official_review

<thread_object>
creator: reviewer_zbxc

<content>
summary:
The paper introduces AttriBoT, a set of methods for efficiently approximating leave-one-out context attribution at LLM scale. In particular, the authors show that (1) using context caching reduces the number of FLOPs approximately twice compared to naive computation of LOO; (2) Hierarchical Leave-k-out attribution efficiently approximates leave-one-out attribution; and (3) LOO attribution scores for larger models can be approximated by LOO attribution scores for smaller models from the same family. The authors demonstrate that combining these techniques can achieve a 300x speedup in the computation of approximate LOO attribution scores, with only a 10% drop in mean average precision.

strengths:
1. The paper addresses an important problem of context attribution. The leave-one-out (LOO) error method for context attribution is computationally expensive due to the need to re-generate the text after removing each piece from the context. The paper proposes a set of techniques for approximating the LOO scores while significantly reducing computational cost. The proposed methods are very intuitive and can be easily applied in practice.

2. The paper evaluates the proposed combination of approximation and computational techniques across a number of datasets and language models. The authors also compare AttriBoT against several baselines and demonstrate the optimality of AttriBoT's precision-speed Pareto front compared to other context attribution methods.

weaknesses:
While most of the proposed techniques are not entirely novel in the field—for example, KV caching has been applied to other problems, as has transferability between language models from the same family—to my knowledge, this is the first paper where these approaches have been applied to the context attribution problem.

questions:
1. Could the authors please label the AttriBoT combinations in Figure 2 for better clarity?

2. It might be beneficial to include more model families in the empirical experiments, such as Gemma.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730720965632

thread_type:
official_review

<thread_object>
creator: reviewer_jn65

<content>
summary:
The paper proposes a novel techniques called AttriBoT, which is an efficient approach for computing context attribution in LLMs by approximating the computationally expensive leave-one-out (LOO) error. It utilizes KV cache, hierarchical attribution, and proxy models to reduce the cost of calculating LOO attributions by over 300×, achieving real-time interpretability in context-augmented LLMs. The method provides a practical, feasible solutions for efficient real-world context attribution methods.

strengths:
1. Efficient Context Attribution: The paper introduces an interesting and efficient approach to approximate the LOO error for context attribution from the perspective of cache reusage, hierarchical attribution, and smaller proxy model.

2. The  proposed different approaches can be composed together to achieve better efficiency and performance. 

3. The framework shows strong performance across large language models by achieving over a 300× speedup in context attribution computations.

4. The paper also shows the implementation of efficient and easy-to-use AttriBoT.

weaknesses:
1. The paper lacks an in-depth comparison with the latest methods, e.g., for SIG[1], a sequential-gradients-based approach to compute word importance, it used Log-Odds, Comprehensiveness, and Sufficiency evaluation metrics; for LOGRA[2], it evaluated the effectiveness in terms of accuracy and efficiency. However, these did not mention the LOO error. Could you please provide a more in-depth explanation of the necessity and importance of using the LOO error?

[1]. Enguehard, Joseph. "Sequential Integrated Gradients: a simple but effective method for explaining language models." arXiv preprint arXiv:2305.15853 (2023).

[2]. Choe, Sang Keun, et al. "What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions." arXiv preprint arXiv:2405.13954 (2024).

2. The innovation is incremental, it has already well-established in large model compression and optimization methods for KV reuse, Mixture of Experts approach, and pruning strategies. Using cached activation avoiding redundant operation, hierarchical attribution reducing computation, and smaller proxy model emulating large model to approximate LOO seems to be a combination of the previous compression methods.

3. In the Approximation Error part. The paper focus on only a few highly contributive sources, which may overlook the cumulative effect of less influential spans. This approach might limit the method's generalizability, especially in cases where there isn't a clear distinction between highly and moderately contributive sources. It would be better to evaluate your method recovering few highly cotributive sources and full sources on datasets like HotpotQA to verify the rationality.

