AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution
Authors: Fengyuan Liu
Nikhil Kandpal
Colin Raffel
Venue: ICLR 2025 Poster (2025)
Keywords: Large Language Model
Context Attribution
Interpretability
Primary area: interpretability and explainable AI
TL;DR: A suite of methods for efficiently attributing an LLM's response back to parts of its context

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.

Published as a conference paper at ICLR 2025
ATTRIBOT: A BAG OF TRICKS FOR EFFICIENTLY APPROXIMATING LEAVE-ONE-OUR CONTEXT ATTRIBUTION
Fengyuan Liu; ; Nikhil Kandpal, Colin Raffel University of Toronto, Vector Institute Toronto, Ontario, Canada
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ABSTRACT
The influence of contextual input on the behavior of large language models (LLMs) has prompted the development of context attribution methods that aim quantify each context span's effect an LLM's generations. The leave-one-out (LOO) error, which measures the change in the likelihood of the LLM's response when given span of the context is removed, provides principled way to per- form context attribution, but can be prohibitively expensive to compute for large models. In this work, introduce AttriBoT, series of novel techniques for efficiently computing an approximation of the L.00 error for context attribution. Specifically, AtrrBBoT uses cached activations to avoid redundant operations, per- forms hierarchical attribution to reduce computation, and emulates the behavior of large target models with smaller proxy models. Taken together, AttriBoT can provide >300x speedup while remaining more faithful to target model's LOO error than prior context attribution methods. This stark increase in performance makes computing context attributions for given response 30 faster than gener- ating the response itself, empowering real-world applications that require comput- ing attributions at scale. We release user-friendly and efficient implementation AttriBoT to enable efficient LLM interpretability as well as encourage future development of efficient context attribution methods" !
INTRODUCTION
The use of large language models (LLMs) has proliferated in recent years including the integration of OpenAl's GPT-4 (OpenAI, 2024) and Google's Gemini (Team, 2024) into Apple and Android-based products with billions of users. As LLMs become more widely used, their influence on information access, decision decision-makingg and social interactions will grow, as will with the consequences of incorrect or problematic outputs. The risks and impact of this widespread adoption spur the need for deeper understanding of how and why L.LMs generate their outputs. Indeed, great deal of recent work on LLMM interpretability aims to uncover and elucidate their inner workings, including determining the influence of pre-training data (Koh & Liang, 2020; Grosse et al., 2023; Kandpal et al., 2023) and mechanistically understanding their underlying architecture (Cammarata et al., 2020).
common usage pattern for LLMS involves providing relevant contextual information alongside a query. For example. in eetrivall-uugeent generation (RAG) (Lewis et al., 2021), documents from an external datastore that are relevant to a given query are retrieved and are provided as part of the LLM's input. This allows L.MSs process data make decisions based not available in their pre-training dataset, which has proven critical to making LLMs applicable to a wide range of use cases. While inspecting the documents retrieved by RAG system can provide form of interpretability, LLMs generally provide no direct insight into which part of the augmented context influenced the model's generation. To address this shortcoming, context attribution methods
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We empirically test the assumptions underlying the AttriBoT's underlying methods on ex- amples from Hotpot QA. Left: The attribution scores of small proxy models ranging from 1B 8B parameters have high correlation with the attribution scores of a 70B-parameter target model, imply- ing that the attributions from smaller models can be a reliable proxy for those from a target model. Middle: Paragraph-Ievel attribution scores correlate extremely well (R = 0.97) with the sum of the sentence-level attribution scores given paragraph, suggesting that hierarchical attribution can provide an effective means of pruning a large amount of irrelevant context. Right: Proxy models can effectively prune contexts of unnecessary sources, achieving recall of 90% when keeping only half of the sources in a context.
Figure 1:
(Cohen-Waid Wang al., 2024; Yin & Neubig, 2022: Gao et al., 2023b) aim quantify the influence of each span of text in an LLM's context on its generated output.
A natural approach for context attribution is remove span of text from the context and measure the ensuing change in the likelihood of the model's 's original response. This notion of importance, known as the Leave-One-Out (LOO) error, is common idea used in training data attribution (Koh & Liang, 2020), data valuation (Choe et al., 2024), feature attribution (Li et al., 2017), and recently for context attribution (Cohen- Wang et al., 2024). While the LOO error produces meaningful and interpretable context attribution scores, it is often viewed impractical due to the need to perform an independent forward pass to score each text span the context. This particularly problematic for modern LLMs whose forward passes are computationally expensive and realistic use cases often involve long contexts with many text spans (c.g., sentences or paragraphs) to score.
In this work, aim to show that LOO context attributions can be efficiently approximated at LLM scale. To do so, we leverage the following observations:
1. Approximately half of the FLOPs needed to naïvely compute LOO attributions redundant and can be avoided by caching the attention key and value tensors at each layer (Pope et al., 2022).
The sum of the LOO attributions for i contiguous text spans (e-g., sentences in a paragraph or paragraphs in a section) are well-approximated by a single Leave-k-Out attribution score (Fig- ure 1, middle).
3. The LOO attribution scores for large model (e.g., 70B parameters) are well-approximated by smaller models (e.g., 8B parameters) in the same model family (Figure 1, left and right).
These findings naturally lend themselves to efficient key-value caching schemes, novel hierarchical approaches that prune low-information context chunks before performing attribution, and methods that leverage small proxy LMs to approximate the LO0 attributions of a larger target model. We develop and explore the practical application of these schemes to develop an aggregate system called AttriBoT (a Bag of Tricks for efficient context attribution). When evaluating AttriBoT in the open- book question answering setting, where a model is presented question with one or more related documents provided its context, we find that our methods significantly reduce the cost of comput- ing LOO attributions at times by >300x while remaining more faithful to the original model's LOO error than a wide range of baselines. BoT can be naturally composed to attain Pareto-optimal trade-off between efficiency and accuracy over efficiencies ranging multiple orders of magnitude.
addition, the cascade of approaches underlying Attri-
In the following section, formalize the problem of context attribution, define the LOO error in detail, and discuss the metrics we use to evaluate a given approximate attribution method's faithful- ness. Then, in Section 3, we detail our novel techniques for efficiently approximating I.00 error,
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ultimately producing AttriBoT. Experimental results across multiple model families and datasets provided in Section 4.1, with related work in Section 5 and a conclusion in section
PROBLEM STATEMENT
In this section, we introduce the problem of context attribution (Section 2.1), define the simple and principled Leave-One: Out (LOO) error method for context attribution (Section 2.2), and define how we evaluate approximate LOO attribution methods (Section 2.3).
