Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Influence Patterns for Explaining Information Flow in BERT
Authors: Kaiji Lu, Zifan Wang, Piotr Mardziel, Anupam Datta
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an extensive empirical study of influence patterns for several NLP tasks: Subject-Verb Agreement (SVA), Reflexive Anaphora (RA), and Sentiment Analysis (SA). Our findings are summarized below. |
| Researcher Affiliation | Academia | Kaiji Lu , Zifan Wang, Piotr Mardziel, Anupam Datta Electrical and Computer Engineering Carnegie Mellon University Mountain View, CA 94089 |
| Pseudocode | Yes | The detailed algorithm of GPR and analysis of its optimality can be found in Appendix B.1 and B.2. |
| Open Source Code | No | we will explore these limitations in future work and release our code and hope the proposed methods will serve as an insightful tool in future exploration. |
| Open Datasets | Yes | We consider two groups of NLP tasks: (1) subject-word agreement (SVA) and reflexive anaphora (RA)... (2) sentiment analysis(SA): we use 220 short examples (sentence length 17) from the evaluation set of the 2-class GLUE SST-2 sentiment analysis dataset [47]. |
| Dataset Splits | Yes | For SST-2 we fine-tuned on the pretrained BERTBASE[7] with L = 12, A = 12. We sample 1000 sentences from each subtask evenly distributed across different sentence types (e.g. singular/plural subject & singular/plural intervening noun) with a fixed sentence structure |
| Hardware Specification | Yes | All computations are done with a Titan V on a machine with 64 GB of RAM. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this work. |
| Software Dependencies | No | The paper mentions using BERT models and references TensorFlow, but does not provide specific version numbers for any software or libraries used in their experiments. |
| Experiment Setup | Yes | Let the target node for SVA and RA tasks be the output of the Qo I score q(y) def = ycorrect ywrong. For instance, y IS y ARE for the sentence she [MASK] happy. Similarly, we use ypositive ynegative for sentiment analysis. We choose an uniform distribution over a linear path from xb to x as the distribution D in Def. 3 where the xb is chosen as the the input embedding of [MASK] because it can viewed a word with no information. For a given input token xi, we apply GPR differently depending on the sign of distributional influence g(x; q, D): if g(x; q, D) 0, we maximize the pattern influence towards q(y) at each iteration of the GPR otherwise we maximize pattern influence towards q(y). |