4. The experimental section needs better reorganization. Key experimental results like the  efficiency vs. accuracy trade-off for each AttriBoT acceleration method should be presented clearly within the main text.

questions:
Please refer to Weakness 3 in the section above.
</content>
</thread_object>

</thread>
<thread>
creation date:
1731122296379

thread_type:
official_review

<thread_object>
creator: reviewer_abnn

<content>
summary:
The paper presents AttriBoT, a set of techniques including key-value caching, hierarchical attribution, and proxy modeling/pruning, to efficiently approximate leave-one-out context attribution for large language models, achieving significant speedup and better faithfulness compared to prior methods, with implications for LLM interpretability and various applications.

strengths:
* The paper presents a novel approach, AttriBoT, for efficiently approximating the Leave-One-Out (LOO) error in context attribution for large language models (LLMs). The idea of using cached activations to avoid redundant operations is highly original. By caching the attention key and value tensors at each layer, the method significantly reduces the computational cost, which is a new way of addressing the inefficiency issue in computing LOO attributions. This has not been explored in such a comprehensive manner in previous works.
* In the context of the increasing use of LLMs, understanding how the model generates its output based on the input context is crucial. The work on context attribution, especially the proposed AttriBoT method, is highly significant as it enables more efficient analysis of the influence of each context span on the LLM's generations. This has practical implications in various applications such as improving the reliability and safety of LLMs by detecting malicious prompts and model hallucinations.
* The hierarchical attribution technique is a novel addition. The assumption that the sum of the LOO attributions for $k$ contiguous text spans can be approximated by a single Leave-$k$-Out attribution score is an innovative concept. This allows for a reduction in the number of forward passes required for attribution computation, especially in hierarchical contexts like paragraphs and sentences.

weaknesses:
__theoretical analysis__
* For the similarity assumption between the proxy model and the target model, the paper mainly proves it by experimentally measuring the correlation, but does not deeply explore the stability and limitations of this similarity assumption under different model architectures and training data distributions. In some cases, the small proxy model may not accurately capture the complex behavior of the large target model, leading to deviations in the attribution results.

__generalization__
* Investigate the adaptability of the method to different model architectures. Consider testing on models with different architectural designs (such as models with different attention mechanisms, non - Transformer-based models) to see if the proposed techniques can be generalized or need to be adjusted. This will provide more comprehensive guidance for the application of the method in the broader field of LLMs.

__evaluation and baselines__
* Explore ways to integrate context attribution results into the human-machine interaction process. For example, design a feedback mechanism that provides users with context attribution information when they receive a response from the LLM, helping them to better understand the basis of the model's answer and guiding them to ask more effective questions. This can improve the overall user experience and the effectiveness of using LLMs.
* The choice of baselines is relatively limited. Although several common methods are included, there may be other emerging or less well-known methods that could provide a more comprehensive comparison. Additionally, the baselines may not cover all possible alternative approaches, leaving room for a more thorough evaluation of the novelty and superiority of the AttriBoT method.

questions:
* When using proxy models for approximation, what is the impact of differences in the training data distribution between the proxy model and the target model on the accuracy of the context attribution? How can this potential issue be mitigated, especially when dealing with models trained on diverse or domain-specific datasets?
* In the hierarchical attribution method, how do you ensure the stability and accuracy of the approximation when the context structure becomes extremely complex, such as in documents with highly nested or irregular hierarchical structures? Are there any theoretical guarantees or additional techniques that could be employed to handle such cases?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732293824687

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Updates to our Submission

comment:
Thank you to all seven reviewers for engaging with our work and providing insightful feedback. Here, we would like to update you all on the changes we’ve made to our submission in response to your comments:

- We updated Figure 2 to indicate which combination of methods achieved different points on the AttriBoT Pareto front
- We’ve added a “Practitioner’s Guide to Using AttriBoT” in the Appendix to advise users on how best to apply AttriBoT to their problem and select appropriate algorithms and hyperparameters
- We expanded our set of results by evaluating our approximate LOO attribution methods using the Qwen 2.5 model family on all three datasets we consider in the paper (see Figure 6)
- We added experiments exploring the effectiveness of proxy modeling when the target model and proxy model come from different model families – e.g., Llama 70B approximated by Mistral 7B (see Figures 7-10)
- We have made some changes to our organization and notation to improve the paper’s clarity where indicated by reviewers
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper simultaneously achieves a big speedup and also faithfull attributions compared to existing work.  The reviewers unanimously vote for acceptance, and I agree.  The reviewers did raise some concerns, but none of these were disqualifying, and the authors engaged extensively with the reviewers during the discussion period, including additional experiments.  For example, several reviewers raised novelty concerns, but methods need not be entirely novel as long as they obtain stronger results and are often composed of existing techniques.  Reviewers also pointed out that the method focuses on decoder-only transformers, which I think is entirely acceptable since virtually all LLMs in use are decoder-only transformers.  Overall, the reviewers and I have a positive view of this paper.