Notation Suppose that autoregressive Transformer (Vaswani al., 2023) language model (e.g. Llama (Dubey et al., 2024), GPT-4 (OpenAl, 2024), etc.) generates a response R conditioned on a query Q and context C. For the purposes of this paper, we view language model with parameters 0 as function pe Q, C) that returns the probability of generating a response given a particular query and context. Furthermore. we assume the context can partitioned into sequence of sources with partitioning function [I()) [ where each source is contiguous chunk of text in the context, like a sentence, paragraph, or document. For notational convenience, we define |C] to be the number of sources in IICC).
2.1 CONTEXT ATTRIBUTION
large body of work on feature attribution has studied the relationship between a model's predic- tions and input features (Li et al., 2016; Wu et al., 2021). More recently, the problem of context atributionn, coined by Cohen-Wang et al. (2024), has been introduced as special case of feature at- tribution, where response generated by LLM is attributed back parts of the LLM's contextual information.
Formally, follow Cohen-Wai Wang et al. (2024) and define context attribution method function 70,, R, (5,,,)),,,.()))) R Cl that maps language model's parameters 0, response R, and sources E II(C') to vector of real-valued scores indicating each source's s importance to the model's response. While Cohen- Cohen-Wang et al. (2024) leave the dependence of on f and implicit, we make this dependence explicit because will later explore algorithms where multiple models used and may be fed other models' responses.
2.2 LEAVE-ONE-OU ATTRIBUTION
There are many notions of what makes a source important that each lead different choices of т. Perhaps the simplest and most interpretable notion of importance is that important sources lead to large changes the likelihood of the model's response when they are removed from the original context. This quantity, often referred to as the Laave-One-Out (LOO) error, leads to the following choice of context attribution function, which we refer to as the "L,O attribution" in the remainder of the paper:
TLOO (f, S1,.... $|c)): k = lo ( Q, - log pe (R|Q, C {s+})
(1)
In practice, LOO attributions can be impractical to compute for particularly large language models since scoring all |C] sources in the context requires |C| + forward passes of the language model pass compute the likelihood of the response given the full context and |C| passes compute the likelihood with each of the |C| sources individually removed from the context. In realistic settings where models provided with large amount of contextual information (i.e. |C'| is very large), computing LOO attributions for all sources in a context can be orders of magnitude more expensive than generating the response itself. Thus, the remainder of this paper explores a variety of methods for efficiently approximating TL.0-
2.3 EVALUATING APPROXIMATE LEAVE-ONE-OUS ATTRIBUTIONS
Much past work on intrepretability and attribution measures performance terms of how well a given method matches human annotations of source importance. Our focus instead on efficiently approximating the attributions of a "target" LLM whose LOO attributions are prohibitively expen- sive compute. We therefore introduce straightforward evaluation procedure for measuring effi-
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Method
Method Parameters
Theoretical FLOPs
Speedup over LO0
LOO
N/A
2PT]C[[[]]
KV Caching
N/A
PT]CI([C] -
2
Proxy
P' Proxy model size
2P'TIC[(IC -
Pruning
P Proxy model size Fraction of sources
2PT]CI((a" + + aa a- F)
P(IC)-
(a (PPPP1)]GG P
Hierarchical
Sources per group Fraction of groups
2PT]CI((5*+ + - 8-:01-8-11 1-1)
H([C--1)
[P -AN-
Table 1: The theoretical number floating poatinggpoint operations (FLOPs) needed by different methods compute LOO attributions expressed in terms of P, the number of target model parameters, T, the number of tokens per source, and C, the number of context sources.
ciency and determining how faithful an approximate method's attributions are to those produced by the original target model.
Approximation Error Past work has found that for many real-world tasks like summarization and question answering. attributions tend be sparse, meaning that only small number of sources in the context significantly influence a model's response (Liu et al., 2024; Kuratov et al., 2024; Yang al., 2018). Thus, when evaluating how well an algorithm approximates LOO attributions, primarily interested in how well the algorithm recovers these few highly contributive sources.
To evaluate approximate LOO attribution methods we first compute the LOO attributions of the tar- get model and identify the most contributive sources. We consider source to be highly contributive if it has outlyingly large score. To find outlier scores, we apply the Generalized Extreme Studen- tized Deviate (ESD) test (Rosner, 1983), statistical test that returns the outlier values in collection of data (for more details, see Appendix B.2). We denote the number of detected outlier sources for a given example as Noutt
We then test the extent to which approximate LOO attribution methods recover these nout out- lier sources using evaluation metrics from information retrieval. In particular, we rank the sources according their approximate LOO attribution scores and measure the mean Average Precision (mAP) over examples in dataset (Manning et al., 2008).
Efficiency For each approximate LOO algorithm, we report its practical and theoretical efficiency. To measure practical efficiency, report the average GPU-seconds needed to compute attribution scores for all sources point operations (FLOOs) needed to compute attributions as function of the number of model parameters P, the number sources in the context |C), the number of tokens each source T, and any other method-speeific parameters. To estimate FLOPs, use the simplifying assumptions from Kaplan et al. (2020) and Hoffmann et al. (2022), i.c. that performing a forward pass for P- P-parameter language model T' tokens requires approximately 2PT FLOPs.
context. For theoretical efficiency, we estimate the number of floating-
ACCELERATING SEAVE-ONE--O OUT ATTRIBUTION WITH ATTRIBOT
In this section, we describe the Bag of Tricks that AttriBoT uses efficiently compute approx- imate LOO attributions. For each method, we provide intuition for why it serves as an effective approximation to the LOO eтrоr as well as discussion on theoretical speedup over standard LOO attribution using the target model. Full details on the efficiency gains for each method can be found Table with derivations available in Appendix A.
3.1 KEY- VALUE CACHING
For context containing (C] sources, computing TLOO requires |C| 1 forward passes (including the first forward pass to generate the response). However, much of this computation is redundant when the underlying language model is autoregressive Transformer. At each self-attention layer
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in autoregressive Transformer, the key and value tensors for given position in the sequence only a function of previous tokens due to autoregressivity or causal masking ( (Vaswani et al., 2023). Thus, if two imputs share a prefix, the key and value tensors at each position in the prefix will be identical. This redundant computation can be avoided by caching the key and value tensors at each layer and reusing the cached values for inputs that have previously computed prefix (Pope al., 2022).
Specifically, when computing LOO attributions, the keys and values can be cached for the full quence of tokens while computing pa (R Q, C). Then, for each subsequent forward pass computing P \ {st]), ) the cached keys and values for Q and the first i sources in C' can be reused. In aggregate over all sources, this avoids computation for a total of (IC) - 1)/2 sources, ultimately saving approximately half of the FLOPs used compute TLOO- Notably, ignoring differences due to numerical error of floating point operations, we should expect KV caching to be lossless i.e., LOO attributions should be the same regardless of whether KV caching used.
