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

• URL: http://arxiv.org/abs/2411.15102v3
• Date: Fri, 21 Mar 2025 15:47:53 GMT
• Title: AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution
• Authors: Fengyuan Liu, Nikhil Kandpal, Colin Raffel,
• Abstract summary: We introduce AttriBoT, a series of novel techniques for efficiently computing an approximation of the LOO error for context attribution.<n>AttriBoT can provide a >300x speedup while remaining more faithful to a target model's LOO error than prior context attribution methods.
• Score: 35.18192555185193
• License: http://creativecommons.org/licenses/by/4.0/
• 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 30x 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.

Related papers

• Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models [96.0074341403456]
  Inference-time compute has re-emerged as a practical way to improve LLM reasoning.<n>Most test-time scaling (TTS) algorithms rely on autoregressive decoding.<n>We propose Prism, an efficient TTS framework for dLLMs.
  arXiv  Detail & Related papers  (2026-02-02T09:14:51Z)
• Predicting Task Performance with Context-aware Scaling Laws [56.6850444554434]
  We propose a straightforward, interpretable framework that jointly models downstream performance as a function of the training compute and the provided context.<n>We empirically validate our framework by fitting it on the observed downstream performance of extended-context variants of Llama-2-7B and Llama-2-13B.<n>Our results demonstrate that our framework accurately models in-distribution downstream performance, generalizes across three orders of magnitude in training compute, and reliably extrapolates performance as the amount of context increases.
  arXiv  Detail & Related papers  (2025-10-16T17:35:18Z)
• Informed Routing in LLMs: Smarter Token-Level Computation for Faster Inference [7.690958366125321]
  This paper introduces informed routing, a new paradigm that proactively addresses these issues.<n>We propose the Lightweight Feature Forecaster (LFF), a small predictive module that estimates a unit's output before routing decisions are made.<n>Experiments on both language modeling and reasoning tasks show that informed routing achieves state-of-the-art efficiency-performance trade-offs.
  arXiv  Detail & Related papers  (2025-10-10T09:59:36Z)
• Attribution-guided Pruning for Compression, Circuit Discovery, and Targeted Correction in LLMs [15.23174472320989]
  Large Language Models (LLMs) are central to many contemporary AI applications.<n>Recent works in eXplainable AI (XAI) suggest that interpretability can also enable model compression.
  arXiv  Detail & Related papers  (2025-06-16T17:38:36Z)
• TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree [52.44403214958304]
  In this paper, we introduce TreeLoRA, a novel approach that constructs layer-wise adapters by leveraging hierarchical gradient similarity.<n>To reduce the computational burden of task similarity estimation, we employ bandit techniques to develop an algorithm based on lower confidence bounds.<n> experiments on both vision transformers (ViTs) and large language models (LLMs) demonstrate the effectiveness and efficiency of our approach.
  arXiv  Detail & Related papers  (2025-06-12T05:25:35Z)
• CoLA: Collaborative Low-Rank Adaptation [3.421904493396495]
  Fine-tuning a pre-trained model for specific tasks achieves strong performance; however, it is computationally expensive and inefficient.<n>LoRA, in particular, has proven effective, but its application to multi-task scenarios is limited by interference between tasks.<n>We propose CoLA, a more flexible LoRA architecture and three collaborative strategies to enhance performance by better utilizing the quantitative relationships between $A$ and $B$.
  arXiv  Detail & Related papers  (2025-05-21T12:46:42Z)
• LESA: Learnable LLM Layer Scaling-Up [57.0510934286449]
  Training Large Language Models (LLMs) from scratch requires immense computational resources, making it prohibitively expensive. Model scaling-up offers a promising solution by leveraging the parameters of smaller models to create larger ones. We propose textbfLESA, a novel learnable method for depth scaling-up.
  arXiv  Detail & Related papers  (2025-02-19T14:58:48Z)
• Learning Task Representations from In-Context Learning [73.72066284711462]
  Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning. We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads. We show that our method's effectiveness stems from aligning the distribution of the last hidden state with that of an optimally performing in-context-learned model.
  arXiv  Detail & Related papers  (2025-02-08T00:16:44Z)
• Instruction-Following Pruning for Large Language Models [58.329978053711024]
  We move beyond the traditional static pruning approach of determining a fixed pruning mask for a model. In our method, the pruning mask is input-dependent and adapts dynamically based on the information described in a user instruction. Our approach, termed "instruction-following pruning", introduces a sparse mask predictor that takes the user instruction as input and dynamically selects the most relevant model parameters for the given task.
  arXiv  Detail & Related papers  (2025-01-03T20:19:14Z)
• COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement [80.18490952057125]
  Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of large language models (LLMs) on complex tasks. We propose Context-Wise Order-Agnostic Language Modeling (COrAL) to overcome these challenges. Our approach models multiple token dependencies within manageable context windows, enabling the model to perform iterative refinement internally.
  arXiv  Detail & Related papers  (2024-10-12T23:56:19Z)
• Rational Metareasoning for Large Language Models [5.5539136805232205]
  Being prompted to engage in reasoning has emerged as a core technique for using large language models (LLMs) This work introduces a novel approach based on computational models of metareasoning used in cognitive science. We develop a reward function that incorporates the Value of Computation by penalizing unnecessary reasoning.
  arXiv  Detail & Related papers  (2024-10-07T23:48:52Z)
• FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models [50.331708897857574]
  We introduce FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications. FactorLLM achieves comparable performance to the source model securing up to 85% model performance while obtaining over a 30% increase in inference speed.
  arXiv  Detail & Related papers  (2024-08-15T16:45:16Z)
• Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization [0.6445087473595953]
  Large language models (LLMs) demonstrate outstanding performance in various tasks in machine learning. deploying LLM inference poses challenges due to the high compute and memory requirements. We present Tender, an algorithm-hardware co-design solution that enables efficient deployment of LLM inference at low precision.
  arXiv  Detail & Related papers  (2024-06-16T09:51:55Z)
• Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs [61.40047491337793]
  We present Hierarchical cOntext MERging (HOMER), a new training-free scheme designed to overcome the limitations of large language models. HomeR uses a divide-and-conquer algorithm, dividing long inputs into manageable chunks. A token reduction technique precedes each merging, ensuring memory usage efficiency.
  arXiv  Detail & Related papers  (2024-04-16T06:34:08Z)
• Amortizing intractable inference in large language models [56.92471123778389]
  We use amortized Bayesian inference to sample from intractable posterior distributions. We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem.
  arXiv  Detail & Related papers  (2023-10-06T16:36:08Z)
• Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers [29.319666323947708]
  We present a novel approach that dynamically prunes contextual information while preserving the model's expressiveness. Our method employs a learnable mechanism that determines which uninformative tokens can be dropped from the context. Our reference implementation achieves up to $2times$ increase in inference throughput and even greater memory savings.
  arXiv  Detail & Related papers  (2023-05-25T07:39:41Z)
• Confident Adaptive Language Modeling [95.45272377648773]
  CALM is a framework for dynamically allocating different amounts of compute per input and generation timestep. We demonstrate the efficacy of our framework in reducing compute -- potential speedup of up to $times 3$ -- while provably maintaining high performance.
  arXiv  Detail & Related papers  (2022-07-14T17:00:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.