On the Generalizability of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals"

• URL: http://arxiv.org/abs/2506.22977v1
• Date: Sat, 28 Jun 2025 18:29:19 GMT
• Title: On the Generalizability of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals"
• Authors: Asen Dotsinski, Udit Thakur, Marko Ivanov, Mohammad Hafeez Khan, Maria Heuss,
• Abstract summary: We reproduce a study of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals"<n>It investigates mechanisms in language models between factual recall and counterfactual in-context repetition.<n>We find that the attention head ablation proposed in Ortu et al. (2024) is ineffective for domains that are underrepresented in their dataset.
• Score: 0.8621608193534839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a reproduction study of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals" (Ortu et al., 2024), which investigates competition of mechanisms in language models between factual recall and counterfactual in-context repetition. Our study successfully reproduces their primary findings regarding the localization of factual and counterfactual information, the dominance of attention blocks in mechanism competition, and the specialization of attention heads in handling competing information. We reproduce their results on both GPT-2 (Radford et al., 2019) and Pythia 6.9B (Biderman et al., 2023). We extend their work in three significant directions. First, we explore the generalizability of these findings to even larger models by replicating the experiments on Llama 3.1 8B (Grattafiori et al., 2024), discovering greatly reduced attention head specialization. Second, we investigate the impact of prompt structure by introducing variations where we avoid repeating the counterfactual statement verbatim or we change the premise word, observing a marked decrease in the logit for the counterfactual token. Finally, we test the validity of the authors' claims for prompts of specific domains, discovering that certain categories of prompts skew the results by providing the factual prediction token as part of the subject of the sentence. Overall, we find that the attention head ablation proposed in Ortu et al. (2024) is ineffective for domains that are underrepresented in their dataset, and that the effectiveness varies based on model architecture, prompt structure, domain and task.

Related papers

• Causality is Key for Interpretability Claims to Generalise [35.833847356014154]
  Interpretability research on large language models (LLMs) has yielded important insights into model behaviour.<n> recurring pitfalls persist: findings that do not generalise, and causal interpretations that outrun the evidence.<n>Pearl's causal hierarchy clarifies what an interpretability study can justify.
  arXiv  Detail & Related papers  (2026-02-18T18:45:04Z)
• Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures [72.27391760972445]
  Large Reasoning Models (LRMs) have pushed reasoning capabilities to new heights.<n>This paper organizes recent findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors.
  arXiv  Detail & Related papers  (2026-01-11T08:48:46Z)
• Tracing Facts or just Copies? A critical investigation of the Competitions of Mechanisms in Large Language Models [1.0058542892457312]
  We show that attention heads promoting factual output do so via general copy suppression rather than selective counterfactual suppression.<n>We show that attention head behavior is domain-dependent, with larger models exhibiting more specialized and category-sensitive patterns.
  arXiv  Detail & Related papers  (2025-07-16T00:08:48Z)
• What Makes a Good Natural Language Prompt? [72.3282960118995]
  We conduct a meta-analysis surveying more than 150 prompting-related papers from leading NLP and AI conferences from 2022 to 2025.<n>We propose a property- and human-centric framework for evaluating prompt quality, encompassing 21 properties categorized into six dimensions.<n>We then empirically explore multi-property prompt enhancements in reasoning tasks, observing that single-property enhancements often have the greatest impact.
  arXiv  Detail & Related papers  (2025-06-07T23:19:27Z)
• Structured Thinking Matters: Improving LLMs Generalization in Causal Inference Tasks [0.7988085110283119]
  Recent results from the Corr2Cause dataset benchmark reveal that state-of-the-art LLMs only marginally outperform random baselines.<n>We provide the model with the capability to structure its thinking by guiding the model to build a structured knowledge graph.<n> Experiments on the test subset of the Corr2Cause dataset benchmark with Qwen3-32B model (reasoning model) show substantial gains over standard direct prompting methods.
  arXiv  Detail & Related papers  (2025-05-23T15:37:40Z)
• Causal Inference Isn't Special: Why It's Just Another Prediction Problem [1.90365714903665]
  Causal inference is often portrayed as distinct from predictive modeling.<n>But at its core, causal inference is simply a structured instance of prediction under distribution shift.<n>This perspective reframes causal estimation as a familiar generalization problem.
  arXiv  Detail & Related papers  (2025-04-06T01:37:50Z)
• Fine-Tuning Topics through Weighting Aspect Keywords [0.8665758002017515]
  Conventional topic modeling techniques are typically static and unsupervised, making them ill-suited for fast-evolving fields like quantum cryptography.<n>We employ design science research methodology to create a framework that enhances topic modeling by weighting aspects based on expert-informed input.<n>This study shows that expert-guided, aspect-weighted topic modeling boosts interpretability and adaptability.
  arXiv  Detail & Related papers  (2025-02-12T15:31:16Z)
• Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability [1.8274323268621635]
  Real Explainer (RealExp) is an interpretability method that decouples the Shapley Value into individual feature importance and feature correlation importance.<n>RealExp enhances interpretability by precisely quantifying both individual feature contributions and their interactions.
  arXiv  Detail & Related papers  (2024-12-02T10:50:50Z)
• CiteFusion: An Ensemble Framework for Citation Intent Classification Harnessing Dual-Model Binary Couples and SHAP Analyses [1.7812428873698407]
  CiteFusion addresses the multi-class Citation Intent Classification task on two benchmark datasets: SciCite and ACL-ARC.<n>The framework employs a one-vs-all decomposition of the multi-class task into class-specific binary sub-tasks.<n>Results show that CiteFusion achieves state-of-the-art performance, with Macro-F1 scores of 89.60% on SciCite, and 76.24% on ACL-ARC.
  arXiv  Detail & Related papers  (2024-07-18T09:29:33Z)
• The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning [78.13481522957552]
  Machine learning models are sensitive to spurious correlations between non-essential features of the inputs and the corresponding labels.<n>This paper provides a comprehensive survey of this emerging issue, along with a fine-grained taxonomy of existing state-of-the-art methods for addressing spurious correlations in machine learning models.
  arXiv  Detail & Related papers  (2024-02-20T04:49:34Z)
• Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals [82.68757839524677]
  Interpretability research aims to bridge the gap between empirical success and our scientific understanding of large language models (LLMs) We propose a formulation of competition of mechanisms, which focuses on the interplay of multiple mechanisms instead of individual mechanisms. Our findings show traces of the mechanisms and their competition across various model components and reveal attention positions that effectively control the strength of certain mechanisms.
  arXiv  Detail & Related papers  (2024-02-18T17:26:51Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review [28.06558596439521]
  In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their submissions based on perceived quality.<n>We employ these author-specified rankings to enhance peer review in machine learning and artificial intelligence conferences.<n>We generate adjusted scores that closely align with the original scores while adhering to author-specified rankings.
  arXiv  Detail & Related papers  (2023-04-21T17:59:08Z)
• Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning [98.78136504619539]
  Causal Triplet is a causal representation learning benchmark featuring visually more complex scenes. We show that models built with the knowledge of disentangled or object-centric representations significantly outperform their distributed counterparts.
  arXiv  Detail & Related papers  (2023-01-12T17:43:38Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)

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.