3.2 HIERARCHICAL ATTRIBUTION
In many settings, we expect that the context has hierarchical nature for example, the context might comprise a sequence of paragraphs that can each be broken down into a sequence of sentences. This structure can be leveraged to efficiently identify sources that are likely to have high LOO attributions, allowing us to avoid computing TLoO for every source in the context.
Imagine want compute LOO attributions at the sentence level. The key assumption underlying our hierarchical attribution algorithm is that the sum of the LOO attributions for sentences in paragraph can be closely approximated by a single Leave-k-out attribution score computed by removing the paragraph as whole. In cases where this holds, paragraphs whose removal incurs a large drop the response's 's likelihood are also likely to contain highly contributive sentences.
Specifically, to perform hierarchical attribution we assume the context can be partitioned into a sequence of "source groups" (e.g., paragraphs) (C) [ M and each of these groups can be further decomposed into its constituent sources (e.g., sentences) II, = [s,], where M, is the number of sentences in group G;. We first compute the LOO error for each source group, TLoo (6, R, G.,... GM) ), and keep only a fraction, B, of the groups with the highest scores. From these high-attribution groups, we construct a shortened context Ö comprising only the retained groups and compute (0, R, s1......$cc! s(c)) for the sources E (C).
For example, when processing a document with 10 paragraphs and using 3 = 0.2, we would first compute the LOO error corresponding to the removal each of the paragraphs and retain the two paragraphs with the highest LOO score. Finally, to compute attributions for the sentences in these two paragraphs, we concatenate the paragraphs into a truncated context and compute the LOO error incurred by removing each sentence from the truncated context.
This hicrarchical approach achieves a speedup over LOO by (1) first doing fewer forward passes over the full context when computing attributions at the source group level and (2) doing fewer for- ward passes on a shortened context after the low-attribution source groups are removed. With the simplifying assumption that each source group contains H sources, if we keep only constant num- ber source groups irrespective of the total number of sources (i.e. ,3 ~ 1/|CD, then this method's speedup over LO0 is approximately H for long contexts.
3.3 PROXY MODELING
The high cost of computing the LOO error stems from having to compute |C] forward passes through a large and computationally expensive language model. The cost of each of these forward passes is naturally cheaper for smaller models. If a smaller "proxy" model's attributions are faithful to a larger target model whose attributions we hope to obtain, this raises the possibility of reducing the cost of computing LOO attributions by using the proxy model instead. In particular, we might hope that a smaller model from the same model family (i.e. sharing a model architecture, training dataset, and training objective but differing in its parameter count) produces similar attributions to a target model. Specifically, we take the response R generated by the target model, and use the approximation 71.00 (Oprazys R, $1,.-.,88[]] Tfoo(0, R. 81, 81,-,,,8(c1}. The proxy model does
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not need produce the response, but rather emulate the conditional likelihood of the target model's response given a context. practice, as further discussed in section 4.2.1, we find that when using the target model's s response to compute attribution scores, the LOO errors from target model are highly correlated with those from a small proxy model from the target model.
3.4 PROXY MODEL PRUNING
While small proxy models can approximate the context attributions of larger target models rea- sonably well, one way improve the fidelity of this approximation is to use the proxy model to prune away low-attribution sources and then re-score the remaining sources with the target model. Specifically,
take the response generated by the target model, use it compute Tooo(Oprozg) (Oprozy. R, $1, $,,..., S(Ci), and keep only a fraction, a, of the highest scoring sources. Then reconstitute the context keeping only these top sources and recompute their LOO attributions using the target model.
Compared to simply using proxy model, this method is more expensive since it requires forward passes of the target model for each of the non-pruned spans. However, this extra compute spent improving the approximation error for the high-attribution sources whose LOO attributions important to recover accurately. If the number of sources kept after pruning is constant (i.e. or ~ I/IC'), then this method provides a speedup of roughly P/P for long contexts, the same speedup achieved by proxy modeling alone.
most
3.5 COMPOSING METHODS IN THE ATTRIBOT BAG OF TRICKS
The methods described above improve the efficiency of computing LOO attributions by avoiding redundant calculations during forward passes, reducing the size of the model used to perform the forward passes, and/or reducing the total number of forward passes. As different methods target different factors that make exact LOO computation expensive, these methods posed together to achieve even greater efficiency. Concretely, in addition to the individual methods described previously. we test the following composite methods in our experiments: KV Caching Hierarchical Attribution, KV Caching + Proxy Modeling, KV Caching + Proxy Model Pruning, and KV Caching Proxy Modeling + Hierarchical Attribution.
often be com-
4 EXPERIMENTS
To validate AttriBoT, we compare its efficiency and faithfulness to range of baseline context at- tribution methods. Our focus is confirming that approximations provide Pareto-optimal trade-off between speed and faithfulness to the target model.
4.1 SETUP
In this section, we describe the models, datasets, and baseline methods used evaluate AttriBoT, as well as the computational resources used to run our experiments.
4.1.1 MODELS
To ensure that AttriBoT is valuable in state-of-the-art settings, we focus on recent performant open LLMs. We therefore experiment with two target models whose LOO context attributions we aim to efficiently approximate: the -0--paraeeter instruction-tuncd LLM from the Llama 3.1 model family (Dubey et al., 2024) and the 72B- par-maraeeter instruction-tume (Yang et al., 2024). As proxy models, we use smaller instruction-tunne models from each of these model families. For Llama 70B, this includes the 8B-parameter model from the Llama 3.1 family and the IB and 3B-parameter models from the Llama 3.2 family. For Qwen 72B, we use the 0.5B, 1.5B, 3B, 7B, and -arameeeet models from the Qwen 2.5 model family as proxies. Each model within given family shares architecture, pre-training objective, and pre-training dataset. We report results for the Llama models in the main text and replicate select experiments with the Qwen models in Appendix C.
L.LM from the Qwen 2.5 model family

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4.1.2 DATASETS
There are a number applications of context attribution that range from verifying faithfulness of model summaries detecting malicious prompts. In this study, we focus the well-studied and widespread setting of open-book question answering (OBQA), which involves prompting a model answer question that is supported by provided context. Since many tasks can be formulated as answering a question about contextual information (McCann et al., 2018), we expect results on OBQA be reliable indicator of performance other settings, Given that each method's efficiency depends on the context length, we consider datasets covering range of context lengths. To this end, we focus on three open-book QA datasets:
1. SQuAD 2.0 (Rajpurkar al., 2018): A reading comprchension benchmark where model is presented question along with Wikipedia article containing the answer. For this dataset, we consider sources to be individual sentences in the context and source groups for hierarchical attribution to be paragraphs.
HotpotQA (Yang al., 2018): A multi-ho hopp question answering benchmark that requires reason- ing across paragraphs from multiple Wikipedia pages, well ignoring distractor paragraphs, in order to answer question. For this dataset, we consider sources to be individual sentences in the context and source groups for hierarchical methods to be paragraphs.
3. QASPER (Dasigi et al., 2021): : A document-gre grounddd, information information-sseeknng question answering dataset where each context natural language processing paper. Answering questions in the QASPER dataset requires complex reasoning about claims made in multiple parts of a paper. For this dataset, we consider sources be paragraphs and source groups for hierarchical methods to be sections of the paper provided in the context.
For more details on these datasets, see Appendix B.
4.1.3 BASELINES
We compare AttriBoT's performance to a diverse set of context attribution methods:
Attention Weights: The self-attention operation in the Transformer architecture provides an im- plicit way of determining which input entries are influential (Jain & Wallace, 2019; Wiegreffe & Pinter, 2019). Concretely, we compute the total attention weight for each source by summing the attention weights of source's ' tokens across all attention heads and layers.
The gradient of the model's 's response likelihood with respect to its input provides first-order approximation of the model's sensitivity input perturbations, and thus is useful in- terpretation technique (Yin & Neubig, 2022; Mohebbi et al., 2021). Specifically, we calculate the Frobenius norm of the gradient of the response's likelihood with respect the token embeddings of each source.
Gradient Norm:
Sentence embedding similarity: As simple model-agnostic baseline, we compute the similar- ity between sentence embeddings for the generated response and each one of the sources. We generate sentence embeddings using the all-MiniLM--L6-22 SentenceBERT model (Reimers & Gurevych, 2019) and compute similarity via cosine similarity.
ContextCite: This method estimates the importance of each context source by training a linear surrogate model to estimate the likelihood of response given trained linear model's weights for each source in the context act as an attribution score (Cohen- Wang et al., 2024). More background ContextCite is provided in in Appendix B.3.
of context sources. The
For more details on baseline methods, see Appendix B.3.
4.1.4 COMPUTATIONAL RESOURCES
servers with 4 NVIDIA A100 SXM4 GPUs with 80GB of VRAM. For experiments involving models with fewer than 15 billion parameters, we use only a single GPU. For models with parameters, model parallelism across multiple GPUs.
All experiments

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Figure 2: We plot the mean average precision compared to attributions of the target model against the GPU time for AttriBoT and a variety of baselines using Llama 3.1 70B Instruct as the target model and smaller Llama instruct variants proxy models. Across all three datasets, AttriBoT is consistently Pareto-optimal over multiple orders of magnitude.
4.2
RESULTS
We now provide empirical results that motivate the approximations made in AttriBoT and demon- strate its effectiveness in the OBQA setting.
4.2.1 VALIDATING ATTRIBOT's APPROXIMATIONS
Hierarchical Attribution The assumption underlying our hierarchical attribution method that the sum of the LOO scores for k sources in a source group (e.g.. sentences in a paragraph) can be estimated by computing the Leave-k-Our attribution the source group as a whole. We empirically test this assumption for Llama 70B by comparing LOO attributions for all paragraphs in HotpotQA with the sum each paragraph's 's sentence-level LOO attributions. Figure (Middle) shows that the these two quantities have very high Pearson correlation (R = 0.97), meaning that the removal of low-attribution source groups in our proposed hierarchical attribution method is likely to keep most high-attribution sources.
Proxy Modeling Proxy modeling assumes that two models from the same model family will have similar LOO attributions. Thus, if the target model large, its LOO attributions for a particular response can be efficiently approximated by attributing the same response with smaller proxy model. We test this claim by measuring the correlation between the HotpotQA sentence-level at- tribution scores from Llama 70B and smaller proxy models from the Llama model family, ranging from IB to 8B parameters. We find that LOO attributions from each of the proxy models are very well correlated with the Llama 70B's LOO attributions. Shown in Figure (Left) the Pearson corre- lation is as high as 0.94 for the largest proxy model with 8B parameters and only decreases to 0.88 for the smallest IB parameter proxy model.
Proxy Model Pruning Like proxy modeling. proxy model pruming assumes that small models can capably approximating a large model's s attributions. However, pruning additionally requires the proxy model to rank outlier sources as one of the top-a/C1| sources. In practice we find this to be the case. Figure 1 (Right) plots the outlier recall of different proxy models as a function of the number of sources kept when pruning context. In practice, we find that Llama 8B achieves 85% recall of sources with outlier Llama 70B attributions while only keeping the top-10 spans.
4.2.2 ATTRIBOT's PARETO-OPTIMALITY
Our main results, comparing AttriBoT the baselines listed in Section 4.1.3 on the datasets de- scribed in Section 4.1.2 for the Llama model family, are provided in Figure 2. We find that the tech- niques composed in AttriBoT produce a Pareto-optimal method across many orders magnitude of costs on all three of our evaluation datasets. Compared to ContextCite, the only other method we are aware of that computes attributions via repeated forward passes on modified contexts, AttriBoT provide attributions that equally faithful to target model's LOO attributions with orders of magnitude lower cost. Compared to cheaper attribution baselines (sentence similarity, gradient norm, and attention weights), AttriBoT remains Pareto-optimal and can even be efficient than
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using sentence embeddings (which are produced by small model and do not require any forward passes of a target or proxy model). These results hold true across all datasets, demonstrating that AttriBoT scales well to datasets of varying context length.
KV Caching We find that, in practice, KV caching offers about 1. 1.6x speedup over computing exact LOO attributions while, as expected, nearly perfectly recovering outlier sources (see Figure 3. While theory computing attributions with and without KV caching should be identical, prac- tice we observe small difference in the attribution values due to accumulated numerical error of floating-poin operations.
Proxy Modeling Shown in Figure 3, proxy modeling can be an effective approach for greatly speeding up LOO computation. We find that as the size the proxy model decreases, its faithfulness to the exact LOO attributions also decreases. However, the faithfulness of attributions from smaller models still remains better than many baselines that use orders of magnitude more compute.
Hierarchical Attribution In Figure 4, we show the accuracy vs. efficiency trade-off of hierar- chical attribution. We find that modulating the fraction of source groups retained, B, an effective method for trading off accuracy for efficiency. Additionally, we find that using the target model yields the most accurate attributions, while substituting the target model for a smaller proxy model achieves greater efficiency.
Proxy Model Pruning Like hierarchical attribution, we show Figure that modulating the frac- tion of sources to retain, o, and the proxy model size effectively trades off accuracy for efficiency.
4.2.3 ATTRIBOT's S IMPACT ON MATCHING HUMAN ANNOTATIONS
Our main focus has been on efficiently approximating the attributions of a target model. However, it also be valuable to measure how closely an attribution method matches human-annotated "important" spans. Such annotations are available for the HotpotQA dataset, so we additionally evaluated the impact of using AttriBoT on agreement with human annotations. Full results are in Appendix C.4; as short summary, we find that the methods introduced in AttriBoT actually remain more faithful to the human- human-annotated annotated attributions than they do to those of the target model. Concretely, we observe that AttriBoT can achieve a x00x speed up with only 10% drop in mAP.
5 RELATED WORK
Computing LOO context attributions involves repeated inference of the target model. contrast, past work has explored whether it is possible to train LLMs to directly provide citations to sources spans from their context (Weller al., 2024; Gao et al., 2023b). Some approaches leverage pipelines for retrieving external facts to ground and verify a generated response while providing explicit attribution (Asai al., 2023; Li et al., 2024b; Sun et al., 2023; Li et al., 2024a; Chen et al., 2024; Huo et al., 2023; Gao et al., 2023a). Other models are explicitly trained produce citations, including GopherCite (Menick et al., 2022), which generates quotes from supporting documents after generating a response; LaMDA (Thoppilan et al., 2022), which provides URLs from the information retrieval system used; and WebGPT (Nakano al., 2022), which provides extracted context from the source webpages. Such citation generation methods only provide insight how each statement is possibly supported by the corresponding source instead of measuring how each source contributes to response via causal intervention like source removal. Therefore, while useful for providing evidence for supporting or verifying an L.MM's response, these methods cannot necessarily provide insight into why an LLM produced particular output.
Generating Citations
While one goal of this line of work is to improve the factuality and reduce hallucinations LLMs, whether such citations can themselves be considered trustworthy is unclear (Peskoff & Stewart, 2023: Zuccon et al., 2023; Gravel et al., 2023). In fact, previous work has explored generative search engines like Bing Chat, NeevaAI, perplexity.ai, and YouChat that provide inline citations, and find that they frequently contain unsupported statements and inaccurate citations (Liu et al., 2023).

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Feature Attribution-Based Explanations The field of neural network interpretability has put forth a huge number of techniques that aim to measure the importance or relevance of cach input feature model's prediction. Perturbation- based techniques including leave-one-out and masking have been applied on features like words and phrases (Li et al., 2016), input word-vector dimensions, intermediate hidden units (Li et al., 2017), and token spans (Wu et al., 2021). The gradient of model's 's output with respect to an input feature indicates the sensitivity of output to input changes and has therefore been used to compute attribution scores (Yin & Neubig, 2022; Mohebbi et al., 2021: Sanyal & Ren, 2021; Sikdar et al., 2021; Enguehard, 2023). We adapted this simple gradient- based attribution approach as one of our baselines. Since attention computes an adaptively weighted average of activations, attention weights can be seen as capturing the correlation between inputs. Attention weights have therefore also been a popular tool to identify important information to the model (Pruthi et al., 2020a; Serrano & Smith, 2019; Wiegreffe & Pinter, 2019) and we therefore use attention weight as one of our basclines. Local interpretable surrogate models are also developed to interpret model outputs (Ribeiro et al., 2016; Lundberg & Lee, 2017).
201,).
Attributing Predictions to Training Data Apart from attributing an LLM's predictions to input features that correspond to elements in its context, it can be of practical interest to uncover the training data that influenced particular prediction. Such is the goal of influence functions (Koh & Liang, 2020), which estimate the impact of removing particular training example by computing the alignment of gradients through bilinear form with the inverse Hessian. The high cost of computing and inverting the Hessian, as well as the cost of computing gradients for every in example within potentially gargantuan training dataset, has led to a great deal of work on computing influence functions more efficiently, particularly for LLMs (Grosse et al., 2023; Choe et al., 2024; Pruthi al., 2020b). Apart from costs, the reliability of influence functions has been questioned (Basu et al., 2021; Bae et al., 2022). As exact LOO for dataset attribution requires full training run for each data point, most training data attribution methods resort to evaluating with proxy metrics like task accuracy as a function of the number of high-attribution training examples removed, instead of ground truth LOO attributions as in our work. Beyond directly measuring influence, other works have sought to understand the influence of pre-training data on factual knowledge internalized by the model (Kandpal et al., 2023; Chang et al., 2024; Razeghi et al., 2022; Antoniades et al., 2024). More broadly, the impact of pre-training data on LLM behavior and performance is an important notion for the well-studied problem of data selection; see (Albalak et al., 2024).
6 CONCLUSION
In this paper, we introduced AttriBoT, a Bag of Tricks for efficiently attributing an LLM's predic- tions to spans of text in its imput context. AttiBoT leverages series of novel techniques for approx- imating the attributions of a large target model, including reusing computations through key-value caching, performing hierarchical attributions reduce the number of forward passes, and leverag- ing a smaller proxy model whose attributions reliably approximate those of the target model. Taken together, AttriBoT can provide >300x speedup, making attributing response 30 more efficient than even generating a response in realistic settings, while remaining more faithful to the target model's s attributions than prior methods for efficient context attribution. In addition, the components of AttriBoT can be included, excluded, and tuned to produce Pareto-optimal trade-off between faithfulness and speed. While our experimental results primarily focus on the general setting of open-book question answering. we anticipate AttriBoT will also be useful for detecting malicious prompts and model hallucinations. We hope that our efficient and casy-to-use implementation of A ensures that it has real-world impact and also enables future work on efficient context attribution.
7 ACKNOWLEDGEMENTS
We thank Haokun Liu, Brian Lester, Wanru Zhao, and the members of Vector Institute for their valu- able feedback and support. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Govermment of Canada through CIFAR, and companies sponsoring the Vec- tor Institute ht https: / /tt/eeettriistttt tute. a fpprtteerssiiii wtteeesssssssseeeeeeeeee cuwrenttteeeree parreentttaattrss.....
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Pickett, Kathleen S.
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Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, V. O. Kuzmina, Joseph Fenton, Aaron Co- hen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera- Arcas, Claire Cui, Marian Rogers Croak, Ed Huai hsin Chi, and Quoc Le. Lamda: Language models for dialog applications. arXiv preprint 1:Xiv:2211008239 2022.
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A THEORETICAL EFFICIENCY DERIVATIONS
In this section, we provide derivations for the theoretical efficiencies reported in Table 1.
Preliminaries We assume a target model with P parameters, a context C' containing [C] sources, and that each source in the context contains exactly T tokens. Following Kaplan et al. (2020) and Hoffmann et al. (2022), we approximate the number of FLOPs for a forward pass P-parameter model on sequence of T tokens to be 2P FLOPs.
To simplify the analysis, we ignore the fact the query and response are also part of the model's input, these are typically negligible compared to the length of the context. Accounting for the query and response would simply add a small constant factor to each method's theoretical runtime. Finally, we assume that the likelihood of the full sequence under the target model, pø (RJQ, C') can be computed while generating R. and is thus not accounted for in the analysis of each attribution method.
A.1 EXACT LOO ATTRIBUTION
Exactly calculating LOO attributions under the target model requires computing po PoRRQQ {s:} for in i E l,..., | total, this is |C| forward passes each on sequences containing T(C - tokens. Thus, the number of FLOPs needed compute attributions is (PT) 1).
A.2 KV CACHING
KV caching allows us to avoid FLOPs spent on prefixes for which keys and values have already been computed. Since keys and values can be cached for the full sequence while computing po [RQQ. C), we can avoid recomputing the keys and values for the first i 2-1 sources while comput- ing pa [R|Q, C {s:)). Thus the total number of FL.OPs for computing all LOO attributions can be written as:
C 2PT(IC1 - -) PTICI(CI) 1)
This provides speedup of factor of compared to computing LOO attributions with caching.
A.3 PROXY MODELING
Proxy modeling is identical to computing exact LOO attributions, except with a model with fewer parameters. If the number of proxy model parameters is P', then the number of FLOPs needed to compute attributions is C - 1) and the speedup gained by using a proxy model P/P'.
A.4 HIERARCHICAL ATTRIBUTION
Our analysis of hierarchical attribution makes the simplifying assumption that each source group in the context contains exactly sources.
We start by first considering the initial source group-scoring step in the hierarchical attribution algorithm. This step requires performing |CI/H forward passes (one for each source group) each sequence of T(ICI- H) tokens. Thus, the initial source group scoring step (PT((CI/H)(C))) H) FLOPs.
The second step in hierarchical attribution computes scores the source level on shortened context containing only 3|C| sources. Thus, the second step hierarchical attribution uses 2PT(()C))(3(C)
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Combining these steps together, can compute the total number of FLOPs for hierarchical attri-
bution:
[C|
Total FLOPs
-PP
(IC) H) + 21PT(11))(
H
|C|
-2PC
1 33CC| - -3) ((++
-2TC
|| -1
By comparing the number of FL.OPs used for exact I.00 attribution we get the following speedup factor:
IC (B2 + } IC|
-(C[- (5 + 1)|CI 3H H
- -
Next, consider the speedup as the number of contexts grows and the number of source groups kept after the first group-level scoring pass is kept constant, i.c., 3 k/|C| for some constant k.
H(IC- 11) (32H + 1]|C| - -BH H
H(CC--1)
k |
H
+
|0|
T
&
H([C|
-1)
12
H1 + |C
11
H C|
for large |C|
C*
=H
Thus, for contexts with many sources, hierarchical attribution's 's speedup approaches H, the number of sources per source group.
A.5 PROXY MODEL PRUNING
The first stage of proxy model pruning simply uses proxy model score each source. Using the result from Appendix A.3, this operation uses 2 1) FLOPs, where P' is the number of parameters in the proxy model. The second stage of the proxy model pruning algorithm uses the target model to score the remaining a|C| sources, requiring 2PT( - 1) FLOPs. Thus the total number of FLOPs can be written as:
Total FLOPPs
- -
PM - -IC1-1) [(((-))+
-2PTIC|
pM
pr
-2PTIC|
p
+ a
((02+
p'
pd P
-2PT|C|
(p+p)
C - a
When compared to exact I.00 attribution with the target model, this method provides a speedup of:
C
P(C
(a² + P1 C
p'
Pa' aP-P
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Next, we consider the speedup the number of contexts grows and the number of sources kept after proxy model pruning is kept constant, i.e. a = k//C|| for some constant k.
P([C| D2P+P')CC aP pd
P([C) -
2
P+
|C|
1 P -P-P
P([C) - 1)
12 P
P'[C|
[ P p
PC
for large |C|
P
=P
Thus, the number of sources in the context grows, the speedup of proxy model pruning approaches P/P'.
B FURTHER DETAILS ON EXPERIMENTAL. SETUP
In this section, we provide more details on the experimental setup we use to evaluate context attri- bution methods.
B. DATASETS
Preprocessing
Before evaluating context attribution methods on SQuAD 2.0, HotpotQA, and QASPER, we first take the following preprocessing steps to make them suitable for running context attribution.
SQuAD2.0:
1. Filter dataset only include examples that are labeled as possible to answer given the context (SQuAD 2.0 contains both questions where the answer and is not present in the contextual information).
2. Truncate each example's supporting Wikipedia page context to 10 paragraphs and filter dataset to only examples where the answer lies in the first 10 paragraphs of the context. This is meant to filter out examples with very large number of context sources, as computing exact LOO attributions on these examples is impractical.
3. Randomly sample 1000 examples from the remaining set of examples.
Hotpot QA:
Sample the first 1000 examples the dataset.
OASPER:
1. Filter dataset to only examples with contexts containing fewer than 60 paragraphs. This limits the context length make exact LOO attribution tractable and also removes im- properly parsed contexts where every line in 1TTeXmath environment is listed as its own paragraph.
2. Randomly sample 1000 examples from the remaining set of examples. In our experiment, there are 2 empty examples and they are ignored, resulting in 998 examples.
report summary statistics for each dataset after preprocessing in Table
Prompting For each dataset, the following prompt templates to format query and context into a prompt suitable for instruction- tuned models.
SQuAD 2.0 and HotpotQA:
Answer the question on based the provided context.
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Source Type
Sources/ Example
Source Groups/ Example
Tokens/ Example
Generation GPU-
Dataset
Examples
seconds/Example
HotpotQA
1000
Sentence
41.2
9.9
1,249
18
SQuAD2.0
1000
Sentence
50.1
10.0
1,592
19.6
QASPER
998
Paragraph
38.2
12.7
4,116
51.7
Table 2: Summary statistics for each of the evaluation datasets.
Context:
(context}
Question:
(question)
QASPER:
Answer the question based the follewing scientific paper:
(context)
Question:
(question)
B.2 OUTLIER DETECTION WITH EXTREME STUDENTIZED DEVIATE TEST
The generalized Extreme Studentized Deviate (ESD) test (Rosner, 1983) is statistical test for de- tecting an unknown number outliers univariate set of data. This test an iterated form of Grubbs' test for detecting a single outlier in a set of data (Grubbs, 1969).
The ESD test parameterized by the maximum possible number of outliers, k, and the significance value a. It works by iteratively selecting the largest value in the dataset, computing that value's Grubbs' statistic G, testing if G is larger than the critical value Gerit, and removing the tested value from the dataset. This process stops when the selected value no longer has Grubbs' statistic greater than the critical value or values are tested.
The Grubbs' statistic and critical value are defined as is defined as follows for a collection of data Y1, Yi,..., Yx:
G max
Y, Y
8
(N X
Gerit VN X VN - 2 8
where Y and s the mean and standard deviation of Yi, YN, and is the upper critical value of the t-distribution with N degrees of freedom and a significance level of a 2n
For our experiments the significance level 0.05 and the maximum number of outliers
50.
B.3 FURTHER DETAILS ON CONTEXTCITE BASELINE
ContextCite works by training linear surrogate model to approximate the LOO attributions of a target model. Given context sources S = (s s|c]), ContextCite samples n ablation vec- tors oi, Vh E CC ~ Bernoulli(p) that act as masks indicating which context sources to re- move/keep for each forward pass. Based on these ablation vectors, ContextCite performs a single forward pass on each ablated context and records the change in logit-scaled response probability, Tr, caused by the removal of the ablated sources. Next, a Lasso regression surrogate model fit to map each ablation vector tù to the corresponding change in response probability т,, Finally, the attribution scores for context source j simply the weight that the trained Lasso model assigns to the j th source.
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C ADDITIONAL EXPERIMENTAL RESULTS
C.I EFFICIENCY VS. ACCURACY TRADEOFF FOR ATTRIBOT METHOSS
We provide plots illustrating the efficiency vs. accuracy tradeoff for each AttriBoT acceleration method in Figure 3, Figure 4, and Figure 5. Overall, we find that each method provides parameters that effectively vary the accuracy-efficinncy tradeoff and are Pareto optimal with respect to baseline context attribution methods.
SQUAD
Hotpot QA
QASPER
Buxt 1O0
*
Moas Avouge Prodioos
0.9-
RV
AtriiouT
0.5-
5It Pross KV
5it Pross KV
0.7-
IlI Prosy KV
0.6
Sen Secdartt
Bxedinss
Qualina Norrs
10
30
10P
10
10
10
10"
10
10
CostiooChe
Atteeike Weilis.
Eeumpio per GeU-aoomm
Exccpplxe por GPU--oood
Reumples per GPU-werod
Figure 3: Plot showing accuracy vs. efficiency tradeoff of proxy modeling. We find that smaller proxy models produce attributions that are less faithful to the target model's attributions.
SQuAD
Hotpot QA
QASPER
*
1.6
Koun Kuroag Phesiden
Derendicall BY dknediaal d2 Prop KV
Poo
0.8
Prey
0.-
Selacte
0.5
10"2
10P
10-4
10-1
36-1
10
Ordeed Neve
Altiziie Higho
Bumpoogee per CPUson
Eusspha per CPUsaaua
Busuko per GPUsoooo
Figure 4: Plot showing accuracy vs, efficiency tradeoff of hierarchical attribution with and without the use of proxy model. Both varying the size of the proxy model and tuning the number of source groups to retain, 3 effectively trades attribution faithfulness for speed.
SQUAD
Horpot
QASPER
Dust LOD
Moo Awerer Pockan
0.3
AttetBlT
Pracy Phinsa IXV
0.8-
oo
0.5
Seatice Nextacts
Gredeer Norrs
0.5
Attiatinn Wollte
10
10
10
10-7
10-2
10
10
Concertir
Esampion per GPU--ooo
Eestrpicn per GlUU-ooood
Ecarplic per GiUU-aooo
Figure 5: Plot showing the accuracy vs. efficiency tradeoff of proxy model pruning. We find that varying the size of the proxy model and the fraction of sources to retain, effectively trades attribu- tion faithfulness for speed.
C.2 ATTRIBOT's EFFECTIVENES: ACROSS DIFFERENT TARGET MODELS
To demonstrate AttriBTT's effectiveness for approximating LOO attributions for different target models, we replicate the experiments on HotpotQA using Qwen 72B as the target model. Shown in Figure 6, we observe nearly identical Pareto front as seen in the Llama model family. Aside from the attention weights baseline, AttriBoT is Pareto optimal at all points on the Pareto front. We hypothesize that the lack of Pareto optimality compared to the attention weights baseline comes from surprisingly good performance of the baseline method compared to its performance in other experiments, achieving mAP about 10 points higher than when used with Llama 70B.
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SQrAD
Hotpot QA
QASPER
Faoe 100
1.04
Noow Avenur Fooionn
0.6
D.5-
P
0.7-
3.--
Sirelaite
Draliet Now
Allettan Minglt
Eanmpies pes SPU-so
Excaglas CiPUsccuud
Koanggks GPU--ooo
O
ConvatCive
Figure 6: We replicate the experiments on SQuAD, HotpotQA and QASPER using Qwen 72B as the target model to demonstrate the AttriBoT's effectiveness across different target models.
C.3 AFFECT OF TRAINING DATA DISTRIBUTION OF PROXY MODEL
To further investigate the potential impact of proxy model selection and their training data distri- bution, we considered mismatched proxy models including Mistral 7B Instruct v0.3, as shown in Figure 7, Figure 8, Figure 9, and Figure 10. While our bag of tricks are still effective, using mismatched proxy model can degrade performance somewhat compared with proxy models trained with same data or distilled from the larger target models.
SQuAD
Hotpot QA
QASPER
* Eost 100
Stou Avenae Procika
1.0
*
Porsts Fruek
Attniezi
0.6
0.5-
0...0
Praze
67-
Pracy
0.5
Crodict New
Attintion Weigle
Eompie Gel--oood
Exnmplex per GPU-srrodd
Eccmppe GPU--ooom
Eonouttike
Figure 7: Using Llama 3.1 70B Instruct as the target model and Mistral 7B Instruct v0.3 as the proxy model.
SQuAD
Hotpos QA
QASPER
*
Foor 100
1.6
Sten Avoroe Pootiko
Porsto Fook
KV
Hil KV
0.5-
Praze - KV
0.7
Hit Ponyy KV
Procy + KV
0.6-
Bustow
0.5-
Euseaa Sexilttt
101
10P
10-1
10-11
10-9
10-1
10
Attentinn Beagitt
Cracket
Bsappis per UPU-oooo
Excrgpass per CiPUscond
Essupes (PUlsoood
Figure 8: Using Llama 3.1 70B Instruct as the target model and Qwen 2.5 7B Instruct as the proxy model.
C.4 MEAN AVERAGE PRECISION COMPARING WITH HUMAN ANNOTATIONS
The HotpotQA dataset comes with human-annotated "important" text spans in each example's con- text. While the primary purpose of AttriBoT is to approximate target model's LOO attributions, here we evaluate the agreement between AttriBo attributions and human- auman-annotated gold sources. We find that AttriBoT's context attributions closely align with human annotations, achieving only 10% drop in mAP compared to exact LOO attributions, while achieving a 300x speedup. Results shown for multiple target models in Figure 11.
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SQuAD
Hotpot QA
QASPER
Faoe 100
Noo Averurt Fooionn
1.04
Attetlle
D.5-
0.7-
3.--
Sirllaite
Doaliet Now
Allentin Minglt
Bsampss per SP0U-ooo
Excanlas CIPUsccoud
Euampes GPUlsoood
ConvatCrve
Figure 9: Using Qwen 2.5 72B Instruct as the target model and Mistral 7B Instruct v0.3 as the proxy model.
SQuAD
Hotpot QA
QASPER
Foor 100
Menn Aesage Poedion
b.9-
Piacto Rook
KV
Attiilo
0.5-
Ilft KV
Preas e RX
0.7-
Prayy a KV
Ponyy KV
0.6
0.5
Sestaa Sixalatte
1012
10
10
HP
1314
10"
11
Attentes Monglt
Greckel
Isompies per SPUddoo
Excerples per CiPUsecond
Elotpppe GPU--ooood
Figure 10: Using Qwen 2.5 72B Instruct the target model and Llama 3.1 8B Instruct as the proxy model.
Llama 70B
Qwen 2.5 72B
Eaaci LOO
AttriBoT
Mesn Averaye Precsion
Pannto Prore
0000
KV
HR KV
Prime RV
0.6-
Pruce
HRt Paony - RV
0.5
Baselings
Sentence Similacity
Guadient Norz
10-2
10
10P
10-2
10-1
10P
Attretion Weights
Exacmples GPU-acood
Examples per CPU-ecood
Contectiite
Figure 11: Mean average precision comparing with human annotation on HotpotQA dataset with
Llama 3 family and Qwen 2.5 family.
D PRACTITIONER'S 'S GUIDE TO USING ATTRIBOT
D.1 METHOD AND HYPERPARAMETER SELECTION
KV caching should always be employed because is effectively lossless and can be combined with other methods. Whenever feasible, float16 should be used avoid possible floating point errors. To further reduce LOO costs, we recommend starting with hierarchical attribution and pruning. For tasks like open-domain QA where only a small subset of the context relevant, retaining paragraphs or sentences is generally sufficient to maintain faithfulness while achieving significant acceleration. For further speedup. consider using smaller models within the same model family or distilled versions of the target model. Models in the same family with sizes as small as IB or 0.5B parameters can remain faithful even for complex tasks. Additionally, combining proxy methods with hierarchical attribution is advisable, as their performance degradation does not compound linearly.
D.2 INPUT PREPROCESSING
In settings such as RAG and in-context learning. input information may be duplicated due to the retrieval process. Such duplication poses challenges for LOO context attribution any attribution
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method that aims to approximate the LOO enror, removing instance of that information would likely not change the model's response likelihood. We strongly recommend deduplicating imput before performing LOO context attribution.
In scenarios where imput information is unstructured, we suggest chunking the input by parsing sen- tences and grouping few (e.g.. 10) sentences together to facilitate hierarchical context attribution.
24


Reviews:
[{"review_id": "0FcNxkI69f", "reviewer": "Reviewer_abNN", "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.", "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?\n* 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?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "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.\n* 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.\n* 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__\n* 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.\n\n__generalization__\n* 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.\n\n__evaluation and baselines__\n* 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.\n* 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.", "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": "JvZ14WZY6n", "reviewer": "Reviewer_Jn65", "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.", "questions": "Please refer to Weakness 3 in the section above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "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.\n\n2. The  proposed different approaches can be composed together to achieve better efficiency and performance. \n\n3. The framework shows strong performance across large language models by achieving over a 300× speedup in context attribution computations.\n\n4. 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?\n\n[1]. Enguehard, Joseph. \"Sequential Integrated Gradients: a simple but effective method for explaining language models.\" arXiv preprint arXiv:2305.15853 (2023).\n\n[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).\n\n2. 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.\n\n3. 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.\n\n4. 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.", "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": "OaBt3Kszcr", "reviewer": "Reviewer_zBXc", "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.", "questions": "1. Could the authors please label the AttriBoT combinations in Figure 2 for better clarity?\n\n2. It might be beneficial to include more model families in the empirical experiments, such as Gemma.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "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.\n\n2. 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.", "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": "xDkC41zCI0", "reviewer": "Reviewer_f2FJ", "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:\n\n- Key-value caching\n- Hierarchical attribution\n- Proxy modeling\n\nThis paper shows that their method achieves lots of speedup comparing to the scenarios where their method is not used while maintaining the performance.", "questions": "See the weakness section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The proposed methods are simple but effective.\n- This research direction is useful.\n- 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.\n- [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).\n- [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.", "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": "jYZ3GcFulI", "reviewer": "Reviewer_uZjG", "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.", "questions": "None", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Technical Innovation & Practicality\n- Introduces multiple complementary techniques that can be used independently or combined\n- Provides both theoretical and empirical justification for each component\n- Achieves practical speedups that make attribution feasible in real-world applications\n- Releases efficient implementation to benefit the research community\n\n2. Comprehensive Evaluation\n- Tests across multiple datasets (SQuAD, HotpotQA, QASPER)\n- Evaluates with different model families (Llama, Qwen)\n- Includes thorough ablation studies of different components\n- Provides detailed theoretical efficiency analysis with derivations\n\n3. Strong Empirical Results\n- Demonstrates clear Pareto-optimal trade-offs between speed and accuracy\n- Shows consistent performance across different context lengths and model sizes\n- Achieves significant speedups (300x) while maintaining high attribution fidelity\n- Outperforms existing attribution methods like ContextCite\n\n4. Clear and Rigorous Methodology\n- Well-defined metrics for measuring attribution quality\n- Thorough baselines for comparison\n- Careful experimental design with appropriate controls\n- Detailed implementation specifications", "weaknesses": "1. Limited Scope of Applications\n- Primary evaluation focuses on open-book QA tasks\n- Could explore effectiveness in other applications like detecting malicious prompts or hallucinations\n- Could demonstrate utility for real-time attribution scenarios\n\n2. Parameter Sensitivity Analysis\n- Could provide more guidance on selecting optimal parameters (e.g., pruning thresholds)\n- Limited discussion of how parameter choices might vary across different use cases\n- Could explore automatic parameter tuning approaches", "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": "ggKp2BcQZp", "reviewer": "Reviewer_5ZgU", "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\n1. KV caching\n2. hierarchical evaluation\n3. approximation via smaller (proxy) models\n\nThe 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).", "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?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "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.\n- Solid discussion of computational cost, and gains by introducing the individual optimizations\n- 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.\n2. It's an important, but fairly narrow area\n\nSome minor concerns:\n1. 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.\n2. How well does the hierarchical approach work in real world scenarios, where information can appear in multiple places (also see in questions section)", "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": "xsqVVHyDMe", "reviewer": "Reviewer_Y8fS", "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.", "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?\n\n2. 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?\n\n3. 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?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "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.", "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)